Dialog-act and maxim-aware prompting improves LLM judge accuracy on multi-turn preference data by up to 8 points, with further gains from jury-style voting.
Report from the NSF Future Directions Workshop on Automatic Evaluation of Dialog: Research Directions and Challenges
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abstract
This is a report on the NSF Future Directions Workshop on Automatic Evaluation of Dialog. The workshop explored the current state of the art along with its limitations and suggested promising directions for future work in this important and very rapidly changing area of research.
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cs.CL 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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Amulet: Putting Complex Multi-Turn Conversations on the Stand with LLM Juries
Dialog-act and maxim-aware prompting improves LLM judge accuracy on multi-turn preference data by up to 8 points, with further gains from jury-style voting.