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

• URL: http://arxiv.org/abs/2609.09349v1
• Date: Tue, 08 Sep 2026 18:39:01 GMT
• Title: SWORD: Wikidata-based Distortions Reveal Hidden Cross-Lingual Inconsistencies in LLM Factual Error Rejection
• Abstract summary: We introduce Systematic Wikidata-based Object-Relation Distortion (SWORD), a benchmark that evaluates whether models consistently reject factual errors across languages.<n>SWORD generates syntactically well-formed but factually incorrect statements in eight widely spoken languages through controlled perturbations of Wikidata triples.<n>Our evaluation surfaces two critical insights that remain entirely obscured by conventional benchmarks.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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-Relation Distortion (SWORD), a benchmark that evaluates whether models consistently reject factual errors across languages. SWORD generates syntactically well-formed but factually incorrect statements in eight widely spoken languages through controlled perturbations of Wikidata triples, ranging from random entity substitutions to semantically plausible property-based selections. Our distortion-based evaluation surfaces two critical insights that remain entirely obscured by conventional benchmarks. First, models counterintuitively achieve higher accuracy on semantically plausible distortions than on nonsensical random substitutions, suggesting reliance on distributional familiarity rather than genuine factual verification. Second, models exhibiting comparable baseline accuracy across languages show substantial performance degradation specifically on (East) Asian languages when presented with distorted statements, with cross-lingual performance gaps reaching up to 28 percentage points (49\% relative reduction) in some models. These findings demonstrate that multilingual factual reasoning involves asymmetric capabilities that aggregate accuracy metrics systematically obscure.

Related papers

• M-GATE: Multilingual Grammar, Accuracy in Translation, and Efficiency Benchmark for Large Language Models [0.0]
  M-GATE is a benchmark of linguistic proficiency spanning 30 typologically diverse languages.<n>We evaluate over 50 models in more than 80 configurations.
  arXiv  Detail & Related papers  (2026-08-04T15:16:05Z)
• Inference-Time Steering for Cross-Lingual Factual Consistency in LLMs [51.56484100374058]
  Large Language Models (LLMs) demonstrate remarkable multilingual fluency, but their internal knowledge representations remain disproportionately biased toward high-resource languages.<n>We investigate whether these biases can be mitigated at inference time, forcing an English-prompted model to answer as if it were queried in target languages.<n>Experiments on Gemma 3 12B Instruct reveal persona prompting to be the strongest overall intervention, balancing efficacy, safety, and out-of-domain generalization.
  arXiv  Detail & Related papers  (2026-07-21T16:15:05Z)
• Lower-Resource, Higher Scores: Language Bias in LLM Evaluators [27.959960538188753]
  We conduct experiments with semantically identical instruction-response pairs across 23 languages.<n>We find that multilingual evaluators assign significantly different scores to different evaluation languages.
  arXiv  Detail & Related papers  (2026-07-16T01:53:09Z)
• Quantifying the Impact of Translation Errors on Multilingual LLM Evaluation [8.48956061615262]
  Machine-translated benchmarks are widely used to assess the multilingual capabilities of large language models (LLMs)<n>We address two practical gaps: how well automatic MQM-style error spans from LLM judges and a span-aware QE baseline.<n>We find that span agreement is non-trivial on naturally occurring benchmark translations, and that target-side translation errors are consistently associated with measurable, percentage-point drops in translated accuracy.
  arXiv  Detail & Related papers  (2026-05-24T07:06:34Z)
• Cross-Lingual Stability of LLM Judges Under Controlled Generation: Evidence from Finno-Ugric Languages [0.22009842278462158]
  Cross-lingual evaluation of large language models (LLMs) typically conflates two sources of variance: genuine model performance differences and measurement instability.<n>We investigate evaluation reliability by holding generation conditions constant while varying target language.<n>Our findings suggest that zero-shot judge transfer is unreliable for discourse-level assessment in morphologically rich languages.
  arXiv  Detail & Related papers  (2026-02-02T16:27:32Z)
• Parallel Universes, Parallel Languages: A Comprehensive Study on LLM-based Multilingual Counterfactual Example Generation [49.2073409243885]
  Large language models (LLMs) excel at generating English counterfactuals and demonstrate multilingual proficiency.<n>We conduct automatic evaluations on both directly generated counterfactuals in the target languages and those derived via English translation across six languages.<n>We identify and categorize four main types of errors that consistently appear in the generated counterfactuals across languages.
  arXiv  Detail & Related papers  (2026-01-01T08:53:49Z)
• Adapting Language Balance in Code-Switching Speech [60.296574524609575]
  Large foundational models still struggle against code-switching test cases.<n>We use differentiable surrogates to mitigate context bias during generation.<n>Experiments with Arabic and Chinese-English showed that the models are able to predict the switching places more correctly.
  arXiv  Detail & Related papers  (2025-10-21T15:23:55Z)
• LINGOLY-TOO: Disentangling Reasoning from Knowledge with Templatised Orthographic Obfuscation [1.2576388595811496]
  We introduce LINGOLY-TOO, a challenging reasoning benchmark grounded in natural language.<n>We permute reasoning problems written in real languages to generate numerous question variations.<n>Experiments and analyses show that models can circumvent reasoning and answer from prior knowledge.
  arXiv  Detail & Related papers  (2025-03-04T19:57:47Z)
• ExpliCa: Evaluating Explicit Causal Reasoning in Large Language Models [75.05436691700572]
  We introduce ExpliCa, a new dataset for evaluating Large Language Models (LLMs) in explicit causal reasoning.<n>We tested seven commercial and open-source LLMs on ExpliCa through prompting and perplexity-based metrics.<n>Surprisingly, models tend to confound temporal relations with causal ones, and their performance is also strongly influenced by the linguistic order of the events.
  arXiv  Detail & Related papers  (2025-02-21T14:23:14Z)
• A Comparative Study of Translation Bias and Accuracy in Multilingual Large Language Models for Cross-Language Claim Verification [1.566834021297545]
  This study systematically evaluates translation bias and the effectiveness of Large Language Models for cross-lingual claim verification. We investigate two distinct translation methods: pre-translation and self-translation. Our findings reveal that low-resource languages exhibit significantly lower accuracy in direct inference due to underrepresentation.
  arXiv  Detail & Related papers  (2024-10-14T09:02:42Z)
• Evaluating Generative Language Models in Information Extraction as Subjective Question Correction [49.729908337372436]
  We propose a new evaluation method, SQC-Score. Inspired by the principles in subjective question correction, we propose a new evaluation method, SQC-Score. Results on three information extraction tasks show that SQC-Score is more preferred by human annotators than the baseline metrics.
  arXiv  Detail & Related papers  (2024-04-04T15:36:53Z)
• Fine-tuning Language Models for Factuality [96.5203774943198]
  Large pre-trained language models (LLMs) have led to their widespread use, sometimes even as a replacement for traditional search engines. Yet language models are prone to making convincing but factually inaccurate claims, often referred to as 'hallucinations' In this work, we fine-tune language models to be more factual, without human labeling.
  arXiv  Detail & Related papers  (2023-11-14T18:59:15Z)
• FactCHD: Benchmarking Fact-Conflicting Hallucination Detection [64.4610684475899]
  FactCHD is a benchmark designed for the detection of fact-conflicting hallucinations from LLMs. FactCHD features a diverse dataset that spans various factuality patterns, including vanilla, multi-hop, comparison, and set operation. We introduce Truth-Triangulator that synthesizes reflective considerations by tool-enhanced ChatGPT and LoRA-tuning based on Llama2.
  arXiv  Detail & Related papers  (2023-10-18T16:27:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.