IUQ: Interrogative Uncertainty Quantification for Long-Form Large Language Model Generation

• URL: http://arxiv.org/abs/2604.15109v2
• Date: Sun, 19 Apr 2026 16:30:03 GMT
• Title: IUQ: Interrogative Uncertainty Quantification for Long-Form Large Language Model Generation
• Abstract summary: This paper introduces Interrogative Uncertainty Quantification (IUQ), a novel framework that leverages inter-sample consistency and intra-sample faithfulness to quantify the uncertainty.<n> Experimental results across diverse model families and model sizes demonstrate the superior performance of IUQ over two widely used long-form generation datasets.
• Score: 25.78840651769687
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite the rapid advancement of Large Language Models (LLMs), uncertainty quantification in LLM generation is a persistent challenge. Although recent approaches have achieved strong performance by restricting LLMs to produce short or constrained answer sets, many real-world applications require long-form and free-form text generation. A key difficulty in this setting is that LLMs often produce responses that are semantically coherent yet factually inaccurate, while the underlying semantics are multifaceted and the linguistic structure is complex. To tackle this challenge, this paper introduces Interrogative Uncertainty Quantification (IUQ), a novel framework that leverages inter-sample consistency and intra-sample faithfulness to quantify the uncertainty in long-form LLM outputs. By utilizing an interrogate-then-respond paradigm, our method provides reliable measures of claim-level uncertainty and the model's faithfulness. Experimental results across diverse model families and model sizes demonstrate the superior performance of IUQ over two widely used long-form generation datasets. The code is available at https://github.com/louisfanhz/IUQ.

Related papers

• Quantization Meets dLLMs: A Systematic Study of Post-training Quantization for Diffusion LLMs [78.09559830840595]
  We present the first systematic study on quantizing diffusion-based language models.<n>We identify the presence of activation outliers, characterized by abnormally large activation values.<n>We implement state-of-the-art PTQ methods and conduct a comprehensive evaluation.
  arXiv  Detail & Related papers  (2025-08-20T17:59:51Z)
• UNCLE: Benchmarking Uncertainty Expressions in Long-Form Generation [69.13972417517233]
  Large Language Models (LLMs) are prone to hallucination, particularly in long-form generations.<n>We introduce UNCLE, a benchmark designed to evaluate uncertainty expression in both long- and short-form question answering (QA)<n>Our dataset is the first to directly link short- and long-form QA through aligned questions and gold-standard answers.
  arXiv  Detail & Related papers  (2025-05-22T17:16:08Z)
• Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning [9.75748930802634]
  We study the power of in-context-learning to improve machine learning performance.<n>We consider an extremely simple domain: next token prediction on simple language tasks.<n>We find that LLMs uniformly underperform n-gram models on this task.
  arXiv  Detail & Related papers  (2025-01-06T07:57:51Z)
• LoGU: Long-form Generation with Uncertainty Expressions [49.76417603761989]
  We introduce the task of Long-form Generation with Uncertainty(LoGU)<n>We identify two key challenges: Uncertainty Suppression and Uncertainty Misalignment.<n>Our framework adopts a divide-and-conquer strategy, refining uncertainty based on atomic claims.<n>Experiments on three long-form instruction following datasets show that our method significantly improves accuracy, reduces hallucinations, and maintains the comprehensiveness of responses.
  arXiv  Detail & Related papers  (2024-10-18T09:15:35Z)
• FltLM: An Intergrated Long-Context Large Language Model for Effective Context Filtering and Understanding [32.197113821638936]
  We propose a novel integrated Long-Context Large Language Model (FltLM) FltLM incorporates a context filter with a soft mask mechanism, identifying and dynamically excluding irrelevant content to concentrate on pertinent information. Experimental results demonstrate that FltLM significantly outperforms supervised fine-tuning and retrieval-based methods in complex QA scenarios.
  arXiv  Detail & Related papers  (2024-10-09T13:47:50Z)
• Unconditional Truthfulness: Learning Unconditional Uncertainty of Large Language Models [104.55763564037831]
  We train a regression model that leverages attention maps, probabilities on the current generation step, and recurrently computed uncertainty scores from previously generated tokens.<n>Our evaluation shows that the proposed method is highly effective for selective generation, achieving substantial improvements over rivaling unsupervised and supervised approaches.
  arXiv  Detail & Related papers  (2024-08-20T09:42:26Z)
• LUQ: Long-text Uncertainty Quantification for LLMs [29.987010627250527]
  Large Language Models (LLMs) are prone to generate nonfactual content. Uncertainty Quantification (UQ) is pivotal in enhancing our understanding of a model's confidence on its generation. We propose textscLuq-Ensemble, a method that ensembles responses from multiple models and selects the response with the lowest uncertainty.
  arXiv  Detail & Related papers  (2024-03-29T16:49:24Z)
• Are Large Language Models Really Robust to Word-Level Perturbations? [68.60618778027694]
  We propose a novel rational evaluation approach that leverages pre-trained reward models as diagnostic tools. Longer conversations manifest the comprehensive grasp of language models in terms of their proficiency in understanding questions. Our results demonstrate that LLMs frequently exhibit vulnerability to word-level perturbations that are commonplace in daily language usage.
  arXiv  Detail & Related papers  (2023-09-20T09:23:46Z)
• Simultaneous Machine Translation with Large Language Models [51.470478122113356]
  We investigate the possibility of applying Large Language Models to SimulMT tasks. We conducted experiments using the textttLlama2-7b-chat model on nine different languages from the MUST-C dataset. The results show that LLM outperforms dedicated MT models in terms of BLEU and LAAL metrics.
  arXiv  Detail & Related papers  (2023-09-13T04:06:47Z)
• LM-Infinite: Zero-Shot Extreme Length Generalization for Large Language Models [83.98062659664785]
  Large language models (LLMs) typically train on short text segments (e.g., 4K tokens) due to the quadratic complexity of their Transformer architectures. This work identifies three major factors contributing to this length generalization failure. We propose LM-Infinite, a simple and effective method for enhancing LLMs' capabilities of handling long contexts.
  arXiv  Detail & Related papers  (2023-08-30T16:47:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.