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).
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).