RELIANCE is a new expert-annotated dataset of TikTok reproductive health content paired with LLM fact-checking evaluations showing 60% accuracy in sampled videos and a 15% gap between claim and full-content assessment.
Risk of misinformation in retrieval-augmented generation with large language models
6 Pith papers cite this work, alongside 20 external citations. Polarity classification is still indexing.
6
Pith papers citing it
20
external citations · external index
representative citing papers
DeLask dynamically skips hallucination-prone decoder layers in LLMs by measuring gradient driftance via cosine similarity and partially aggregating states instead of full skipping.
CUE-R uses REMOVE, REPLACE, and DUPLICATE interventions on individual evidence items to quantify their per-item utility in RAG along correctness, grounding faithfulness, and confidence axes.
C3RL is a new RL algorithm combining correctness, calibration, and reference accuracy rewards to improve LLM confidence calibration, enabling CAS to outperform majority voting with up to 12.33x lower inference cost.
LLMs show improved accuracy on gastroenterology questions but remain overconfident in self-reported certainty across commercial, open-source, and quantized variants.
A survey classifying hallucination phenomena specific to large foundation models, establishing evaluation criteria, examining mitigation strategies, and discussing future directions.
citing papers explorer
-
RELIANCE: Curating and Evaluating Reproductive Health Information on Social Media
RELIANCE is a new expert-annotated dataset of TikTok reproductive health content paired with LLM fact-checking evaluations showing 60% accuracy in sampled videos and a 15% gap between claim and full-content assessment.
-
Mitigating Hallucinations in Large Language Models Via Decoder Layer Skipping
DeLask dynamically skips hallucination-prone decoder layers in LLMs by measuring gradient driftance via cosine similarity and partially aggregating states instead of full skipping.
-
CUE-R: Beyond the Final Answer in Retrieval-Augmented Generation
CUE-R uses REMOVE, REPLACE, and DUPLICATE interventions on individual evidence items to quantify their per-item utility in RAG along correctness, grounding faithfulness, and confidence axes.
-
Scaling with Confidence: Calibrating Confidence of LLMs for Adaptive Test Time Scaling
C3RL is a new RL algorithm combining correctness, calibration, and reference accuracy rewards to improve LLM confidence calibration, enabling CAS to outperform majority voting with up to 12.33x lower inference cost.
-
Self-Reported Confidence of Large Language Models in Gastroenterology: Analysis of Commercial, Open-Source, and Quantized Models
LLMs show improved accuracy on gastroenterology questions but remain overconfident in self-reported certainty across commercial, open-source, and quantized variants.
-
A Survey of Hallucination in Large Foundation Models
A survey classifying hallucination phenomena specific to large foundation models, establishing evaluation criteria, examining mitigation strategies, and discussing future directions.