LLM Hallucinations in Practical Code Generation

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.

I'm surprised to read that from a prominent figure in the industry such as yourself.

The problem is that many hallucinations do not produce a runtime error, and can be very difficult to spot by a human, even if the code is thoroughly reviewed, which in many cases doesn't happen. These can introduce security issues, do completely different things from what the user asked (or didn't ask), do things inefficiently, ignore conventions and language idioms, or just be dead code.

For runtime errors, feeding them back to the LLM, as you say, might fix it. But even in those cases, the produced "fix" can often contain more hallucinations. I don't use agents, but I've often experienced the loop of pasting the error back to the LLM, only to get a confident yet non-working response using hallucinated APIs.

So this problem is not something external tools can solve, and requires a much deeper solution. RAG might be a good initial attempt, but I suspect an architectural solution will be needed to address the root cause. This is important because hallucination is a general problem, and doesn't affect just code generation.

> The problem is that many hallucinations do not produce a runtime error [...]

Don't hallucinations mean nonexistent things, that is, in the case of code: functions, classes, etc. How could they fail to lead to a runtime error, then? The fact that LLMs can produce unreliable or inefficient code is a different problem, isn't it?

Hallucinations can be manifested in different ways. Using nonexistent APIs is just one of them. The LLM could just as well hallucinate code that doesn't fix a problem, or hallucinate that a problem exists in the first place, all while using existing APIs. This might not be a major issue for tasks like programming where humans can relatively easily verify the output, but in other scientific fields this can be much more labor-intensive and practically infeasible to do, as this recent example[1] showcases. So hallucination is a problem that involves any fabricated output that isn't grounded in reality.

Which isn't to say that it is a universal problem. In some applications such as image, video or audio generation, especially in entertainment industries, hallucinations can be desirable. They're partly what we identify as "creativity", and the results can be fun and interesting. But in applications where facts and reality matter, they're a big problem.

[1]: https://news.ycombinator.com/item?id=44174965