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New dataset trains AI agents to search Wikidata more effectively

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced a new dataset and harness called Wikidata Search Traces, designed to train agents for querying large knowledge bases like Wikidata. The dataset addresses limitations in current language models, which often rely on internal memory rather than active graph exploration. The proposed method involves creating multi-hop questions and managing retrieved evidence effectively, showing improved performance for both commercial and open-weight models. AI

IMPACT This research could lead to more capable AI agents for complex information retrieval from large knowledge bases.

RANK_REASON The cluster contains a research paper detailing a new dataset and methodology for training AI agents on knowledge graph search. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New dataset trains AI agents to search Wikidata more effectively

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The cluster contains a research paper detailing a new dataset and methodology for training AI agents on knowledge graph search. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Mohamed Chenene, Carlos Rosas-Hinostroza, Anastasia Stasenko, Shani Evenstein Sigalov, Pierre-Carl Langlais ·

    Wikidata Search Traces: A Dataset for Training Knowledge Graph Search Agents

    arXiv:2610.06650v2 Announce Type: replace Abstract: Wikidata is one of the largest open knowledge bases, yet answering a complex question over it still requires a SPARQL query that names the right entities and properties and chains their relations. Language models offer a natural…

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