Browse State-of-the-Art › Fact Selection
Fact Selection
7 papers with code · 1 benchmark · 1 dataset archive 2025-07-28
A task where an agent should select at most two sentences from the paper as argumentative facts.
Description from the archive archive 2025-07-28.
Benchmarks archive 2025-07-28
1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.
| Dataset | Best model (first row in archive order) | Paper | Code | Syntology | Compare |
|---|---|---|---|---|---|
| ArgSciChat (4 rows) | TF-IDF | ArgSciChat: A Dataset for Argumentative Dialogues on Scientific Papers | code | — | Compare |
Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.
Libraries
Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.
Datasets archive 2025-07-28
1 dataset whose archive record lists this task, ordered by the archive's paper count.
Subtasks archive 2025-07-28
No subtask under this task in the archive's task tree.
Most implemented papers archive 2025-07-28
7 shown of 7 papers with code (14 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
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14 Feb 2022 2 repositories listedThe applications of conversational agents for scientific disciplines (as expert domains) are understudied due to the lack of dialogue data to train such agents.
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4 Jul 2024 1 repository listedGenerating diverse, high-quality outputs from language models is crucial for applications in education and content creation.
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25 May 2023 1 repository listedUFO turns LLMs into knowledge sources and produces relevant facts (knowledge statements) for the given question.
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5 May 2023 1 repository listedExpository documents are vital resources for conveying complex information to readers.
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15 Feb 2023 1 repository listedWe introduce a new benchmark, COVID-VTS, for fact-checking multi-modal information involving short-duration videos with COVID19- focused information from both the real world and machine generation.
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1 May 2022 1 repository listedA crucial challenge of such systems is to select facts from a knowledge graph pertinent to the dialogue context for response generation.
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19 Mar 2022 1 repository listed Syntology ran 1 of 9 samples · 8 unverifiedRecent works show that such models can also produce the reasoning steps (i.
Syntology lines on 1 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections