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English-forced LLM communication incurs significant performance tax

By PulseAugur Editorial · [2 sources] ·

A new research paper investigates the performance impact of forcing multi-agent LLM communication through English, even for non-English tasks. The study found a significant "English-Forcing Tax," which reduces accuracy by up to 30.6 percentage points in languages like Hindi compared to native-language pipelines. This suggests that using native languages for inter-agent communication can improve performance, especially for typologically distant languages, by mitigating translation loss. AI

IMPACT Native-language routing in agent frameworks may be crucial for performance, especially in cross-lingual applications.

RANK_REASON The cluster contains an academic paper detailing research findings on LLM communication.

Read on arXiv cs.MA (Multiagent) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

English-forced LLM communication incurs significant performance tax

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The cluster contains an academic paper detailing research findings on LLM communication.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Kushagra Agrawal, Yuming Feng, Man-Fai Leung ·

    Translating the Translator: Decomposing the Cost of English-Forced Inter-Agent Communication

    arXiv:2609.15079v1 Announce Type: cross Abstract: Multi-agent LLM architectures, such as LangChain and AutoGen, largely assume English as the lingua franca for internal inter-agent communication, even when the end-user task is non-English. We fill this gap by evaluating a two-age…

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Man-Fai Leung ·

    Translating the Translator: Decomposing the Cost of English-Forced Inter-Agent Communication

    Multi-agent LLM architectures, such as LangChain and AutoGen, largely assume English as the lingua franca for internal inter-agent communication, even when the end-user task is non-English. We fill this gap by evaluating a two-agent extraction-answer core, with an additional back…

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