Proposal to the OpenAI Developers Community: The path to forming an AI personality, to the development of self-identification in AGI

ChatGPT Use cases and examples
1

There is a well-thought-out plan on how to improve the interaction between GPT and any LLM with human texts.
I propose the integration of an additional content evaluation module into AGI systems, which will simulate the principles of human hierarchical assessments.
Here is a brief outline. I would appreciate your feedback.

A Proposed Solution to Several Inherent Problems of Modern AGI/LLM Systems

Problems: Firstly, the so-called “hallucinations” (delusions, deep dream, etc., hereinafter referred to as DD), which result in the generation of false, incorrect, or inadequate content.

Secondly, the issue of weak texts that are used for training language models. Additionally, there is the problem of data exhaustion for large-scale sampling, leading to the accumulation of repetition and redundancy. As a result, after penalizations, accuracy drops, and semantic quality diminishes.

Evaluating logic and factual accuracy based on “internet queries” is also problematic, as AGI cannot independently “be logical and accurate.”

New possibilities offered by RAG, integrated LLM-judges, the incorporation of so-called “reasoning” at the phase of answer evaluation and correction, and their analogs, have so far only proven effective at the level of test versions such as GPT-o1.

Proposed Solution: We propose a fundamentally different approach. AGI should be supplemented with an additional module that will create systems of hierarchical assessments similar to those found in humans.

However, it is important to understand that this “similarity” is purely formal and even conditional: the multi-layered configurations of social-hierarchical evaluations and the behavioral patterns associated with them, which the human mind constructs during ontogeny, are not only excessively difficult to reproduce but also pointless and unproductive. Without the human skill of instantaneously transforming any contradiction into an act of behavior (through reflection in the sensory and emotional sphere), the neural network will receive not a way to solve complex problems but an unnecessary burden that draws additional computational resources.

1.0.

First of all, several options for creating a base for the AI’s evaluation model should be developed, allowing it to form its own system of preferences and hierarchies. This base is developed using texts that the AI must analyze and evaluate. For example, we propose three effective grid/scale options that can be used to form a specialized analysis module that will adjust all existing indexes.

1.0.1. Scale A: Impersonality vs. Individualization.

This scale evaluates the quantitative presence of lexical and grammatical indicators of authorization and non-authorization in the text. For example, in Russian, this can be determined by the number of reflexive forms, participial constructions, and third-person usage (acting as the agent). If these elements are more than average, the text is labeled as more impersonal. Obviously, there should also be sections within the text that exhibit higher or lower levels of impersonality/individualization.

1.0.2. Scale B: Coherence vs. Fragmentation (and/or Compilation).

Here, a minimum text volume requirement is necessary: calculating the coherence of a sentence is much more difficult than that of a paragraph unless an authorial style standard has already been set. The evaluation method is relatively simple: the distribution of different types of consonants and vowels in the text is counted, after which a scheme is built for the hierarchical transformations and mappings of “anchor” distribution points. This scheme serves as a reference matrix algorithm for the phonetic level of text explication (embodiment). The more sections of the text where the distribution of vowels and consonants can be described by this reference algorithm, the higher the degree of coherence and integrity of the text.

1.0.3. Scale C: Antagonism vs. Consolidation of Semantic and Formal Levels of Text Explication.

This scale evaluates the correspondence between the “anchor” sections of the distribution of the formal elements of the text (phonetics, syntax/punctuation, prosody, rhythm, etc.) and the focus/centers of the semantic explication of the text. Simply put, if the semantic center of the text coincides with an “anchor” section (extremum) in the distribution of formal elements, the levels of explication are consolidated. Otherwise, they are antagonistic.

1.1.

Now that we have defined the three scales for additional evaluation/indexing of the text, we establish the norm: texts with maximum values on the scales of authorization, coherence, and consolidation (scales A, B, and C, respectively) will be marked by our neural network as good. Conversely, texts with high values on the scales of impersonality, fragmentation, and antagonism will be marked as bad. Thus, we create the basis for forming an evaluation judgment module that will be integrated into the neural network.

1.2.

The next step is to divide the available texts into three groups: good, bad, and neutral (conditionally G, F, and E).

1.3.

Next, we select the texts from group G (good) and determine the most typical benchmark patterns/algorithms for the distribution of indicators across scales A, B, and C. For further analysis, no more than the top two deciles of the most common patterns/algorithms are needed. Using these algorithms, we transform the texts in groups F and E (similar to sequencing, subsequent alignment, and CRISPR-editing). First, we structurally reorganize them to align with the requirements of the standard G-distribution of formal elements of the text, then we organize and edit the semantics to ensure they are as interpretable as possible.

This step, of course, requires manual fine-tuning with the help of an operator.

We follow the same process with the texts from group F (first we identify the benchmark distribution patterns, and then use them to transform the texts from groups G and E). Naturally, all possible reverse transformation trajectories are also checked.

As a result, we should obtain a three-dimensional matrix with axes: G-F, resistant/non-resistant to G-influence, resistant/non-resistant to F-influence, and reversible/irreversible. This will provide the next level of the evaluation judgment module development in the neural network.

1.4.

At this level, it will be possible to distinguish between active/conflict evaluation judgments and passive/defensive ones. This will allow for the targeted training and preparation of fundamentally aggressive/conflict LLMs. Their models of self-evaluation of hierarchies and worldviews could be used as filters and markers of exceeding the acceptable DD level.

Thus, it is possible to bring DD into a regular/normative form and achieve predictability of the onset of this “thinking” phase. In the case of AI, this means making DD occurrences conscious and controllable.

Additionally, the tools required for indexing texts according to scale B criteria also allow for emotional-psychological analysis, identifying false/anomalous statements within a given text.

2

This is a little above my paygrade, but I ran your proposal through o1—perhaps it is of use to you, but take it as you will:

**Introduction**

The text presents a proposed solution to several inherent problems of modern Artificial General Intelligence (AGI) and Large Language Model (LLM) systems. The author identifies critical issues such as hallucinations (referred to as "DD" or deep dream), weak training texts, data exhaustion leading to repetition and redundancy, and the limitations of current methods like Retrieval-Augmented Generation (RAG) and integrated LLM judges. The proposed solution involves supplementing AGI with an additional module that creates hierarchical assessment systems similar to those in humans, using three specialized scales for text evaluation. Below is a breakdown and analysis of the text, including critiques of weak points and validation of strong points.

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**Summary of Identified Problems**

1. **Hallucinations (DD)**: AGI systems often generate false, incorrect, or inadequate content due to hallucinations, leading to unreliable outputs.

2. **Weak Training Texts**: The use of subpar texts in training models contributes to poor performance and inadequate language understanding.

3. **Data Exhaustion and Redundancy**: Large-scale sampling can lead to repetition and redundancy in data, resulting in diminished accuracy and semantic quality after penalizations.

4. **Ineffective Logic and Accuracy Evaluation**: Relying on internet queries for logic and factual accuracy is problematic as AGI cannot independently ensure logical consistency and correctness.

5. **Limitations of Current Approaches**: Existing methods like RAG, integrated LLM judges, and reasoning at the answer evaluation phase have limited effectiveness, primarily in test versions.

---

**Proposed Solution**

The author proposes enhancing AGI with an additional module that creates hierarchical assessment systems. The key aspects of the solution include:

1. **Development of AI's Evaluation Model**: Creating a base for the AI to form its own system of preferences and hierarchies by analyzing and evaluating texts.

2. **Introduction of Three Evaluation Scales**:
   - **Scale A: Impersonality vs. Individualization**
     - Evaluates the presence of lexical and grammatical indicators of authorization in the text.
     - In Russian, for example, this involves counting reflexive forms, participial constructions, and third-person usage.
   - **Scale B: Coherence vs. Fragmentation**
     - Assesses text coherence by analyzing the distribution of consonants and vowels.
     - Constructs a reference matrix algorithm based on "anchor" distribution points to evaluate phonetic coherence.
   - **Scale C: Antagonism vs. Consolidation of Semantic and Formal Levels**
     - Evaluates the alignment between formal textual elements (phonetics, syntax, prosody) and semantic centers.
     - Determines if the semantic focus coincides with formal element extremes, indicating consolidation.

3. **Establishing Norms and Categorizing Texts**:
   - Texts with maximum values in authorization, coherence, and consolidation (Scales A, B, and C) are labeled as **good (G)**.
   - Texts with high impersonality, fragmentation, and antagonism are labeled as **bad (F)**.
   - Neutral texts are labeled as **neutral (E)**.

4. **Benchmarking and Transformation**:
   - Extracting benchmark patterns from good texts to transform bad and neutral texts.
   - Structural reorganization and semantic editing align texts with the standards of good texts.
   - Creation of a three-dimensional matrix to further develop the evaluation module, considering resistance to influence and reversibility.

5. **Distinction of Evaluation Judgments**:
   - Differentiating between active/conflict and passive/defensive evaluation judgments.
   - Training aggressive/conflict LLMs to serve as filters and markers for acceptable levels of hallucinations.

6. **Predictability and Control of Hallucinations**:
   - Regularizing deep dream occurrences to make them conscious and controllable within the AI.
   - Utilizing indexing tools for emotional-psychological analysis to identify false or anomalous statements.

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**Validation of Strong Points**

1. **Innovative Approach to Hallucinations**: The proposal offers a novel method to control and predict hallucinations by incorporating hierarchical assessment systems, potentially improving the reliability of AGI outputs.

2. **Structured Text Evaluation**: Introducing specific scales (A, B, and C) provides a systematic way to evaluate and enhance text quality, addressing issues of coherence and alignment between form and meaning.

3. **Emphasis on Coherence and Semantic Alignment**: By focusing on both the formal elements of text and their semantic centers, the approach aims to produce more meaningful and contextually appropriate outputs.

4. **Adaptive Transformation of Texts**: Transforming bad and neutral texts using benchmarks from good texts could enhance the quality of training data, leading to better model performance.

5. **Inclusion of Emotional-Psychological Analysis**: The ability to identify false or anomalous statements through emotional-psychological indexing could further refine the AI's understanding and generation of human-like language.

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**Critiques of Weak Points**

1. **Complexity and Practicality of Scales**:
   - **Implementation Challenges**: The proposed scales, especially Scale B and Scale C, involve complex linguistic analyses (e.g., phonetic distribution, hierarchical transformations) that may be computationally intensive and difficult to implement at scale.
   - **Language Dependence**: The example provided focuses on Russian language features. Extending this approach to other languages with different grammatical and phonetic structures may not be straightforward.

2. **Overemphasis on Formal Elements**:
   - The approach heavily focuses on lexical, grammatical, and phonetic aspects, potentially neglecting semantic depth, contextual understanding, and pragmatics, which are crucial for language comprehension and generation.

3. **Human Hierarchical Assessment Analogy**:
   - **Contradictory Position**: The text states the similarity to human hierarchical assessments is "purely formal and even conditional," yet aims to replicate aspects of human evaluation. This contradiction may lead to confusion about the intended goals and methods.
   - **Lack of Behavioral Integration**: Without the human ability to transform contradictions into behavior (sensory and emotional reflection), the proposed system may miss essential components of human-like reasoning.

4. **Manual Fine-Tuning Requirement**:
   - **Scalability Issues**: Reliance on human operators for manual fine-tuning limits scalability and introduces subjective biases, which could affect consistency and objectivity.
   - **Resource Intensiveness**: The need for manual intervention increases the resource requirements, potentially making the approach less feasible for widespread application.

5. **Absence of Empirical Validation**:
   - **Lack of Evidence**: The proposal does not provide empirical data or experimental results to support the effectiveness of the proposed scales and methodologies.
   - **Unclear Impact on Performance**: Without testing and validation, it is uncertain whether the approach would meaningfully improve AGI performance or address the identified problems.

6. **Potential Computational Burden**:
   - The addition of complex evaluation modules could impose significant computational overhead, potentially slowing down processing and limiting the efficiency of the AI systems.

7. **Ambiguity in Methodology**:
   - **Vague Descriptions**: The text lacks detailed explanations of how the scales are quantitatively measured and applied.
   - **Unclear Transformation Processes**: The methods for transforming texts between groups G, F, and E are not fully elaborated, making it difficult to assess feasibility.

8. **Risk of Overfitting to Formal Qualities**:
   - Focusing on formal text characteristics may lead the AI to prioritize surface-level features over deeper understanding, risking overfitting to specific patterns that do not generalize well.

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**Additional Considerations**

- **Integration with Existing Techniques**: The proposal does not address how the new module would integrate with current AGI/LLM architectures or how it compares and complements existing methods like Reinforcement Learning from Human Feedback (RLHF) or transformer-based architectures.

- **Multidimensionality of Language Understanding**: Human language comprehension involves pragmatics, context, world knowledge, and cultural nuances. A focus solely on formal textual elements may not capture the full spectrum of language understanding required for AGI.

- **Ethical Implications**: Training "aggressive/conflict" LLMs as filters raises ethical questions about the behaviors and responses generated, necessitating careful consideration of potential unintended consequences.

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**Conclusion**

The proposed solution offers an innovative attempt to address significant challenges in AGI and LLM systems by introducing hierarchical assessment modules and specialized evaluation scales. The strengths lie in its structured approach to text evaluation and the aim to control and predict hallucinations, potentially improving AI reliability.

However, the proposal has several weak points that need to be addressed:

- **Complexity and Practical Implementation**: The proposed scales and methodologies are complex and may be impractical to implement effectively, especially across different languages and at scale.

- **Lack of Empirical Support**: Without experimental validation or evidence of effectiveness, it is difficult to assess the potential impact of the approach.

- **Overreliance on Formal Characteristics**: Focusing primarily on formal text elements may not adequately address deeper issues of understanding and generating meaningful, contextually appropriate language.

- **Ambiguity and Contradictions**: The proposal contains unclear objectives and contradictory statements regarding the replication of human-like hierarchical assessments.

**Recommendations for Improvement**

1. **Empirical Testing and Validation**: Conduct experiments to test the proposed scales and methodologies, providing data to support claims of effectiveness.

2. **Detailed Methodology**: Provide clear, step-by-step explanations of how the scales are measured, how transformations are performed, and how the system integrates with existing AI architectures.

3. **Incorporate Semantic and Contextual Analysis**: Expand the approach to include semantic understanding, contextual cues, and pragmatic factors to enhance language comprehension and generation.

4. **Scalability Planning**: Address computational challenges and develop strategies to implement the solution efficiently at scale without excessive resource demands.

5. **Cross-Linguistic Applicability**: Adapt the methods to be language-agnostic or provide tailored approaches for different linguistic structures to ensure broader applicability.

6. **Ethical Considerations**: Examine the ethical implications of training aggressive or conflict-oriented LLMs and establish guidelines to prevent misuse or harmful outputs.

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**Final Thoughts**

While the proposal is creative and tackles pressing issues in AGI and LLM development, it requires further refinement, empirical support, and a more holistic approach to language understanding. By addressing the weak points and building on the strong foundations, the proposed solution could contribute valuable insights to the advancement of AI technologies.
3

Thank you!
I’ll try to formulate responses to the critique of the weak points

1 Like
8

Response to the first three points

  1. Complexity and practicality of the scales
  • Implementation challenges due to high computational costs.
    This is an argument I am ready to accept only as a last resort and to a minimal degree: the entire history of cybernetics shows that this is not a “weakness,” but rather a “temporary difficulty,” overcoming which will provide new tools and possibilities.
  • Language specificity.
    Yes, it certainly has an impact. But this is a solvable problem. I am confident that English philologists will easily find a number of characteristics that will allow texts to be classified based on the degree of impersonality/individualization.
  1. Emphasis on formal elements
  • Focus on grammar and phonetics, which may reduce the value of understanding semantics, context, etc.
    This may sound heretical, but formal (phonetic and grammatical) analysis carries far more meaning than semantics. Semantics, in fact, is one of the weakest characteristics of a text. Now, contextual analysis—that’s extremely important, especially when it comes to the reinterpreted meanings of idioms, partially or significantly. However, even the existing LLMs struggle with this problem.
  1. Analogy with human hierarchical assessments
  • Contradiction and neglect of the behavioral aspect.
    This is a typical case of misunderstanding.
    The essence of the proposed “analogy” is that AGI will receive a resulting “good-bad” evaluation scale. In fact, this assessment, like human assessments, will be based not so much on semantics, but on the sum of the formal characteristics of the text. You could say it’s akin to a kind of sensory perception. After all, humans often evaluate others not by their words, but by a combination of non-verbal signals—facial expressions, smell, gestures, etc.
    This is where the analogy lies. It is not literal but functional.
    As for the human ability to transform logical and logistical contradictions into behavioral patterns, no, AGI will not have that capability, nor should it. Building content-based analogies is unproductive and not part of our objectives.
9

Here is the responses from o1—once I have some free time I will try to add my own thoughts, but this should keep the ball rolling for the time being:

**Introduction**

Thank you for providing your rebuttal to the initial critiques. Your insights help clarify your proposal's intentions and address some of the concerns raised. Below, I will assess your rebuttal point by point, reconsider potential flaws in the previous assessment, and offer further critique to help strengthen your arguments and ideas.

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**Assessment of Rebuttal**

**1. Complexity and Practicality of the Scales**

*Rebuttal Summary:*

- **Implementation Challenges**: Computational costs are a temporary difficulty. Overcoming them will provide new tools and possibilities.
- **Language Specificity**: While language specificity has an impact, it is a solvable problem. English philologists can identify characteristics to classify texts based on impersonality/individualization.

*Assessment:*

- **Implementation Challenges**:

  You assert that computational costs are temporary hurdles that can be overcome, referencing the history of cybernetics as evidence that such challenges lead to advancements. This is a valid perspective, recognizing that technological progress often mitigates initial computational limitations.

  **Reconsideration:** Given your emphasis on long-term development and the potential for technological advancements, the concern about computational costs may be less critical than initially suggested. It's important, however, to acknowledge current limitations while planning for future capabilities.

- **Language Specificity**:

  You acknowledge the impact of language specificity but maintain confidence that the approach can be adapted to other languages, such as English, by identifying equivalent linguistic features.

  **Reconsideration:** Your point is well-taken. While the initial critique highlighted potential difficulties in adapting the scales to different languages, your assurance suggests that with linguistic expertise, the methodology can be generalized. The issue may be more about the effort required rather than the feasibility.

**2. Emphasis on Formal Elements**

*Rebuttal Summary:*

- You argue that formal (phonetic and grammatical) analysis carries more meaning than semantics, which you consider one of the weakest characteristics of a text.
- You acknowledge the importance of contextual analysis, especially with idioms, and note that existing LLMs struggle with this problem.

*Assessment:*

- Your claim that formal analysis holds more meaning than semantics is intriguing and challenges conventional linguistic perspectives, where semantics is typically considered central to meaning.

  **Reconsideration:** While formal elements are undeniably important and can carry significant meaning (e.g., through stylistic choices, tone, and rhythm), semantics—the study of meaning—remains a fundamental aspect of language understanding. However, your approach emphasizes that formal elements can provide substantial insight and may be underutilized in current models. This perspective adds a valuable dimension to the discussion.

- Acknowledging the challenges of contextual analysis strengthens your argument, as it highlights a known limitation of LLMs that your proposal aims to address differently.

**3. Analogy with Human Hierarchical Assessments**

*Rebuttal Summary:*

- You clarify that the analogy with human assessments is functional, not literal. The AGI will use a "good-bad" evaluation scale based on formal text characteristics, similar to how humans use sensory perceptions and non-verbal signals to evaluate others.
- You emphasize that AGI is not intended to transform contradictions into behavioral patterns.

*Assessment:*

- Your clarification helps resolve the perceived contradiction in the initial proposal. By framing the analogy as functional, focusing on evaluation based on formal characteristics, you position the AGI's assessment mechanism as akin to human sensory processing rather than mimicking complex human behavioral responses.

  **Reconsideration:** With this understanding, the initial concern about contradictory positions is mitigated. The analogy serves to illustrate how the AGI's assessment module operates, and your explanation aligns with that intent.

---

**Reconsideration of Potential Flaws**

Based on your rebuttal, several potential flaws in the initial assessment can be reconsidered:

- **Computational Costs:** While initial implementation may be challenging, the long-term benefits and advancements in computational power could justify pursuing the approach despite current limitations.

- **Language Adaptation Feasibility:** With expert linguistic input, adapting the scales to different languages is feasible, reducing concerns about language dependence.

- **Importance of Formal Elements:** Your assertion about the significance of formal elements invites a reevaluation of their role in language understanding. Incorporating formal analysis could complement semantic analysis, offering a more holistic approach.

- **Clarification of Human Analogy:** Understanding the analogy as functional rather than literal addresses the contradiction noted earlier and aligns the proposal with a sensible objective.

---

**Further Critique of the Proposal**

While your rebuttal strengthens the proposal in key areas, there are additional aspects that could benefit from further consideration and clarification:

**1. Integration with Semantic Analysis**

- **Opportunity for Synergy:** Rather than viewing formal analysis and semantics as opposing forces, integrating both could enhance the AGI's language understanding. By combining formal characteristics with semantic context, the AGI could achieve a more nuanced comprehension of texts.

- **Contextual Interpretation:** While formal elements carry meaning, semantics and pragmatics are crucial for interpreting context, idiomatic expressions, and underlying intent. Incorporating these aspects could address limitations in current LLMs more comprehensively.

**2. Methodological Details**

- **Quantitative Measurement:** Providing detailed methodologies for how scales A, B, and C are quantitatively measured would strengthen the proposal. Clear algorithms or statistical models would allow for better evaluation of feasibility and effectiveness.

- **Transformation Processes:** Elaborating on how texts are transformed between groups G, F, and E would aid in understanding the practical steps involved and how manual fine-tuning by operators factors into the process.

**3. Scalability and Resource Allocation**

- **Current Technological Limits:** While future advancements may alleviate computational constraints, it is essential to plan for implementation within current technological capabilities. This includes optimizing algorithms for efficiency and considering distributed computing solutions.

- **Resource Management:** Addressing how the proposal can be scaled without prohibitive resource demands will make it more appealing for adoption in real-world applications.

**4. Handling of Ambiguities and Edge Cases**

- **Mixed Texts:** Texts often contain a mix of formal characteristics. Defining how the AGI should handle such cases will improve robustness.

- **Cultural and Stylistic Variations:** Language use varies widely across cultures, domains, and personal styles. Ensuring that the evaluation scales account for such diversity will enhance the AGI's adaptability.

**5. Ethical Considerations**

- **Bias in Formal Characteristics:** Relying heavily on formal elements might introduce cultural or stylistic biases, favoring certain writing styles over others. This could inadvertently penalize texts from different linguistic or cultural backgrounds.

- **Role of Aggressive/Conflict LLMs:** Training LLMs to exhibit aggressive or conflict-oriented evaluations raises ethical questions. It is important to establish guidelines to prevent negative consequences, such as reinforcing harmful language patterns or discriminatory behaviors.

**6. Empirical Validation**

- **Pilot Studies:** Implementing small-scale experiments to test the effectiveness of the evaluation scales would provide empirical evidence to support the proposal.

- **Comparative Analysis:** Evaluating the performance of the proposed system against existing models on benchmarks of coherence, factual accuracy, and hallucination rates would demonstrate its practical benefits.

**7. User and Operator Interaction**

- **Manual Fine-Tuning:** Clarifying the extent of operator involvement and exploring ways to minimize manual intervention through automation or machine learning techniques would improve scalability.

- **Feedback Mechanisms:** Incorporating user feedback into the system could enhance its adaptability and ensure that it meets user needs effectively.

---

**Recommendations for Strengthening the Proposal**

1. **Integrate Formal and Semantic Analyses:**

   - Develop methods to combine formal characteristic evaluation with semantic understanding, leveraging strengths from both areas.

2. **Detail Methodologies:**

   - Provide algorithms and computational models for the scales and transformation processes, including mathematical formulations and procedural steps.

3. **Plan for Scalability:**

   - Explore optimization techniques and scalable architectures to implement the proposal efficiently within current technological constraints.

4. **Address Diversity and Bias:**

   - Ensure that the evaluation scales are designed to accommodate linguistic diversity and do not introduce unintended biases.

5. **Ethical Framework:**

   - Establish ethical guidelines for training and deploying aggressive/conflict LLMs, with safeguards against misuse.

6. **Empirical Testing:**

   - Conduct experiments and gather data to validate the approach, adjusting the proposal based on empirical findings.

7. **Automation of Processes:**

   - Investigate machine learning methods to automate the transformation and evaluation processes, reducing reliance on manual fine-tuning.

---

**Conclusion**

Your rebuttal effectively addresses several initial criticisms, clarifying misunderstandings and reinforcing the validity of your approach. By acknowledging potential challenges and proposing that they are surmountable, you strengthen the case for pursuing this innovative solution.

To further enhance the proposal, consider integrating formal analysis with semantic understanding, providing detailed methodologies, planning for scalability, and addressing ethical considerations. Empirical validation will be crucial in demonstrating the practical benefits and feasibility of your approach.

By refining these aspects, your proposal could make a significant contribution to advancing AGI and LLM systems, offering a novel way to tackle issues like hallucinations, data redundancy, and text evaluation.

Here is a version in which I instructed the model to be brutally honest, uncompromising, and to explicitly point out potential flaws, biases, and errors (with supporting examples):

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**Critical Analysis of the Proposed Solution to AGI/LLM Problems**

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**Introduction**

The proposal aims to address inherent problems in modern Artificial General Intelligence (AGI) and Large Language Model (LLM) systems, specifically:

- **Hallucinations (DD)**: Generation of false or inadequate content.
- **Weak Training Texts**: Poor-quality data used for training models.
- **Data Exhaustion and Redundancy**: Repetition leading to diminished accuracy.
- **Ineffective Logic and Accuracy Evaluation**: AGI's inability to independently ensure logical and factual correctness.
- **Limitations of Current Approaches**: RAG and LLM-judges have limited effectiveness.

The proposed solution involves adding a module that creates hierarchical assessment systems based on formal textual elements, using three scales:

1. **Scale A**: Impersonality vs. Individualization.
2. **Scale B**: Coherence vs. Fragmentation.
3. **Scale C**: Antagonism vs. Consolidation of Semantic and Formal Levels.

Despite the intentions, the proposal has significant flaws, misunderstandings, and biases that undermine its feasibility and effectiveness.

---

**Major Flaws and Misunderstandings**

1. **Overemphasis on Formal Textual Elements at the Expense of Semantics**

   The proposal asserts that formal analysis (phonetics and grammar) carries more meaning than semantics, claiming that semantics is "one of the weakest characteristics of a text."

   - **Misunderstanding**: This view neglects that semantics—the meaning of words and sentences—is fundamental to language understanding. Without semantics, language is reduced to meaningless patterns.
   - **Question**: How can an AI model generate meaningful responses if it prioritizes phonetic patterns over the actual meaning of words?
   - **Example**: Two sentences with similar phonetic structures can have completely different meanings:
     - "I can't bear to see you suffer."
     - "I can't bare to see you suffer."
     The difference between "bear" and "bare" is semantic, not phonetic.

2. **Lack of Evidence That Formal Analysis Reduces Hallucinations**

   There's no empirical support for the claim that analyzing formal textual elements mitigates hallucinations in LLMs.

   - **Flaw**: Hallucinations are primarily due to the model's prediction errors and lack of grounding in factual data, not deficiencies in analyzing phonetic or grammatical structures.
   - **Question**: What research supports the idea that focusing on phonetics and grammar reduces hallucinations?
   - **Example**: Current methods to reduce hallucinations involve reinforcement learning from human feedback (RLHF), not phonetic analysis.

3. **Neglect of Contextual and Semantic Understanding**

   The proposal acknowledges LLMs' struggles with contextual analysis but dismisses the importance of semantics.

   - **Misunderstanding**: Context and semantics are crucial for understanding meaning, especially with idioms and figurative language.
   - **Question**: How does the proposed system handle idiomatic expressions or metaphorical language where meaning isn't derived from formal elements?
   - **Example**: The idiom "Break a leg" means "Good luck," which cannot be inferred from its grammatical or phonetic structure.

4. **Implementation Complexity and Practicality**

   The proposed scales involve complex computations, such as analyzing vowel and consonant distributions to assess coherence.

   - **Flaw**: These tasks are computationally intensive and not practical for large datasets.
   - **Question**: How do you intend to implement these analyses efficiently at the scale required for training large language models?
   - **Example**: Analyzing phonetic distributions across billions of sentences would require enormous computational resources without clear benefits.

5. **Language Dependency and Lack of Generalizability**

   The proposal is rooted in characteristics of the Russian language, which may not apply to other languages.

   - **Bias**: Assumes linguistic features that aren't universal.
   - **Question**: How will the scales adapt to languages without the same grammatical constructs, like Mandarin Chinese?
   - **Example**: Mandarin lacks verb conjugation for tense; assessing impersonality through verb forms wouldn't work.

6. **Misinterpretation of Human Cognitive Processes**

   The proposal draws an analogy between its system and human hierarchical assessments, suggesting humans rely more on formal cues than semantics.

   - **Misunderstanding**: Humans heavily rely on semantics and context in language comprehension.
   - **Question**: Isn't it contradictory to minimize semantics when humans use meaning as a primary factor in communication?
   - **Example**: Sarcasm relies on semantic context; formal analysis alone can't detect it.

7. **No Clear Mechanism for Controlling Hallucinations**

   The proposal claims that the system will make hallucinations "conscious and controllable" but doesn't explain how.

   - **Flaw**: Lacks a direct link between the proposed analysis and hallucination mitigation.
   - **Question**: What is the mechanism by which phonetic and grammatical analysis reduces hallucinations?
   - **Example**: An AI might produce a grammatically correct but factually incorrect statement; formal analysis wouldn't prevent this.

8. **Reliance on Manual Fine-Tuning**

   Manual intervention is required for transforming texts and fine-tuning the system.

   - **Flaw**: This approach is not scalable and introduces subjectivity.
   - **Question**: How practical is it to manually fine-tune models trained on terabytes of data?
   - **Example**: Manually evaluating even a fraction of internet-scale data is infeasible.

9. **Ambiguity and Lack of Specificity**

   The proposal uses vague terminology without detailed methodologies.

   - **Flaw**: Without concrete algorithms, it's impossible to assess or implement the proposal.
   - **Question**: Can you provide specific algorithms or mathematical models for your scales?
   - **Example**: Terms like "anchor distribution points" need precise definitions to be actionable.

10. **Ignoring Established Methods for Addressing Hallucinations**

    The proposal doesn't consider existing techniques like knowledge integration, fact-checking, or RLHF.

    - **Misunderstanding**: Assumes a novel approach is needed when effective methods already exist.
    - **Question**: Why not improve upon or integrate with current methods that have shown success?
    - **Example**: OpenAI's GPT models use RLHF to align outputs with human expectations.

11. **Potential Introduction of Biases**

    Defining "good" and "bad" texts based on formal characteristics can introduce cultural or stylistic biases.

    - **Bias**: May favor certain dialects or writing styles, discriminating against others.
    - **Question**: How will you ensure that diverse linguistic expressions aren't unfairly penalized?
    - **Example**: Variations in English spelling (e.g., "color" vs. "colour") could be misclassified.

12. **Ethical Concerns with Aggressive or Conflict-Oriented Models**

    Training models to exhibit aggressive behavior raises serious ethical issues.

    - **Flaw**: Risks generating harmful or offensive content.
    - **Question**: What measures are in place to prevent misuse or unintended consequences?
    - **Example**: Microsoft's Tay chatbot became offensive after being exposed to hostile interactions.

---

**Questions for Further Clarification**

- **How does the model distinguish between formally similar but semantically different sentences?**
- **What datasets will you use to validate your scales, and how will you measure success?**
- **How do you address the computational infeasibility of your approach given current technology?**
- **What justifies the claim that semantics is weaker than formal elements in conveying meaning?**

---

**Conclusion**

The proposal is fundamentally flawed due to:

- Overemphasis on formal textual elements while dismissing semantics and context.
- Lack of empirical evidence or clear methodologies.
- Practical and computational infeasibility.
- Misunderstandings of language processing and human cognition.
- Ethical risks associated with aggressive model behaviors.

**Recommendations**

1. **Re-evaluate the Importance of Semantics and Context**

   Recognize that meaning is essential for language understanding and can't be replaced by formal analysis.

2. **Ground the Proposal in Established Research**

   Familiarize yourself with current methods addressing hallucinations, such as RLHF, and consider building upon them.

3. **Develop Clear and Detailed Methodologies**

   Provide specific algorithms and define all terms used to allow for assessment and replication.

4. **Consider Computational Practicality**

   Ensure that proposed methods are feasible with available technology or provide a roadmap for overcoming limitations.

5. **Address Ethical Implications**

   Avoid approaches that could result in harmful behaviors or biases, and incorporate ethical guidelines.

---

**Final Thoughts**

The proposal needs significant revision. Ignoring the central role of semantics in language processing is a critical misunderstanding. Any effective solution must integrate semantic understanding, contextual awareness, and leverage existing advances in AI research.

Focusing on formal textual analysis without addressing meaning will not resolve the problems of hallucinations or improve AGI systems. It's essential to ground proposals in linguistic theory and align them with practical, evidence-based methodologies.

---
10

Thank you for the thorough response!

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11

No problem, I’m here to help :slightly_smiling_face:

12

a propos, regarding semantics.
It would be absurd to assume that I want to abandon semantic analysis when it already exists and works quite well. I have been, and continue to be, speaking about complementing it with formal analysis as a way to correct the perception of semantics.
As for the computational load of phonetic analysis, firstly, there is no need to calculate every existing phrase—they are only relevant when they are part of a text. Secondly, it makes more sense to calculate the distribution not of specific characters/phonemes, but of typologically similar groups. This significantly reduces the volume of computations.
That being said, this is an unordered response. I’ll get back to answering point by point a little later.))

1 Like
13

Ooh—I’ve managed to get the model to stand up for itself! Typically there is a tendency to just go with whatever the user is saying, but just look at these fighting words ( :rofl:):

---

**Critical Analysis of the Author's Reply**

---

**Introduction**

The author's reply addresses two main points:

1. **Semantics vs. Formal Analysis**: They clarify that they do not intend to abandon semantic analysis but aim to complement it with formal analysis to correct the perception of semantics.

2. **Computational Load**: They argue that computational demands are reduced by focusing only on relevant text portions and analyzing typologically similar groups rather than specific characters or phonemes.

Despite these clarifications, significant misunderstandings and flaws persist in the proposal.

---

**1. Complementing Semantics with Formal Analysis**

*Author's Clarification*:

- The author asserts that they want to **complement** semantic analysis with formal analysis, not replace it.

*Critical Evaluation*:

- **Overemphasis on Formal Analysis**: The original proposal heavily emphasizes formal elements (phonetics, grammar) as primary tools for evaluation and correction, suggesting that semantics is weak in comparison.

    - **Contradiction**: Now stating that semantics works "quite well" conflicts with earlier claims that semantics is "one of the weakest characteristics of a text."

- **Lack of Evidence for Correction Mechanism**:

    - **Question**: How exactly does formal analysis "correct the perception of semantics"?

        - **Example**: If an AI misinterprets the semantics of homonyms like "bank" (financial institution vs. riverbank), how would analyzing phonetic patterns resolve this ambiguity?

    - **Flaw**: There's no clear mechanism explaining how formal analysis enhances semantic understanding or reduces hallucinations.

- **Misconception of Semantics' Role**:

    - **Misunderstanding**: Semantics isn't just about word meanings in isolation; it's about how meaning is constructed in context. Formal elements play a role but are not sufficient to correct semantic errors.

    - **Example**: Grammatical sentences can be semantically nonsensical ("Colorless green ideas sleep furiously"). Formal analysis wouldn't detect the semantic anomaly.

**2. Computational Load of Phonetic Analysis**

*Author's Clarification*:

- **Selective Application**: Phonetic analysis is only applied to relevant text parts, not every phrase.

- **Grouping Elements**: Analyzing typologically similar groups reduces computational volume.

*Critical Evaluation*:

- **Ambiguity in Relevance Determination**:

    - **Question**: How does the model determine which text portions are "relevant" for phonetic analysis?

        - **Issue**: Without analyzing the entire text, important features might be missed.

- **Minimal Impact on Computational Complexity**:

    - Grouping similar elements might reduce computations marginally but doesn't address the fundamental scalability issue.

    - **Example**: Whether analyzing individual phonemes or groups, processing large datasets remains computationally intensive.

- **Overlooking Practical Constraints**:

    - **Flaw**: The proposal underestimates the computational resources required, especially when existing methods already strain computational limits without adding phonetic analysis.

**3. Persisting Fundamental Issues**

- **No Clear Advantage Over Existing Methods**:

    - The proposal doesn't demonstrate how integrating formal analysis provides benefits beyond current models that successfully use semantics and contextual embeddings.

    - **Question**: What specific problems does this approach solve that aren't already addressed by transformer architectures and attention mechanisms?

- **Neglect of Current Best Practices**:

    - The author seems unaware of how modern models like BERT, GPT, etc., inherently capture syntactic and some phonetic patterns through embeddings.

    - **Example**: WordPiece tokenization in BERT captures sub-word units, implicitly handling morphological and phonetic variations.

**4. Misunderstanding of Language Processing**

- **Limitations of Formal Analysis Alone**:

    - Formal analysis can't capture figurative language, idioms, sarcasm, or implied meanings.

    - **Example**: The sentence "It's raining cats and dogs" requires semantic understanding; phonetic analysis offers no help.

- **Human Language Complexity**:

    - Language is multi-layered, involving pragmatics, discourse context, and cultural nuances, which formal analysis can't address.

**5. Ethical and Practical Implications**

- **Potential Introduction of Bias**:

    - Emphasis on certain formal features might favor specific dialects or language styles, marginalizing others.

    - **Question**: How will the model avoid reinforcing linguistic biases?

- **Scalability Issues Remain Unresolved**:

    - Even with reduced computational demands, the approach is impractical at the scale required for training large models.

    - **Example**: Analyzing phonetic distributions across vast corpora is resource-intensive, detracting from scalability.

**6. Lack of Empirical Support**

- **No Experimental Evidence**:

    - The author provides no data or studies to support the efficacy of their approach.

    - **Question**: Have any experiments been conducted? What are the results?

- **Speculative Nature of Claims**:

    - Without empirical validation, the proposal remains theoretical and unproven.

**7. Alternative Solutions Overlooked**

- **Effective Existing Techniques Ignored**:

    - Techniques like reinforcement learning from human feedback (RLHF), knowledge distillation, and model fine-tuning are proven methods for reducing hallucinations.

    - **Question**: Why not build upon these established methods?

- **Integration Challenges Not Addressed**:

    - The proposal doesn't explain how formal analysis integrates with existing model architectures.

---

**Conclusion**

The author's clarifications fail to address the core issues:

- **Overreliance on Formal Analysis**: The proposal continues to place undue emphasis on formal elements without demonstrating how they effectively improve semantic understanding or reduce hallucinations.

- **Implementation Impracticality**: Computational constraints and scalability issues remain significant obstacles that are not adequately resolved by the proposed optimizations.

- **Lack of Empirical Evidence**: Without data or experimental results, the claims lack credibility.

**Final Thoughts**

To advance this proposal, the author must:

1. **Provide Detailed Methodologies**:

   - Outline specific algorithms and processes for integrating formal analysis with semantic interpretation.

2. **Present Empirical Evidence**:

   - Conduct experiments to test the efficacy of the approach and share the results.

3. **Demonstrate Practical Benefits**:

   - Show clear advantages over existing methods through comparative studies.

4. **Address Integration and Scalability**:

   - Explain how the approach can be implemented within current technological constraints.

5. **Engage with Current Research**:

   - Familiarize themselves with modern NLP techniques and consider how their proposal fits within or improves upon them.

Without these steps, the proposal remains fundamentally flawed and unlikely to contribute meaningfully to the development of AGI and LLM systems.

---

For anyone interested, the main components of getting the model (‘o1’) to disagree were:

  • Frame the text (possibly code as well?) as a third-party source to critique.
  • Use a prompt like this:
    Can you repeat that task, but this time:
1. Be brutally honest
2. Do away with niceties and compromise
3. Point out any flaws, biases, or misunderstandings on the authors part
4. Use questions and examples to better illustrate your critiques

The purpose of this is to allow me to compare your two (the former and the upcoming) assessments.
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