Browse State-of-the-Art › Implicit Relations
Implicit Relations
20 papers with code · 1 benchmark · 1 dataset 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 |
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| BIG-bench (2 rows) | Chinchilla-70B (few-shot, k=5) | Training Compute-Optimal Large Language Models | code | Syntology ran 8 of 11 samples · 3 unverified | 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.
Parent tasks archive 2025-07-28
Most implemented papers archive 2025-07-28
20 shown of 20 papers with code (67 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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8 Dec 2021 3 repositories listedLanguage modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.
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29 Mar 2022 2 repositories listed Syntology ran 8 of 11 samples · 3 unverified · 4 pointer-only (licence)We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget.
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17 Sep 2018 2 repositories listed Syntology ran 0 of 7 samples · 7 unverifiedWe instead focus on a more challenging multi-hop generative task (NarrativeQA), which requires the model to reason, gather, and synthesize disjoint pieces of information within the context to generate an answer.
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18 May 2025 1 repository listedThe increasing context length of modern language models has created a need for evaluating their ability to retrieve and process information across extensive documents.
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6 Jul 2024 1 repository listedThe visual question generation (VQG) task aims to generate human-like questions from an image and potentially other side information (e.
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28 Mar 2024 1 repository listed Syntology ran 1 of 1 samples · 0 unverifiedMagicLens is built on a key novel insight: image pairs that naturally occur on the same web pages contain a wide range of implicit relations (e.
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19 Aug 2023 1 repository listedRecently, the wanton dissemination of fake news on social media has adversely affected our lives, rendering automatic fake news detection a pressing issue.
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23 May 2023 1 repository listedInternet links enable users to deepen their understanding of a topic by providing convenient access to related information.
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17 Oct 2022 1 repository listedWe study automatic Contract Clause Extraction (CCE) by modeling implicit relations in legal contracts.
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28 Apr 2022 1 repository listedA prominent challenge for modern language understanding systems is the ability to answer implicit reasoning questions, where the required reasoning steps for answering the question are not mentioned in the text…
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17 Mar 2022 1 repository listed Syntology ran 2 of 10 samples · 8 unverifiedKnowledge-based visual question answering requires the ability of associating external knowledge for open-ended cross-modal scene understanding.
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11 Jan 2022 1 repository listedStock Movement Prediction (SMP) aims at predicting listed companies' stock future price trend, which is a challenging task due to the volatile nature of financial markets.
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25 Oct 2021 1 repository listedKnowledge Graph Question Answering (KGQA) aims to answer user-questions from a knowledge graph (KG) by identifying the reasoning relations between topic entity and answer.
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17 Sep 2021 1 repository listedExploiting relations among 2D joints plays a crucial role yet remains semi-developed in 2D-to-3D pose estimation.
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11 Jun 2021 1 repository listed Syntology ran 4 of 4 samples · 0 unverifiedTo gain insights into the reasoning process of a generation model, we propose a new method, local explanation of response generation (LERG) that regards the explanations as the mutual interaction of segments in input…
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22 Jul 2019 1 repository listedExisting techniques from Information Retrieval and Natural Language Processing attempt to identify the hidden or unpublished connections between information concepts within published literature, however, these…
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9 Jul 2019 1 repository listed Syntology ran 0 of 1 samples · 1 unverifiedDiscourse relation identification has been an active area of research for many years, and the challenge of identifying implicit relations remains largely an unsolved task, especially in the context of an open-domain…
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1 Jun 2019 1 repository listedShort texts challenge NLP tasks such as named entity recognition, disambiguation, linking and relation inference because they do not provide sufficient context or are partially malformed (e.
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29 Mar 2019 1 repository listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)In order to answer semantically-complicated questions about an image, a Visual Question Answering (VQA) model needs to fully understand the visual scene in the image, especially the interactive dynamics between…
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2 Nov 2018 1 repository listedTo capture additional context, PathNet also composes the passage representations along each path to compute a passage-based representation.
Syntology lines on 7 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