对比大模型解码器与经典编码器在孟加拉语零样本多标签分类中的表现
Zero-Shot Multi-Label Classification of Bangla Documents: Large Decoders Vs. Classic Encoders
- 首次在孟加拉语上对比32个先进模型的零样本多标签分类能力
- 发现现有主流模型在该任务上准确率仍不理想，存在明显提升空间
- 为资源稀缺语言的NLP研究提供关键基准，适合关注低资源语言的学者
孟加拉语是全球第六大使用语言，母语者超过3亿，其复杂的形态特征和有限的资源给自然语言处理带来独特挑战。尽管近年来基于解码器的大语言模型（如GPT、LLaMA、DeepSeek）在诸多NLP任务中表现优异，但其在孟加拉语上的应用仍鲜有研究。本文首次建立基准，对比基于解码器的LLM与经典编码器模型在孟加拉语零样本多标签分类（Zero-Shot-MLC）任务上的表现。我们评估了32个前沿模型，结果表明，当前所谓的高性能编码器与解码器在该任务上仍难以达到高准确率，凸显出对孟加拉语NLP进一步研究与资源投入的迫切需求。
原文摘要 · Abstract (English)
Bangla, a language spoken by over 300 million native speakers and ranked as the sixth most spoken language worldwide, presents unique challenges in natural language processing (NLP) due to its complex morphological characteristics and limited resources. While recent Large Decoder Based models (LLMs), such as GPT, LLaMA, and DeepSeek, have demonstrated excellent performance across many NLP tasks, their effectiveness in Bangla remains largely unexplored. In this paper, we establish the first benchmark comparing decoder-based LLMs with classic encoder-based models for Zero-Shot Multi-Label Classification (Zero-Shot-MLC) task in Bangla. Our evaluation of 32 state-of-the-art models reveals that, existing so-called powerful encoders and decoders still struggle to achieve high accuracy on the Bangla Zero-Shot-MLC task, suggesting a need for more research and resources for Bangla NLP.
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