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LLMs show varied performance in English vs. Bengali summarization

By PulseAugur Editorial · [1 sources] ·

A new study published on arXiv evaluates the performance of eight large language models (LLMs) on multilingual abstractive summarization, focusing on English and Bengali. The research found significant cross-lingual performance variations, with models like GPT 5.6 Luna excelling in English but not consistently in Bengali. LLaMA 3.3 70B showed strong ROUGE and BLEU scores in Bengali, while Gemini 2.5 Flash achieved the highest factuality score for the language. The study highlights the limitations of single-metric evaluations and the need for comprehensive frameworks for low-resource languages. AI

IMPACT Highlights the need for specialized evaluation frameworks for low-resource languages, impacting how LLMs are assessed and developed for global applications.

RANK_REASON Academic paper evaluating LLM performance on a specific NLP task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs show varied performance in English vs. Bengali summarization

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Academic paper evaluating LLM performance on a specific NLP task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Khan Mahi Al Atahar, Nazifa Tasneem, Ajwad Abrar ·

    Evaluating Large Language Models for Multilingual Abstractive Summarization: A Comprehensive Study of English and Bengali

    arXiv:2610.19003v1 Announce Type: new Abstract: Abstractive summarization remains challenging across languages, where linguistic quality and factual consistency may diverge, particularly in low-resource settings. We present a standardized zero-shot benchmark of eight closed-sourc…

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