What Makes a Good Query? Measuring the Impact of Human-Confusing Linguistic Features on LLM Performance (2026.findings-eacl)
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| Challenge: | Large Language Models (LLMs) are often treated as defects of the model or its decoding strategy. |
| Approach: | They construct a 22-dimension query feature vector covering clause complexity, lexical rarity, anaphora, negation, answerability, and intention grounding. |
| Outcome: | The proposed model covers clause complexity, lexical rarity, anaphora, negation, answerability, and intention grounding, all known to affect human comprehension. |
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| Challenge: | Recent studies on hallucination in large language models (LLMs) have been actively progressing in natural language processing. |
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| Challenge: | Recent advances in large language models have improved summarization, but they still face a challenge of hallucination. |
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The Illusion of Progress: Re-evaluating Hallucination Detection in LLMs (2025.emnlp-main)
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