SOURCE-LINKED INTELLIGENCE
Do Reasoning Representations Help Humans Evaluate LLM Outputs?
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 conduct a controlled human study of six reasoning formats across tasks of varying complexity, supported by a web-based framework that randomizes task domains, problem instances, and representation ord
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-08T17:03:57.000Z
First collected: 2026-09-20T20:02:11.508Z. This is not the publication date.