mlsanityData to 7 Oct 2026

Selected Languages are All You Need for Cross-lingual Truthfulness Transfer

arXiv 2406.14434 ↗First posted 20 Jun 2024cs.CLcited by 4

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Abstract (arXiv)

Truthfulness stands out as an essential challenge for Large Language Models (LLMs). Although many works have developed various ways for truthfulness enhancement, they seldom focus on truthfulness in multilingual scenarios. Meanwhile, contemporary multilingual aligning technologies struggle to balance numerous languages and often exhibit serious truthfulness gaps across different languages, especially those that differ greatly from English. In our work, we extend truthfulness evaluation to multilingual contexts and propose a practical method for cross-lingual truthfulness transfer called Fact-aware Multilingual Selective Synergy (FaMSS). FaMSS is able to select an optimal subset of all tested languages by language bias and transfer contributions, and then employ translation instruction tuning for cross-lingual truthfulness transfer. Experimental results demonstrate that our approach can effectively reduce the multilingual representation disparity and boost cross-lingual truthfulness transfer of LLMs.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 40 of its 58 references that are in the map. First posted on arXiv 20 Jun 2024.

Builds on

40 of 58 references.

References · 40

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28 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 1.

4 papers.

  1. 2024A Survey on Multimodal Benchmarks: In the Era of Large AI Modelscited by 27
  2. 2025Paths Not Taken: Understanding and Mending the Multilingual Factual Recall Pipelinecited by 20
  3. 2025CREST: Universal Safety Guardrails Through Cluster-Guided Cross-Lingual Transfercited by 2
  4. 2026Inference-Time Steering for Cross-Lingual Factual Consistency in LLMscited by 1