arXiv (All)
open access
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 reaso
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Choices Speak Louder than Questions#1131383
- Knowing When to Critique: Task-Adaptive Metacognitive Regulation for Reliable LLM Reasoning#1118403
- Answer-Distribution Trajectories: A Stochastic-Dynamics View of LLM Reasoning#668034
- Does Reasoning Improve Psychological Depth in Large Language Models? It Depends on Who's Judging#1083274
- The Lifecycle of LLM-as-a-Judge for Large-Scale Recommendation Explanations#819452
Record · ID 986647
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive