arXiv (All)
open access
Detecting Errors in Large Language Model Responses: The Incremental Predictive Value of Hidden States
Although large language models support a wide range of tasks, hallucinations remain a major obstacle to user trust. When a model generates an answer, both its internal processing and final output vary with the question. Internal states are often expected to offer richer signals for hallucination det
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
- Revealing Epistemic Uncertainty in MLLMs via Causal-Invariant Masking#1187565
- U-Space: Uncovering When and Why Uncertainty Arises in Language Models#1233126
- Measuring Mechanistic Uncertainty in Large Language Models via Causal Robustness#1386608
- Prioritizing Repeated LLM Evaluation for Hidden Failure Discovery#1116878
- SynthSentry: Detecting Synthetic Data Contamination in Language Model Training Data#1000309
Record · ID 1402843
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