Researchers have developed a system called PageRecall to measure how effectively question-answering models select relevant pages from research papers. The system found that evidence grounding is limited by retrieval rather than the model's reading capabilities. Specifically, the page selection model only identified the correct page about half the time, and when it failed to find the right page, it often did so silently by returning incorrect information or nothing at all. To address this, the researchers propose showing entire papers within the model's context, which improved gold-page recall to 100% for parsable papers. AI
IMPACT This research highlights limitations in current retrieval systems for grounded QA, suggesting a shift towards full-context processing for improved accuracy.
RANK_REASON The item describes a new research paper detailing a novel system and its findings. [lever_c_demoted from research: ic=1 ai=1.0]
- alphaXiv
- arXiv
- CatalyzeX Code Finder for Papers
- Connected Papers
- CORE Recommender
- DagsHub
- EMNLP 2026
- Gotit.pub
- GroundLM
- Hugging Face
- Influence Flower
- Litmaps
- LitTraceQA
- PageRecall
- ScienceCast
- scite Smart Citations
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