Reading Between the Lines: Can LLMs Discover the Question Behind the Text?
Mirrored from arXiv — NLP / Computation & Language for archival readability. Support the source by reading on the original site.
Computer Science > Computation and Language
arXiv:2609.19070 (cs)
[Submitted on 22 Jul 2026]
Title:Reading Between the Lines: Can LLMs Discover the Question Behind the Text?
View a PDF of the paper titled Reading Between the Lines: Can LLMs Discover the Question Behind the Text?, by Claudiu Creanga and 1 other authors
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Abstract:This paper introduces ``question archaeology'', a specific evaluation task focused on inferring the single, authentic "genesis question" that motivated the creation of a complete text. Distinct from question generation, which targets any plausible question, or discourse frameworks that model utterance-level acts, our task assesses a model's grasp of authorial intent. We present a new dataset of commissioned texts paired with their original research questions and plausible distractors. Our evaluation of both proprietary models, like Gemini Flash and Pro, as well as open source models like Mistral and Qwen, reveals significant progress in this task, with the newer versions outperforming the earlier ones, while BERT-based models performed poorly. Notably, our findings indicate that current LLMs surpass human performance on this task, suggesting advanced understanding of authorial intent. This capability has important implications for AI's role in tasks requiring nuanced interpretation of human communication. Our work thus provides a new framework and a challenging benchmark for future models.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2609.19070 [cs.CL] |
| (or arXiv:2609.19070v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.19070
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Reading Between the Lines: Can LLMs Discover the Question Behind the Text?, by Claudiu Creanga and 1 other authors
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