I don’t exactly know how well this prompting approach works yet but it seems worthy of exploring so I thought I’d share with the community…
The intuition is that if “think step-by-step” works to marginally improve the models reasoning ability by getting it to break out its thought process out in steps. Maybe we can do something similar to improve the models ability to ideate by getting it to reverse engineer a solution by getting it to think in steps backwards.
Here’s an example prompt:
a team of researchers at Stanford just had a break through where they say they have solved the hallucination problem in large language models. work backwards and reverse engineer their solution.
Reverse engineering a hypothetical breakthrough in solving the hallucination problem in large language models (LLMs) involves several steps. Since this is a speculative exercise, I’ll outline a plausible approach based on current understanding and research trends in the field of artificial intelligence and natural language processing.
Step 1: Understanding the Hallucination Problem
Hallucination in LLMs refers to the generation of text that is plausible-sounding but factually incorrect or nonsensical. This can occur due to several reasons, such as:
Over-reliance on patterns in training data without understanding context.
Lack of grounding in real-world knowledge.
Inability to verify facts or cross-reference information.
Step 2: Hypothesize the Solution Components
Given the nature of the problem, a comprehensive solution might involve several components:
Enhanced Training Data: Ensuring the training data is more accurate, diverse, and representative of real-world knowledge.
Contextual Understanding: Improving the model’s ability to understand and maintain context over long conversations.
External Knowledge Integration: Leveraging external databases and knowledge graphs.
Feedback Loops: Implementing mechanisms for continuous learning and correction.
Step 3: Enhanced Training Data
Data Curation: The researchers likely curated a high-quality dataset, filtering out unreliable sources and ensuring a diverse range of topics.
Data Augmentation: They might have used techniques to augment the dataset with paraphrased and contextually varied examples to improve the model’s robustness.
Step 4: Fact-Checking Mechanisms
Real-Time Fact-Checking: The solution could involve integrating APIs from reliable fact-checking services or databases like Wikipedia, Wolfram Alpha, or specialized knowledge bases.
Cross-Referencing: The model might cross-reference generated text with multiple sources to ensure consistency and accuracy.
Step 5: Contextual Understanding
Long-Context Models: They could have developed models capable of maintaining context over longer text spans, possibly using advanced architectures like transformers with extended context windows.
Contextual Embeddings: Improved embeddings that better capture the nuances of context and meaning.
Step 6: External Knowledge Integration
Knowledge Graphs: The model might integrate with knowledge graphs that provide structured, real-world information.
Dynamic Retrieval: Implementing a retrieval-based approach where the model dynamically fetches relevant information from external sources during generation.
Step 7: Feedback Loops
User Feedback: Incorporating user feedback to identify and correct hallucinations.
Self-Supervised Learning: Using self-supervised learning techniques to continuously improve the model based on new data and corrections.
Step 8: Model Architecture and Training
Hybrid Models: Combining generative models with retrieval-based models to balance creativity and accuracy.
Fine-Tuning: Fine-tuning the model on specific tasks that require high factual accuracy, such as medical or legal text generation.
Step 9: Evaluation and Testing
Benchmarking: Developing new benchmarks and evaluation metrics specifically designed to measure hallucination rates.
A/B Testing: Conducting extensive A/B testing with human evaluators to compare the performance of the new model against previous versions.
Step 10: Implementation and Deployment
Scalable Infrastructure: Ensuring the solution is scalable and can be deployed in real-world applications.
User Interface: Designing user interfaces that allow users to easily identify and report hallucinations.
Hypothetical Solution Summary
The Stanford researchers likely developed a multi-faceted approach combining enhanced training data, real-time fact-checking, improved contextual understanding, external knowledge integration, and continuous feedback mechanisms. They might have also innovated in model architecture, creating a hybrid system that balances generative capabilities with retrieval-based accuracy.
By addressing the hallucination problem from multiple angles, they could have significantly reduced the occurrence of hallucinations in large language models, leading to more reliable and trustworthy AI-generated text.
a team of researchers at Stanford just solved the planning problem for large language models. work backwards and reverse engineer their solution.
Reverse engineering a hypothetical solution to the planning problem for large language models (LLMs) involves several steps. Since this is a speculative exercise, I’ll outline a plausible approach based on current knowledge and trends in AI research.
Step 1: Understanding the Planning Problem
The planning problem in the context of LLMs involves generating coherent, goal-directed sequences of actions or text. This requires the model to:
Understand the goal.
Break down the goal into sub-goals or steps.
Generate a sequence of actions or text that achieves the goal.
Step 2: Identifying Key Components of the Solution
To solve the planning problem, the researchers likely focused on several key components:
Goal Representation: How to represent the goal in a way that the LLM can understand and use.
Sub-goal Decomposition: Breaking down the main goal into manageable sub-goals.
Sequence Generation: Generating a coherent sequence of actions or text that leads to the goal.
Feedback Mechanism: Ensuring the generated sequence is on track to achieve the goal.
Step 3: Goal Representation
The researchers might have developed a method to encode goals in a way that LLMs can process. This could involve:
Natural Language Descriptions: Using natural language to describe goals.
Structured Representations: Using structured formats like JSON or XML to represent goals and sub-goals.
Step 4: Sub-goal Decomposition
To break down goals into sub-goals, the researchers could have used:
Hierarchical Task Networks (HTNs): A method from classical AI planning where tasks are broken down into smaller tasks.
Reinforcement Learning (RL): Training the model to learn sub-goal decomposition through trial and error.
Prompt Engineering: Designing prompts that guide the LLM to generate sub-goals.
Step 5: Sequence Generation
For generating sequences, the researchers might have:
Fine-tuned the LLM: Using a dataset of goal-directed sequences to fine-tune the model.
Used Transformers: Leveraging the transformer architecture to generate coherent sequences.
Incorporated Memory Mechanisms: Using memory-augmented neural networks to keep track of the sequence and ensure coherence.
Step 6: Feedback Mechanism
To ensure the generated sequence is on track, the researchers could have implemented:
Self-critique Mechanisms: Allowing the LLM to evaluate its own output and make adjustments.
External Feedback: Using human or automated feedback to correct the sequence.
Iterative Refinement: Generating sequences iteratively and refining them based on feedback.
Step 7: Integration and Testing
Finally, the researchers would integrate all these components and test the system extensively:
Benchmarking: Using standard benchmarks to evaluate the performance of the system.
Real-world Testing: Applying the system to real-world tasks to ensure its practical utility.
Hypothetical Solution Summary
Goal Representation: Developed a method to encode goals in natural language or structured formats.
Sub-goal Decomposition: Used HTNs, RL, or prompt engineering to break down goals.
Sequence Generation: Fine-tuned the LLM, used transformers, and incorporated memory mechanisms.
Feedback Mechanism: Implemented self-critique, external feedback, and iterative refinement.
Integration and Testing: Integrated all components and tested extensively using benchmarks and real-world tasks.
By following these steps, the researchers at Stanford could have developed a robust solution to the planning problem for large language models.
Sure, let’s delve deeper into the sub-goal decomposition step. This is a critical part of solving the planning problem, as it involves breaking down a complex goal into smaller, more manageable tasks that the model can handle sequentially or in parallel.
Step 4: Sub-goal Decomposition
4.1 Hierarchical Task Networks (HTNs)
Hierarchical Task Networks (HTNs) are a classical AI planning method where tasks are decomposed into smaller subtasks. Here’s how HTNs could be applied:
Task Representation: Represent the main goal as a high-level task.
Decomposition Rules: Define rules for breaking down high-level tasks into subtasks. These rules can be learned from data or manually crafted.
Recursive Decomposition: Apply the decomposition rules recursively until the tasks are simple enough for the LLM to handle directly.
Example: If the goal is “Plan a trip to Paris,” the HTN might break it down into subtasks like “Book flights,” “Reserve hotel,” “Create itinerary,” etc.
4.2 Reinforcement Learning (RL)
Reinforcement Learning can be used to train the model to decompose goals through trial and error:
Environment Setup: Define an environment where the LLM can perform actions to achieve sub-goals.
Reward Function: Design a reward function that gives positive feedback when the model successfully decomposes a goal and achieves sub-goals.
Policy Learning: Use RL algorithms (e.g., Q-learning, Policy Gradients) to learn a policy for decomposing goals into sub-goals.
Example: The model might be trained in a simulated environment where it receives rewards for successfully planning and executing a trip to Paris.
4.3 Prompt Engineering
Prompt engineering involves designing prompts that guide the LLM to generate sub-goals:
Template Prompts: Create templates that explicitly ask the model to break down a goal. For example, “To achieve [goal], what are the necessary steps?”
Contextual Prompts: Provide context that helps the model understand the goal and generate relevant sub-goals. For example, “Given the goal of planning a trip to Paris, list the steps needed to complete this task.”
Iterative Prompts: Use a series of prompts to iteratively refine the sub-goals. For example, “What is the first step to plan a trip to Paris?” followed by “What is the next step after booking flights?”
4.4 Knowledge Graphs and Ontologies
Using knowledge graphs and ontologies can help in understanding the relationships between different tasks and sub-tasks:
Graph Representation: Represent goals and sub-goals as nodes in a graph, with edges indicating dependencies or relationships.
Graph Traversal: Use graph traversal algorithms to identify paths from the main goal to sub-goals.
Ontology Integration: Integrate domain-specific ontologies to provide structured knowledge that aids in sub-goal decomposition.
Example: A knowledge graph for travel planning might include nodes for “Book flights,” “Reserve hotel,” and “Create itinerary,” with edges indicating the sequence of tasks.
4.5 Case-Based Reasoning
Case-based reasoning involves using past cases to inform the decomposition of new goals:
Case Library: Maintain a library of past cases where goals were successfully decomposed and achieved.
Similarity Matching: Use similarity measures to find past cases that are similar to the current goal.
Adaptation: Adapt the sub-goals from past cases to fit the current goal.
Example: If the goal is to plan a trip to Paris, the model might retrieve a past case of a trip to Rome and adapt the sub-goals accordingly.
Integration of Sub-goal Decomposition Methods
In practice, the researchers might combine several of these methods to achieve robust sub-goal decomposition:
Hybrid Approach: Use HTNs for initial decomposition, RL for learning optimal sub-goal sequences, and prompt engineering to guide the LLM.
Feedback Loop: Implement a feedback loop where the model’s sub-goal decomposition is evaluated and refined based on performance.
Continuous Learning: Continuously update the model with new data and cases to improve its sub-goal decomposition capabilities.
By integrating these techniques, the researchers at Stanford could have developed a sophisticated system for sub-goal decomposition, enabling the LLM to effectively break down complex goals into manageable tasks.
I’m trying to get the model to think outside it’s distribution… If you ask the model “how would you solve hallucinations” it’s not going to parrot back anything novel. It can only predict what someone would say based on everything its read so unless it’s read a solution to the hallucination problem it can’t generate the tokens for that solution.
What it can do is combine. You can ask it to “create a version of chess that’s inspired by bunnies” and I challenge you to say that its answer isn’t novel. The thing is you need to give the model the spark and it will run with it. With this reverse engineer prompt I’m hoping to get the model down a token path that it’s never encountered in its training set.
Basically, the model is really good at guessing why things are the way they are so maybe it can guess its way to novel problem solutions by being forced to reverse engineer things.
I am just trying to figure out how I would actually use that output. By default - in this particular example - I know that the output itself is likely a hallucination and not representative of the actual approach the team has taken.
Now I understand that we can still gain from having the model provide a plausible approach for how this was achieved. But again, given it’s hypothetical information, how does it help me? Is it just intended as input for general brainstorming or is it intended as input to help solve other problems?
How would the results differ from a prompting approach that would involve providing the model with the actual approach that was taken and then asking it to identify alternative (different) solution paths? (NB: I haven’t tested it but curious to see to what degree this would also result in new token paths).
Yeah Im working that out. I was doing an experiment earlier tonight where I gave the model a list of key ML innovations leading up to transformers and then spent an hour with it brainstorming ideas beyond transformers and I couldn’t get it to come up with a single idea that I haven’t heard a human suggest. It simply won’t parrot back concepts outside of its distribution.
But then I got to thinking… I know it can be creative. I’ve seen it. If you ask the model to create a new idea that’s a mix of two existing ideas (convergent thinking) it’s insanely creative. So why is that?
It’s because I’m providing it with the spark that’s going to lead it to something creative. Then the reverse engineer idea hit me.
If I can get the model to first generate a plausible hallucination by working backwards through a problem. I can then use that as a guide to drill into potentially novel solution paths in the forward direction…. It would be like disassembling a camera and then speculating on what the shutter doors do
From experience, I’d say working top-down from a hypothetical given just primes the model for hallucination, trying to reconcile things it can’t. but, as we know, there’s a fine line between creativity and insanity. So knowingly tip-toeing on the border of schizophrenia is an interesting idea.
I’m still skeptical though:
I suspect that most successful creatives still work off of what they know. I don’t know if leaving the defined distribution through interpolation (if it works) will actually be much better than playing the token lottery…
It feels a bit off, but I certainly don’t want to discourage you from playing with the idea more!
In this case you want it to hallucinate. If an idea doesn’t exist yet and the AI creates a novel idea it’s by definition hallucinating.
The model knows everything that’s ever been published. If a human comes up with a novel idea and that idea is an amalgamation of other ideas then the odds are the roots of that novel idea lie somewhere in the models distribution. The challenge is getting the model to recognize that it can take a set of concepts it already knows and recombine them into something new and interesting.
It would be interesting to then find a way to still “ground” the model’s response in facts somehow.
So basically, how do you validate that (a) it’s genuinely a novel idea and (b) it’s a realistic/feasible idea? I wonder if at some point in the process - likely after the initial hallucinated ideation - you’d want to expose it to factual data? Or is this where the human evaluation would come in?
I’ve been thinking about this more and I think “reverse engineering” a desired outcome is just the first step of a process. You take the outcome you want, a better approach to AI planning, and ask the model to reverse engineer the solution by thinking backwards step by step. This results in the model making its best guess for how the outcome might be achieved.
You then take all of those steps and let the model start exploring them genetically using Monte Carlo Tree Search. Lots of details need to be flushed out there but the end goal is for the model to arrive at its best approximation for a working solution that achieves the desired outcome.
You dont want that in this first phase. This is just creating a set of initial best guesses for achieving the desired outcome. You use Monte Carlo Tree Search or a diffusion model of some sort from there
Yeah, but that would still be in-distribution, wouldn’t it? A constructive amalgamation of known things constructing a previously unknown thing, now in context instead of in training.
Ultimately I think that mentioning your target (“solved hallucination problem”) is supposed to be a sort of heuristic that tells the model in what direction it should go - but I’m thinking our issue is that it often stumbles by generating correct-sounding assumptions and then falling apart when the collection of assumptions become irreconcilable.
Typically you’d go from a grounded state, trying to stay grounded, building up towards your heuristic - in this case you’re ungrounded hoping to somehow eventually collapse into a grounded state.
I think there’s too many loose variables for this to work in a reliable manner, but, as you said in the beginning, it could be used to generate intermediate potential heuristics (you said generate ideas)
It would be… This weekend I was planning to look through some recent papers to see if I can find a novel idea the model wouldn’t know about (suggestions welcome) I want to give the model a basic outline of the idea without revealing how it works so that I can see how close it gets to predicting the actual solution when reverse engineering the goal.
I’m still thinking about how to structure this experiment so ideas welcome
Yeah. The model is ultimately going to need tools that it can use to run experiments like what AI Scientist does. Humans are the same way though… our ideas can only take us so far. The devils in the details and you ultimately have to run experiments
but the challenge, here, I think, is somehow being able to verify whether the concept is grounded or not, and I think you need an experience graph to do that (what I think strawberry is supposed to be)
Interesting… I’ve been thinking for some time that you probably need some sort of diffusion model to do broad ideation which sounds kind of similar to the wave function.
The wave nature of things is particularly interesting because that meshes with the lens idea I’ve been talking about on here.
When lenses are applied to large sets of data they seem to point the models embeddings all in the same directions. This shape that comes out feels very much like a wave and that’s actually how I’ve been thinking about things.
I can pass a bunch of unstructured data through a lens (26 million tokens worth as an example) and what comes out is a highly uniform transformation of those 26 million tokens that’s around 2 million tokens in length. I can then bend these tokens in insanely predictably ways with future queries. It’s almost like applying a wave function to information.
I’ll give you an example… let’s say the output of a lens is something like a structured list companies names and their industries but there’s like 200 of them. The uniformity of the output makes it easier for the model to do nearest neighbor transformations on that list. You can ask for the address of the corporate headquarters and ceo and you’ll very reliably get back 200 addresses and CEO names.
If you don’t first run your data source through the lens you’ll be lucky to get 50 back.
