arXiv AI
Sep 7

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 AI
5d ago

A Small Model Asks Better: Fine-Tuning Query Clarification for Retrieval-Augmented Generation

The paper introduces ClariEdge, a dataset of about 3,900 examples that capture ambiguous or ill‑formed user queries in retrieval‑augmented generation contexts, including negative cases that do not need clarification. A lightweight LoRA fine‑tuned model achieves 96.6% classification accuracy, outperforming larger prompted baselines in both classification and clarification generation. The study highlights the importance of hard negatives for distinguishing truly ambiguous queries and demonstrates that the same model can be applied before and after retrieval in a RAG pipeline.

By Lihong He, Lucian Popa, Marina Danilevsky