New Methods & Metrics for LFQA tasks
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Abstract (arXiv)
Long-form question answering (LFQA) tasks require retrieving the documents pertinent to a query, using them to form a paragraph-length answer. Despite considerable progress in LFQA modeling, fundamental issues impede its progress: i) train/validation/test dataset overlap, ii) absence of automatic metrics and iii) generated answers not being "grounded" in retrieved documents. This work addresses every one these critical bottlenecks, contributing natural language inference/generation (NLI/NLG) methods and metrics that make significant strides to their alleviation.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 16 of its 27 references that are in the map. First posted on arXiv 26 Dec 2021.
Builds on
16 of 27 references.
References · 16
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4 more
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Citing papers per year: 2024: 1; 2025: 1.
2 papers.