跳到主要内容
AI News HubLIVE
来源内容 · 翻译待补全2 分钟阅读

待翻译:Think Before You Link: Rarity, Reasoning, and Retrieval in Multilingual Entity Linking

文章摘要

AI 服务暂时不可用,以下为来源摘要,待恢复后补全翻译:arXiv:2609.10745v1 Announce Type: new Abstract: Multimodal entity linking grounds entity mentions in text and images to knowledge-base entries. These systems degrade on rare entities, but prior work measures rarity primarily through popularity-based metrics such as pageviews. We broaden this view using knowledge-graph structural metrics that capture how well an entity is documented and connected. These metrics identify many rare entities that popularity metrics miss. Across the resulting rare-entity slices, state-of-the-art accuracy drops by 15.4-39.9%, showing that different rarity definitions expose different failure modes. To address these failures, we introduce a simple, training-free framework in which a reasoning-capable vision-language model iteratively…

来源arXiv Computational Linguistics作者: Parinthapat Pengpun, Simran Khanuja, Graham Neubig
待翻译:Think Before You Link: Rarity, Reasoning, and Retrieval in Multilingual Entity Linking
报告错误

纠错通道尚未开通,可先复制下方文章信息留存。

查看更正说明
直接读正文

AI 服务暂时不可用,以下为来源正文,待恢复后补全翻译。

[Submitted on 9 Sep 2026] Title:Think Before You Link: Rarity, Reasoning, and Retrieval in Multilingual Entity Linking View a PDF of the paper titled Think Before You Link: Rarity, Reasoning, and Retrieval in Multilingual Entity Linking, by Parinthapat Pengpun and 2 other authors View PDF HTML (experimental) Abstract:Multimodal entity linking grounds entity mentions in text and images to knowledge-base entries. These systems degrade on rare entities, but prior work measures rarity primarily through popularity-based metrics such as pageviews. We broaden this view using knowledge-graph structural metrics that capture how well an entity is documented and connected. These metrics identify many rare entities that popularity metrics miss. Across the resulting rare-entity slices, state-of-the-art accuracy drops by 15.4-39.9%, showing that different rarity definitions expose different failure modes. To address these failures, we introduce a simple, training-free framework in which a reasoning-capable vision-language model iteratively searches and reasons over Wikipedia, gathering evidence dynamically. Controlled experiments show that reasoning and retrieval are complementary. Reasoning alone does not significantly improve accuracy on rare entities. Retrieval without reasoning improves rare-entity accuracy but can hurt overall accuracy. Their combination performs best. On MERLIN, a multilingual multimodal entity linking benchmark over five languages (Hindi, Indonesian, Japanese, Tamil, Vietnamese), our best system improves over the state of the art by 6.9% overall and by up to 23.3% on rare-entity slices. We release MERLIN-Rare, rare-entity test slices for targeted evaluation, with our framework. Comments: Accepted to EMNLP 2026 Main Conference Subjects: Computation and Language (cs.CL) Cite as: arXiv:2609.10745 [cs.CL] (or arXiv:2609.10745v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2609.10745 arXiv-issued DOI via DataCite (pending registration) Submission history From: Parinthapat Pengpun [view email] [v1] Wed, 9 Sep 2026 18:41:35 UTC (341 KB) Full-text links: Access Paper: View a PDF of the paper titled Think Before You Link: Rarity, Reasoning, and Retrieval in Multilingual Entity Linking, by Parinthapat Pengpun and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL new | recent | 2026-09 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)

展开要点与分析

文章情报

工程师进阶

要点

  • AI 服务暂时不可用,系统已先保留来源内容与降级元数据。
  • arXiv:2609.10745v1 Announce Type: new Abstract: Multimodal entity linking grounds entity mentions in text and images to knowledge-base entries. These systems degrade on rare entit…

要点与分析由自动化流程生成,可能有误,请结合原始来源核实。