arXiv (OAI Expanded)
open access
Does Reasoning Improve Psychological Depth in Large Language Models? It Depends on Who's Judging
LLM-as-a-Judge evaluators are increasingly used to score open-ended generation, yet a judge's correlation with human ratings on its development set may not guarantee valid measurement when outputs are closely matched and human preferences are subjective. We study this failure mode through psychologi
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
- Do Reasoning Representations Help Humans Evaluate LLM Outputs?#986647
- Beyond Symmetric Agents: Cognitive Diversity and Multi-Agent Debate in Small Language Models#1130717
- Do Not Trust the Benchmark: Limitations of General LLM Rankings and a Case for Task-Specific Evaluation#1048945
- Small Reward Models via Backward Inference#798774
Record · ID 1083274
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