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English(EN) VakyArth: Evaluating Pragmatic Competence in LLMs across Indic Languages

新基准揭示大型语言模型在印度语言和翻译机制方面存在困难 · 跟踪3个来源

研究人员开发了VakyArth,这是一个新的基准,旨在评估大型语言模型(LLMs)在印地语、旁遮普语、泰米尔语和马拉雅拉姆语等印度语言中的语用能力。初步研究结果显示,在解释这些语言中通过上下文和文化习俗暗示的含义时,大型语言模型存在持续的失败,并且在不同语言和任务中的表现差异很大。此外,另一项独立研究对多语言大型语言模型中“潜在语言”的解释提出了质疑,认为当前的探测可能揭示了多语言处理的不同方面,而不是单一的内部通用语。另一篇论文提出,多语言大型语言模型中的翻译是模块化的,句法与语言生成是分开的,并确定了对句法转换敏感的特定注意力头。 AI

影响 突出了大型语言模型在非英语语言理解和翻译能力方面的关键差距,强调需要更多样化的评估数据集和对模型内部机制更细致的解读。

排序理由 该集群包含三篇在arXiv上发表的学术论文,详细介绍了关于大型语言模型能力和机制的新研究。

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新基准揭示大型语言模型在印度语言和翻译机制方面存在困难 · 跟踪3个来源

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该集群包含三篇在arXiv上发表的学术论文,详细介绍了关于大型语言模型能力和机制的新研究。
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报道来源 [4]

  1. arXiv cs.AI TIER_1 English(EN) · Usneek Singh, Poorvaja Veera Balaji Kumar, Parth Nanda, Anand Madhusoodanan, Geyang Guo, Wei Xu, Junyi Jessy L ·

    VakyArth:评估LLM在印度语言中的语用能力

    arXiv:2609.01788v1 Announce Type: cross Abstract: Real-world communication often requires pragmatic reasoning: interpreting meanings implied through context and cultural convention rather than stated literally. Existing pragmatic evaluation remains largely limited to English and …

  2. arXiv cs.AI TIER_1 English(EN) · Deniz Bayazit, Badr AlKhamissi, Antoine Bosselut ·

    通用语还是探测伪影?重新思考多语言大型语言模型中的潜在语言

    arXiv:2609.00155v1 Announce Type: cross Abstract: Latent language identification is often used to argue that multilingual language models route computation through language-specific states, such as English pivots. However, existing probes infer latent language from different sign…

  3. arXiv cs.CL TIER_1 English(EN) · Mikhail Sonkin, Tanja Baeumel, Daniil Gurgurov, Josef van Genabith, Simon Ostermann ·

    分离语法与语言:多语言大型语言模型中翻译的机制性解释

    arXiv:2609.01356v1 Announce Type: new Abstract: Multilingual large language models (mLLMs) achieve strong performance in machine translation, yet our understanding of the mechanisms by which they transform representations from one language to another remains incomplete. Prior wor…

  4. dev.to — LLM tag TIER_1 English(EN) · zahid23saim ·

    我从跨四种语言评估大型语言模型中学到的东西

    <p>Most writing about large language models assumes English. The benchmarks are in English, the failure examples are in English, and the quiet implication is that whatever is true in English is true everywhere else, just a little worse. After spending a long stretch evaluating mo…