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New SWORD benchmark reveals cross-lingual factual inconsistencies in LLMs

By PulseAugur Editorial · [1 sources] ·

A new benchmark called SWORD has been developed to evaluate Large Language Models' (LLMs) ability to reject factual errors across different languages. SWORD uses distortions derived from Wikidata to create factually incorrect statements, revealing that models perform better on semantically plausible errors than random ones. The benchmark also highlights significant performance disparities in LLMs when processing East Asian languages compared to others, with accuracy drops of up to 28 percentage points. AI

IMPACT Highlights critical cross-lingual weaknesses in LLM factual reasoning, suggesting current benchmarks may obscure true understanding.

RANK_REASON The cluster contains a research paper introducing a new benchmark for evaluating LLMs. [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 SWORD benchmark reveals cross-lingual factual inconsistencies in LLMs

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The cluster contains a research paper introducing a new benchmark for evaluating LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Sanghyeok Park, Minji Kang, Hosung Kwak, Jinhyuk Yun ·

    SWORD: Wikidata-based Distortions Reveal Hidden Cross-Lingual Inconsistencies in LLM Factual Error Rejection

    arXiv:2609.09349v1 Announce Type: new Abstract: Modern LLMs demonstrate impressive multilingual performance, yet standard benchmarks primarily reward selecting correct answers rather than evaluating genuine factual understanding. We introduce Systematic Wikidata-based Object-Rela…

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