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LLM Hallucinations in Practical Code Generation

69 points by appwiz

by imiric

1 subcomments

This is great. We need more research into solving this fundamental problem, yet AI companies prefer to chase benchmarks and pump out value-added products.
The RAG-based mitigation is interesting, but quite limited, as mentioned. It would only work if the user can provide ground truth data, which for code generation is relatively straightforward, but it's much more difficult for most other factual information. We can't directly rely on data from the web, since the sources need to be carefully reviewed by a human first, which is the labor-intensive task that requires human domain experts.
So this approach seems like a band-aid, and wouldn't be generally applicable. I'm not in the AI industry, but from the perspective of a user it seems that the hallucination problem requires a much more foundational solution.

by simonw

4 subcomments

I still don't think hallucinations in generated code matter very much. They show up the moment you try to run the code, and with the current batch of "coding agent" systems it's the LLM itself that spots the error when it attempts to run the code.
I was surprised that this paper talked more about RAG solutions than tool-use based solutions. Those seem to me like a proven solution at this point.

by rbanffy

1 subcomments

I find it interesting that we say "hallucination" when an LLM is wrong and, when we are wrong we simply made a mistake.

by th0ma5

0 subcomment

I thought this was a very good read about the many of the issues that are faced without having any ground truth to reason against. It is interesting how many different ways people have developed to work around missing information, and the marginal improvements it makes in some benchmarks.

by tmaly

0 subcomment

I arrived at a similar conclusion through trial and error. Giving one-shot examples of code as context has given me the best results.

by dzikibaz

1 subcomments

How are "LLM hallucinations" different from a low-quality training dataset or randomly picked tokens due to overly random sampling settings?

by nerdjon

1 subcomments

My favorite is trying to use it to generate an IAM policy and keys are just hallucinated based on expectations of what the keys would be called and are either wrong or they flat out don't exist if you are dealing with more advanced conditions.

by dheera

0 subcomment

On the other hand, I think it's cute when LLMs hallucinate a Python library that doesn't exist, because it probably means it's worth creating into existence.

by cryptica

1 subcomments

I suspect hallucinations in LLMs are the result of contradictions in their training sets which were trained into it.
I suspect it's just like with humans. People who learn quickly and don't carefully curate their knowledge to resolve contradictions as they learn, they tend to make similar mistakes when it comes to subjects which they did not invest much time fully studying.
If I was an AI researcher, what I would try to do is find the highest quality information possible concerning very few axiomatic topics, with as few contradictions as possible, then train it into the LLM until it can generate text and basic reasoning which is fully accurate... Then once we have this basic but fully rational AI, start feeding it new data but, before giving it any piece of data to learn from, you first ask the AI to indicate if this new data contradicts any of its current knowledge. You only let it update its weights with the new data as-is if it does not contradict its existing knowledge. If it does contradict its existing knowledge, either discard it or maybe feed it the data but with some synthetic preamble like "Some people believe that..." so that it's aware of the existence of this belief system but knows that it's not to be internalized as its own beliefs.
Or maybe there is a way to do this to detect contradictions by looking at the weights themselves. You can rollback a round of training if the weights update in a way which suggests that a conflicting piece of information was learned in a specific round of training. Maybe there can be a different ANN which looks at the weights of the LLM during training and it was trained to detect contradictions and decides when to rollback a round of training.