The Anatomy of a Blind Spot: What an LLM Judge Must Be Shown, and How Much

Key points

• We treat a judge whose reports are acted on as a regulator, in the sense of cybernetics, and ask what it must be shown, how much, in what form, and what its reports must get right.1
• When a system revised its answers on feedback from a judge denied the material they had to respect, it ended below its starting point on instruction following and question answering.1
• On news summarisation, a correct reference summary beside the source undid most of the source's contribution, whereas on single-answer questions a reference helped.1
• About a tenth of the source did almost all of its work, and a small entailment graph matched an LLM judge at finding errors in grounded text.1

Sources (1)

• [1]The Anatomy of a Blind Spot: What an LLM Judge Must Be Shown, and How Much
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 11, 09:38 PM

  Evaluation of text generated by large language models (LLMs) is increasingly delegated to LLMs acting as judges, yet what such a judge must be shown remains unresolved.

  We treat a judge whose reports are acted on as a regulator, in the sense of cybernetics, and ask what it must be shown, how much, in what form, and what its reports must get right.

Extractive summary: sentences quoted from the sources.