Pith. sign in

REVIEW 9 cited by

Not All Languages Are Created Equal in LLMs: Improving Multilingual Capability by Cross-Lingual-Thought Prompting

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2305.07004 v2 pith:I2H2OZAP submitted 2023-05-11 cs.CL

Haoyang Huang , Tianyi Tang , Dongdong Zhang , Wayne Xin Zhao , Ting Song , Yan Xia , Furu Wei This is my paper · ORCID
classification cs.CL
keywords languagesperformancemultilingualcapabilityllmsreasoningtasksacross
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Large language models (LLMs) demonstrate impressive multilingual capability, but their performance varies substantially across different languages. In this work, we introduce a simple yet effective method, called cross-lingual-thought prompting (XLT), to systematically improve the multilingual capability of LLMs. Specifically, XLT is a generic template prompt that stimulates cross-lingual and logical reasoning skills to enhance task performance across languages. We conduct comprehensive evaluations on 7 typical benchmarks related to reasoning, understanding, and generation tasks, covering both high-resource and low-resource languages. Experimental results show that XLT not only remarkably enhances the performance of various multilingual tasks but also significantly reduces the gap between the average performance and the best performance of each task in different languages. Notably, XLT brings over 10 points of average improvement in arithmetic reasoning and open-domain question-answering tasks.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 9 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Large Language Models for Code Generation from Multilingual Prompts: A Curated Benchmark and a Study on Code Quality

    cs.SE 2026-07 conditional novelty 6.0 of 10

    Prompt language affects LLM code generation, but English is not consistently best: Chinese prompts improve Python correctness on CoderEval, while quality and lexicon effects vary by model and programming language.

  2. The Emergence of Abstract Thought in Large Language Models Beyond Any Language

    cs.CL 2025-06 conditional novelty 6.0 of 10

    Across 20 open LLMs, shared multilingual neurons grow in number and per-neuron importance over release generations, which the authors interpret as evidence of language-agnostic abstract thought and use to guide neuron...

  3. Paths Not Taken: Understanding and Mending the Multilingual Factual Recall Pipeline

    cs.CL 2025-05 conditional novelty 6.0 of 10

    LLMs recall facts through an English-centric internal path and then translate the answer; injecting a translation vector and a recall vector raises accuracy by over 35 percentage points in the weakest language.

  4. MMATH: A Multilingual Benchmark for Mathematical Reasoning

    cs.CL 2025-05 conditional novelty 6.0 of 10

    A new multilingual math benchmark shows that reasoning models often respond in the wrong language, and English-reasoning training improves both accuracy and language consistency.

  5. Multilingual Question Answering in Low-Resource Settings: A Dzongkha-English Benchmark for Foundation Models

    cs.CL 2025-05 conditional novelty 6.0 of 10

    DZEN, a parallel Dzongkha-English benchmark of 5,161 school science exam questions, shows large LLM accuracy gaps in Dzongkha; adding English translations narrows the gap.

  6. Mind the Gap! Choice Independence in Using Multilingual LLMs for Persuasive Co-Writing Tasks in Different Languages

    cs.CL 2025-02 conditional novelty 6.0 of 10

    Users who first used a Spanish AI writing assistant subsequently used the English AI writing assistant less, suggesting a spillover that violates choice independence.

  7. SeaExam and SeaBench: Benchmarking LLMs with Local Multilingual Questions in Southeast Asia

    cs.CL 2025-02 conditional novelty 6.0 of 10

    SeaExam and SeaBench are locally sourced benchmarks for Indonesian, Thai, and Vietnamese that show larger cross-model performance differences than translated English benchmarks, though the evidence for 'better discrim...

  8. Text2Cypher Across Languages: Evaluating and Finetuning LLMs

    cs.CL 2025-06 conditional novelty 5.0 of 10

    A new multilingual Text2Cypher benchmark shows LLMs rank English highest, Spanish next, and Turkish lowest, and multilingual finetuning narrows the language gap more than English-only finetuning.

  9. Beyond English: The Impact of Prompt Translation Strategies across Languages and Tasks in Multilingual LLMs

    cs.CL 2025-02 conditional novelty 5.0 of 10

    Selective pre-translation, translating only some prompt components into English, generally outperforms both full prompt translation and direct inference across tasks and languages, with the largest gains for low-resou...

Pith tools