arXiv AI August 18, 2026 By Haotian Zong, Binze Li, Yufei Long, Sinyin Chang, Jialong Wu, Gillian K. Hadfield

I-CALM: Incentivizing Confidence-Aware Abstention for LLM Selective Answering

arXiv:2604. 03904v2 Announce Type: replace-cross Abstract: Large language models (LLMs) often produce confident but incorrect answers, in part because standard evaluation incentives reward guessing over expressing uncertainty.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jun 10

TruthRL: Incentivizing Truthful LLMs via Reinforcement Learning

arXiv:2509. 25760v2 Announce Type: replace-cross Abstract: While large language models (LLMs) have demonstrated strong performance on factoid question answering, they are still prone to hallucination and untruthful responses, particularly when tasks demand information outside their parametric knowledge.

By Zhepei Wei, Xiao Yang, Kai Sun, Jiaqi Wang, Rulin Shao, Jingxiang Chen, Mohammad Kachuee, Teja Gollapudi, Yiwei Liao, Nicolas Scheffer, Rakesh Wanga, Anuj Kumar, Yu Meng, Wen-tau Yih, Xin Luna Dong

llms nlp reinforcement-learning benchmarks safety

arXiv AI

Sep 10

Clarify, Abstain or Answer? Strategising in Conversation with Belief-Augmented Generation

arXiv:2605.25831v2 Announce Type: replace-cross Abstract: Large language models (LLMs) define a distribution over text, which can be viewed as a probabilistic representation of uncertainty: sampling...

By Joris Baan, Wilker Aziz, Barbara Plank, Raquel Fern\'andez

llms nlp

arXiv Machine Learning

Jun 2

Towards Lightweight Reliability: Using Soft Prompts for Hallucination Mitigation in Large Language Models

arXiv:2606. 00919v1 Announce Type: cross Abstract: Large language models (LLMs) have seen widespread adoption across various domains, yet their reliability is frequently undermined by hallucinations - responses that are plausible-sounding but factually incorrect.

By S M Tahmid Siddiqui, Akib Jawad Ononto, Anoop Singhal, Latifur Khan

llms safety

arXiv Computation and Language

Sep 7

ConfRAG: Confidence-Guided Retrieval-Augmenting Generation

ConfRAG introduces a confidence-guided approach to reduce hallucinations in large language models and selectively trigger Retrieval-Augmented Generation (RAG) only when the model is uncertain. The ConfQA fine‑tuning strategy trains the model to answer correctly or respond with "I am unsure," achieving a drop in hallucination rates from 20‑40% to below 5% across factuality benchmarks. Building on ConfQA, ConfRAG limits external retrievals by more than 30% while maintaining over 95% accuracy in ideal scenarios.

By Yin Huang, Yifan Ethan Xu, Kai Sun, Vera Yan, Alicia Sun, Haidar Khan, Jimmy Nguyen, Jingxiang Chen, Mohammad Kachuee, Zhaojiang Lin, Yue Liu, Aaron Colak, Anuj Kumar, Wen-tau Yih, Xin Luna Dong

llms rag fine-tuning benchmarks safety

arXiv AI

Jul 29

Beyond Self-Knowledge: Propagating Uncertainty Across Reasoning and Retrieval in LLMs

arXiv:2607. 25600v1 Announce Type: cross Abstract: Retrieval-augmented generation improves knowledge-intensive question answering, but indiscriminate retrieval can introduce irrelevant evidence and unnecessary computation.

By Chandan Kumar Sah, Xiaoli Lian, Li Zhang

llms rag nlp benchmarks

arXiv Computation and Language

Aug 25

Claim-Level Confidence Calibration for Reliable Decision Making with Large Language Models

arXiv:2608.22483v1 Announce Type: new Abstract: Large Language Models (LLMs) increasingly support decision-making in high-stakes domains, but they often hallucinate and express confidence that is mis...

By Toghrul Abbasli, Kentaroh Toyoda, Yuan Wang, Li Chen

llms fine-tuning safety

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Related stories

TruthRL: Incentivizing Truthful LLMs via Reinforcement Learning

Clarify, Abstain or Answer? Strategising in Conversation with Belief-Augmented Generation

Towards Lightweight Reliability: Using Soft Prompts for Hallucination Mitigation in Large Language Models

ConfRAG: Confidence-Guided Retrieval-Augmenting Generation

Beyond Self-Knowledge: Propagating Uncertainty Across Reasoning and Retrieval in LLMs

Claim-Level Confidence Calibration for Reliable Decision Making with Large Language Models