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Do Reasoning Representations Help Humans Evaluate LLM Outputs?

Paper recorded by Signals 4 on 2026-09-08 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-08 on arXiv · recorded by Signals 4 on 2026-09-09

Category: cs.LG · 机器学习 · first seen 2026-09-09

Abstract

Reasoning representations are increasingly used as explanations for large language model outputs. Yet they are typically evaluated with model-centric criteria, such as answer accuracy and faithfulness, leaving it unclear whether they help people evaluate model responses. In this work, we study reasoning representations as human-facing interfaces rather than proxies for model reasoning ability. We

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#102 most recent of 215 cs.LG papers we have recorded · ↑ newer: PlayTrain: An Efficient Reinforcement Learning Framework for LLM-Gener · ↓ older: Answer-Distribution Trajectories: A Stochastic-Dynamics View of LLM Re
Cite this page: Do Reasoning Representations Help Humans Evaluate LLM Outputs?: the #102 most recent of 215 cs.LG papers we have recorded (as of 2026-09-08). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/do-reasoning-representations-help-humans-evaluate-llm-outputs.html
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