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New MUDDLE benchmark tests LLM document understanding against distractors

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced MUDDLE, a new benchmark designed to evaluate document question-answering systems by separating the effects of document length and distracting information. The benchmark uses 270 human-annotated questions, each tested under five conditions: source document alone, source with similar distractors, and source with random distractors. Initial tests on GPT-5 mini indicate that topically similar distractors have a greater negative impact on accuracy than random distractors of equivalent length. AI

IMPACT This benchmark could lead to more robust document QA systems by highlighting the impact of distracting information.

RANK_REASON The cluster describes a new academic benchmark for evaluating LLM capabilities. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New MUDDLE benchmark tests LLM document understanding against distractors

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The cluster describes a new academic benchmark for evaluating LLM capabilities. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jason Luo, Saibilila Abudukelimu, Judy Song, Andrew Feng, Shivank Garg, Vasu Sharma, Kevin Zhu ·

    MUDDLE: Measuring Understanding of Documents under Distractor and Length Effects

    arXiv:2608.29477v1 Announce Type: cross Abstract: Document question-answering systems increasingly answer questions over collections of retrieved documents rather than one clean source, so robustness to distracting context matters as much as reading ability. When such systems fai…

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