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

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

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arXiv Computation and Language
5d ago

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
arXiv AI
5d ago

From Answers to Interpretations: Rethinking Ambiguity-Induced Aleatoric Uncertainty Estimation in LLMs

The paper challenges the common practice of estimating aleatoric uncertainty in large language models (LLMs) by generating multiple clarified inputs and comparing the resulting answers. It argues that answers are unnecessary, costly, and can introduce epistemic leakage, proposing instead a clarification-only method that directly assesses ambiguity from the space of plausible interpretations. Experiments on three benchmarks show the new approach improves AUROC, reduces computational cost, and yields uncertainty estimates less correlated with epistemic uncertainty.

By Omer Nahum, Niv Nayman, Jonathan Fhima, Alon Zolfi, Jeremy Levy, Shai Mazor, Paolo Favaro
arXiv Machine Learning
Jun 17

From Drift to Coherence: Stabilizing Beliefs in LLMs

arXiv:2606. 17832v1 Announce Type: new Abstract: Large language models (LLMs) are often hypothesized to perform implicit Bayesian inference, yet a key coherence condition, the martingale property of predictive beliefs, has been shown to fail in controlled synthetic in-context learning settings.

By SongEun Kim, Seungyoo Lee, Edwin Fong, Hyungi Lee, Juho Lee