This has some examples of mode collapse for LLM judges. There are ways to mitigate it, usually by choosing a model family for the judge that’s different to your main LLM. Though mode collapse is a broader concept, I don’t know the canonical source for it.
Thanks for this. I think that mode collapse in the case of LLM-judging-LLM is related to “LLM self-preference”. I don’t know if this paper still holds up because it’s quite old now for an LLM paper and I haven’t read any good follow-ups. Raises the question whether self-preference extends to semantic topics in output (code style/arguments/etc.).
Yeah, I can see why conditioning a judge on outputs produced by a similarly trained model would make its own output distribution more peaky. I remember mixed evidence on this self-preference point, or at least at the time when we were looking at it it was not the main failure mode. You can measure this in a toy setting where you vary the model family of your judge; it might even be enough to eyeball the outputs to see if same-family gives you worse judgements. But IME other things affect LLMJ output quality more (like managing context, rubric, output format).
This has some examples of mode collapse for LLM judges. There are ways to mitigate it, usually by choosing a model family for the judge that’s different to your main LLM. Though mode collapse is a broader concept, I don’t know the canonical source for it.
Thanks for this. I think that mode collapse in the case of LLM-judging-LLM is related to “LLM self-preference”. I don’t know if this paper still holds up because it’s quite old now for an LLM paper and I haven’t read any good follow-ups. Raises the question whether self-preference extends to semantic topics in output (code style/arguments/etc.).
Yeah, I can see why conditioning a judge on outputs produced by a similarly trained model would make its own output distribution more peaky. I remember mixed evidence on this self-preference point, or at least at the time when we were looking at it it was not the main failure mode. You can measure this in a toy setting where you vary the model family of your judge; it might even be enough to eyeball the outputs to see if same-family gives you worse judgements. But IME other things affect LLMJ output quality more (like managing context, rubric, output format).