Demystifying Uncertainty in LLMs: Active Calibration between Concepts and Human Evaluations (2026.acl-long)
Copied to clipboard
| Challenge: | Existing static strategies for mitigating hallucinations do not explicitly model the information gain from interacting with the external environment. |
| Approach: | They propose a calibration-driven interactive learning strategy that selects clarification queries by optimizing calibration error. |
| Outcome: | The proposed method provides theoretical guarantees and empirical gains for reliability. |
Similar Papers
Detecting LLM Hallucination Through Layer-wise Information Deficiency: Analysis of Ambiguous Prompts and Unanswerable Questions (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) often generate confident yet inaccurate responses, introducing significant risks for deployment in safety-critical domains. |
| Approach: | They propose a method to detect model hallucination by systematic analysis of information flow across model layers. |
| Outcome: | The proposed approach improves model reliability by immediately integrating with universal LLMs without additional training or architectural modifications. |
Can LLMs Learn Uncertainty on Their Own? Expressing Uncertainty Effectively in A Self-Training Manner (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) exhibit excessive, random, and uninformative uncertainty rendering them unsuitable for decision-making in human-computer interactions. |
| Approach: | They propose an uncertainty-aware instruction tuning method that aligns LLMs’ perception with the probabilistic uncertainty of the generation. |
| Outcome: | The proposed method improves LLMs' performance by 45.2%, with reasonably good out-of-domain generalization capabilities. |
Uncertainty Quantification for Large Language Models (2025.acl-tutorials)
Copied to clipboard
| Challenge: | Large language models (LLMs) produce hallucinations, which undermine user trust and reliability. |
| Approach: | This tutorial offers the first systematic introduction to uncertainty quantification (UQ) for LLMs in text generation tasks. |
| Outcome: | The proposed framework provides tools for communicating the reliability of a model answer. |
Uncertainty in Language Models: Assessment through Rank-Calibration (2024.emnlp-main)
Copied to clipboard
Xinmeng Huang, Shuo Li, Mengxin Yu, Matteo Sesia, Hamed Hassani, Insup Lee, Osbert Bastani, Edgar Dobriban
| Challenge: | Language Models (LMs) have shown promising performance in natural language generation . however, it is crucial to correctly quantify their level of uncertainty in responding to inputs. |
| Approach: | They propose a framework to quantify uncertainty and confidence for Large Language Models . they use a Rank-calibration framework to measure uncertainty and confident responses . |
| Outcome: | The proposed framework assesses uncertainty and confidence measures for LMs. |
Calibration Across Layers: Understanding Calibration Evolution in LLMs (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have demonstrated inherent calibration capabilities, where predicted probabilities align well with correctness . previous studies have linked this behavior to specific components in the final layer, such as entropy neurons and the unembedding matrix’s null space. |
| Approach: | They propose to examine how calibration evolves throughout the network's depth. |
| Outcome: | The proposed calibration direction improves calibration metrics without harming accuracy. |
A Survey of Confidence Estimation and Calibration in Large Language Models (2024.naacl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) have demonstrated impressive capabilities across a wide range of tasks in various domains, but they can be unreliable due to factual errors in their generations. |
| Approach: | They summarize recent advances in LLM confidence estimation and calibration and outline their main lessons learned. |
| Outcome: | The proposed methods can be used to assess the reliability of models and to calibrate them across tasks. |
Beyond "I Don’t Know": Evaluating LLM Self-Awareness in Discriminating Data and Model Uncertainty (2026.acl-long)
Copied to clipboard
| Challenge: | Prior studies treat refusal as a generic "I don't know" lack of distinction limits downstream action decisions like requesting clarification or invoking external tools. |
| Approach: | They propose a benchmark to evaluate explicit uncertainty attribution in large language models. |
| Outcome: | The proposed method improves uncertainty attribution while preserving answer accuracy. |
Uncertainty Quantification for In-Context Learning of Large Language Models (2024.naacl-long)
Copied to clipboard
Chen Ling, Xujiang Zhao, Xuchao Zhang, Wei Cheng, Yanchi Liu, Yiyou Sun, Mika Oishi, Takao Osaki, Katsushi Matsuda, Jie Ji, Guangji Bai, Liang Zhao, Haifeng Chen
| Challenge: | Existing studies on in-context learning have focused on quantifying the uncertainty associated with the model's response, but they neglect the complexity of the LLM and the uniqueness of in-constitut learning. |
| Approach: | They propose a method to quantify the uncertainty associated with in-context learning and propose corresponding estimation method to quantify both types of uncertainties. |
| Outcome: | The proposed method offers an unsupervised way to understand the prediction of in-context learning in a plug-and-play fashion. |
Do We Know What LLMs Don’t Know? A Study of Consistency in Knowledge Probing (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for probing knowledge gaps in large language models are inconsistent and inconsistent. |
| Approach: | They propose a process based on input variations and quantitative metrics to evaluate probing methods that are inconsistent on knowledge gaps. |
| Outcome: | The proposed process exposes two dimensions of inconsistency in knowledge gap probing. |
A Survey of Uncertainty Estimation Methods on Large Language Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) have demonstrated remarkable capabilities but could produce biased, hallucinated, or non-factual responses. |
| Approach: | They propose to conduct extensive experimental evaluations of LLM uncertainty estimation methods . large language models have demonstrated remarkable capabilities across tasks . |
| Outcome: | The proposed method could produce biased, hallucinated, or non-factual responses . a lack of comprehensive surveys on LLM uncertainty estimation is a problem . |