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[2602.17431] Fine-Grained Uncertainty Quantification for Long-Form Language Model Outputs: A Comparative Study
Summary
This study presents a taxonomy for fine-grained uncertainty quantification in long-form language model outputs, highlighting effective methods and their comparative performance.
Why It Matters
As language models increasingly generate long-form content, understanding and quantifying uncertainty is crucial for improving factual accuracy and reliability. This research provides a structured approach to evaluate and enhance long-form outputs, addressing a significant gap in existing methodologies.
Key Takeaways
- Introduces a taxonomy for uncertainty quantification in long-form outputs.
- Finds that claim-response entailment outperforms complex scoring methods.
- Demonstrates that claim-level scoring is more effective than sentence-level scoring.
- Highlights the effectiveness of uncertainty-aware decoding for factual accuracy.
- Provides practical guidance for selecting components in uncertainty quantification.
Computer Science > Computation and Language arXiv:2602.17431 (cs) [Submitted on 19 Feb 2026] Title:Fine-Grained Uncertainty Quantification for Long-Form Language Model Outputs: A Comparative Study Authors:Dylan Bouchard, Mohit Singh Chauhan, Viren Bajaj, David Skarbrevik View a PDF of the paper titled Fine-Grained Uncertainty Quantification for Long-Form Language Model Outputs: A Comparative Study, by Dylan Bouchard and 3 other authors View PDF HTML (experimental) Abstract:Uncertainty quantification has emerged as an effective approach to closed-book hallucination detection for LLMs, but existing methods are largely designed for short-form outputs and do not generalize well to long-form generation. We introduce a taxonomy for fine-grained uncertainty quantification in long-form LLM outputs that distinguishes methods by design choices at three stages: response decomposition, unit-level scoring, and response-level aggregation. We formalize several families of consistency-based black-box scorers, providing generalizations and extensions of existing methods. In our experiments across multiple LLMs and datasets, we find 1) claim-response entailment consistently performs better or on par with more complex claim-level scorers, 2) claim-level scoring generally yields better results than sentence-level scoring, and 3) uncertainty-aware decoding is highly effective for improving the factuality of long-form outputs. Our framework clarifies relationships between prior methods, enables...
