| Challenge: | Existing frameworks fail to identify outliers that violate the exchangeability assumption, leading to unbounded miscoverage rates and unactionable prediction sets. |
| Approach: | They propose a method that implements significance tests to determine whether a given sample deviates from the uncertainty distribution of the calibration set. |
| Outcome: | The proposed approach facilitates rigorous management of miscoverage rates across single-domain and interdisciplinary contexts, and enhances the efficiency of predictions. |
Similar Papers
ConU: Conformal Uncertainty in Large Language Models with Correctness Coverage Guarantees (2024.findings-emnlp)
Copied to clipboard
Zhiyuan Wang, Jinhao Duan, Lu Cheng, Yue Zhang, Qingni Wang, Xiaoshuang Shi, Kaidi Xu, Heng Tao Shen, Xiaofeng Zhu
| Challenge: | Uncertainty quantification (UQ) in natural language generation tasks remains an open challenge . however, black-box uncertainty measures require investigating with the proliferation of LLMs served via APIs. |
| Approach: | They propose a conformal uncertainty measure and a method to transform heuristic uncertainty notions into rigorous prediction sets. |
| Outcome: | Empirical results show that the proposed method outperforms state-of-the-art methods and can provide reliable guarantees for open-ended NLG tasks. |
API Is Enough: Conformal Prediction for Large Language Models Without Logit-Access (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for quantifying uncertainty in large language models with black-box API access are limited due to the complex data distributions and inner model mechanism. |
| Approach: | They propose a conformal prediction method that minimizes the size of prediction sets and ensures a statistical guarantee of the user-defined coverage. |
| Outcome: | The proposed method outperforms existing methods on close-ended and open-ended questions. |
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. |
Exploring Predictive Uncertainty and Calibration in NLP: A Study on the Impact of Method & Data Scarcity (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Using low-resource languages, we assess the quality of uncertainty estimates from a wide array of approaches, but with more data. |
| Approach: | They train models on sub-sampled datasets in three different languages to assess the confidence of a neural classifier. |
| Outcome: | The proposed models train on sub-sampled datasets in three different languages and show that the quality of uncertainty estimates suffers with more data. |
Non-Exchangeable Conformal Language Generation with Nearest Neighbors (2024.findings-eacl)
Copied to clipboard
| Challenge: | Existing methods to evaluate reliability of generated text are lacking in natural language generation. |
| Approach: | They propose a non-exchangeable conformal prediction method that provides bounds on coverage . they validated their method with k-NN retrieval and show that it produces encouraging results . |
| Outcome: | The proposed method produces encouraging results in machine translation and language modeling tasks. |
Benchmarking and Improving LLM Robustness for Personalized Generation (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing evaluations focus on whether a model’s responses align with a user’s preferences, but factuality is an important yet overlooked dimension. |
| Approach: | They propose a scalable framework for evaluating robustness of large language models in personalization and a new dataset, PERGData. |
| Outcome: | The proposed framework improves robustness by 25% across models. |
Analyzing Uncertainty of LLM-as-a-Judge: Interval Evaluations with Conformal Prediction (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) are powerful automatic evaluators for natural language generation (NLG) tasks, but their uncertainty may limit their deployment in many applications. |
| Approach: | They propose a conformal prediction framework that provides a prediction interval with coverage guarantees and a midpoint-based score as a low-bias alternative to raw model score and weighted average. |
| Outcome: | The proposed framework provides a prediction interval with coverage guarantees and a midpoint-based score as a low-bias alternative to raw model score and weighted average. |
Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language Models (2024.acl-long)
Copied to clipboard
Jinhao Duan, Hao Cheng, Shiqi Wang, Alex Zavalny, Chenan Wang, Renjing Xu, Bhavya Kailkhura, Kaidi Xu
| Challenge: | Large Language Models (LLMs) show promising results in language generation but often “hallucinate”, making their outputs less reliable. |
| Approach: | They propose to shift attention to more relevant components at token- and sentence-levels for better UQ. |
| Outcome: | The proposed approach improves the performance of a range of popular “off-the-shelf” LLMs with model sizes extending up to 33B parameters. |
A Novel Metric for Measuring the Robustness of Large Language Models in Non-adversarial Scenarios (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Using large language models, we evaluated their robustness on multiple datasets. |
| Approach: | They propose a new metric for assessing model robustness by empirical evaluation of several models on multiple datasets. |
| Outcome: | The proposed metric is based on a set of datasets that are constructed by introducing naturally-occurring, non-malicious perturbations or by generating semantically equivalent paraphrases of input questions or statements. |
Robust Uncertainty Quantification for Self-Evolving Large Language Models via Continual Domain Pretraining (2026.findings-acl)
Copied to clipboard
| Challenge: | Conformal prediction (CP) has shown promise in offering correctness guarantees for LLMs, but it faces major challenges in continual domain pretraining (CDP). |
| Approach: | They propose an adaptive rejection and non-exchangeable CP framework that allows the LLM to selectively abstain from answering when its confidence or competence shifts significantly. |
| Outcome: | Experiments show that the proposed framework improves performance under continuous domain pretraining scenarios. |