arXiv (All)
open access
Alleviating Hallucination in Reasoning Tasks with Training-Free Uncertainty-Guided Steering
Recent work on hallucination detection in large language models has shown that, for a fixed pre-trained model and reasoning task, it is possible to estimate the model's confidence in the correctness of its outputs. Such uncertainty estimates have primarily been used to improve truthfulness by detect
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
- SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models#16586
- Is it Cake or is it AI? A Systematic Review of Human Uncertainty in Distinguishing Generative Artificial Intelligence Content#16470
- Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models#177037
- HalluPrism: When Multimodal Uncertainty Should Diagnose, Not Decide#794011
- HalluPrism: When Multimodal Uncertainty Should Diagnose, Not Decide#789992
Record · ID 1143929
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