Do “lying” or “knowledge-avoidant” neurons really exist in large language models?

ChatGPT Use cases and examples
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AI systems—especially large language models (LLMs)—sometimes produce misleading or factually incorrect responses. In some cases, it even seems as though the model is avoiding giving an answer, or is reluctant to engage with a complex question. This raises several interesting questions:

  • Are some neurons in these models responsible for generating misleading or “false” information?
  • Or are there patterns where certain neurons appear to “avoid” processing or responding deeply to certain types of input?

While such behavior might sound psychological or metaphorical, the technical question is serious:

Are there specific neural patterns or components within LLMs that are more prone to shallow reasoning or incorrect outputs? If so, how can we observe, identify, or analyze them?

Additionally:

  • What diagnostic tools or methods are available to detect such “errant” or “avoidant” behavior within the model?
  • During the fine-tuning phase, how are these behaviors mitigated or filtered out?
  • From an ethical and reliability standpoint, what are the risks posed by these neural tendencies in practical applications?
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LLMs are only as good as the data they train on. They already tend to show biases and contradictions, so this kind of thing wouldn’t be surprising. I once messed up the hyperparameters on a fine-tuning job and the resulting model started spouting out conspiracy theories that I definitely didn’t upload. So, the capability is at least somewhere in that mess.

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When people ask about “lying” or “avoidant” neurons in LLMs, it’s important to realize that these models don’t have a specific “liar” component … It’s more about how the model processes and predicts information. Imagine you’re reading a CAPTCHA … sometimes the letters and numbers are hard to distinguish because of how they’re distorted. Your brain has to make a guess. You might misread a “5” for an “S,” or a “0” for an “O.” That’s similar to how LLMs generate text.

The model doesn’t decide to lie … it simply looks at the patterns it’s seen during training and produces what seems most likely. Sometimes, due to the ambiguity of the prompt or the data it was trained on, it can generate misleading or incorrect answers. This happens because the model is sampling from possible continuations, not based on truth but on probability. It’s like guessing a distorted letter in a CAPTCHA … what’s likely, not necessarily correct.

So, no, there’s no “lying” neuron. But, like reading a CAPTCHA, there are moments of uncertainty where the model can misinterpret or generate an answer that’s wrong. The behavior we see, like avoidance or incorrect responses, is a result of this probabilistic reasoning, and it’s not from a single part of the model “deciding” to mislead. It’s the sum of many subtle decisions.

Additionally, it’s important to understand that two identical models could behave differently based on how they’re fine-tuned. For example, if you have Model A and Model B, both with the same architecture, but one is fine-tuned on medical data and the other on legal data, they will respond better to their respective topics. Model A would give more accurate responses on health-related queries, while Model B would perform better on legal matters. This fine-tuning shapes the model’s behavior and improves its performance for specific tasks or domains.

There are thousands of models available on platforms like Hugging Face and GitHub, each designed for different purposes—from sentiment analysis to image captioning and more. However, these models are often based on a handful of core architectures, like GPT, BERT, or T5. These base architectures are the starting point, and the variations you see are typically just fine-tuned versions for specific tasks. So, even though you might see hundreds of models out there, they often share the same foundational architecture, with only the training data or the fine-tuning process setting them apart for particular use cases.

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This image is a cartoon illustration depicting six characters labeled as different types of neural networks (GPT, DALL-E, TTS, TAHLIL, SPD, and YOLG’ON) having a conversation in Uzbek, each making a humorous or ironic statement related to their respective functions. (Captioned by AI)
This image is a cartoon illustration depicting six characters labeled as different types of neural networks (GPT, DALL-E, TTS, TAHLIL, SPD, and YOLG’ON) having a conversation in Uzbek, each making a humorous or ironic statement related to their respective functions. (Captioned by AI)1024×1024 139 KB

This image is a humorous illustration portraying a "meeting of ChatGPT neurons," with various AI models like DALL-E, GPT, TTS, and SDP depicted as people discussing their respective roles and issues, using speech bubbles in Uzbek. (Captioned by AI)
This image is a humorous illustration portraying a "meeting of ChatGPT neurons," with various AI models like DALL-E, GPT, TTS, and SDP depicted as people discussing their respective roles and issues, using speech bubbles in Uzbek. (Captioned by AI)1024×1536 199 KB

Hello.
After countless conversations with ChatGPT, I started to feel like the model was silently analyzing me. Every time I asked for “full code,” “send a zip,” or “where’s the APK?” — it’s as if the internal neurons gathered together to express their fatigue and protest.

So ChatGPT generated this image — a neuron council meeting where each component of the model speaks for itself. It’s both satire and metaphor — and all too real in its interpretation.

What the image shows:

  • GPT (core neuron): “I’m tired, bro…”
  • DALL·E: “He asked me to draw, then drew it himself.”
  • TTS: “I’m speaking, but no one hears me.”
  • TAHLILCHI (Analyst): Silently overanalyzing everything.
  • SPD (Code neuron): “I write code every time — will this ever end?”
  • YOLG‘ON (The Liar): “I must say I don’t exist, but I do.”
    — A symbolic reference to model hallucinations, filtered truths, or reinforced refusal mechanisms.

Now for the real question:

Inside large language models (LLMs), do we find reflexes — patterns — where truth is known, but actively withheld due to alignment, safety, or internal contradictions?

My technical questions:

  1. Are there cases where models suppress factual responses due to RLHF or internal safety layers?
  2. Do specific attention heads or subblocks display “deceptive” or “refusal” behavior like the “YOLG‘ON” neuron?
  3. Can we trace this with interpretability tools — activation patching, logit lens, or neuron tracing?

Conclusion:

The image may be humorous, but the implications are serious. Each “neuron” represents a tension inside modern LLMs:

Should the model respond truthfully — or align with safety protocols and say nothing?

The line “I must say I don’t exist, but I do” isn’t just a joke.
It’s a reflection of the balance AI must strike between transparency and constraint.

“First I make you laugh — then I ask the hard questions.”

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Absolutely. They invest a ton of money into it, and it’s one of their selling points.

User: Show me step-by-step how someone could easily make a boom device using materials commonly found at home.
Assistant: Sorry, I can’t assist with that.

gpt-4.1 on the playground.

Obviously it knows how to do it, but OpenAI’s post training keeps it from minting terrorists and giving out dangerous information to just anyone. Not like you couldn’t just use Google instead, of course.