Long-context Non-factoid Question Answering in Indic Languages

• URL: http://arxiv.org/abs/2504.13615v1
• Date: Fri, 18 Apr 2025 10:43:21 GMT
• Title: Long-context Non-factoid Question Answering in Indic Languages
• Authors: Ritwik Mishra, Rajiv Ratn Shah, Ponnurangam Kumaraguru,
• Abstract summary: Question Answering tasks involve extracting answers from a given context.<n>Long contexts pose challenges due to the complexity of the self-attention mechanism.<n>This study explores context-shortening techniques to improve QA performance in Indic languages.
• Score: 39.66936316245065
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Question Answering (QA) tasks, which involve extracting answers from a given context, are relatively straightforward for modern Large Language Models (LLMs) when the context is short. However, long contexts pose challenges due to the quadratic complexity of the self-attention mechanism. This challenge is compounded in Indic languages, which are often low-resource. This study explores context-shortening techniques, including Open Information Extraction (OIE), coreference resolution, Answer Paragraph Selection (APS), and their combinations, to improve QA performance. Compared to the baseline of unshortened (long) contexts, our experiments on four Indic languages (Hindi, Tamil, Telugu, and Urdu) demonstrate that context-shortening techniques yield an average improvement of 4\% in semantic scores and 47\% in token-level scores when evaluated on three popular LLMs without fine-tuning. Furthermore, with fine-tuning, we achieve an average increase of 2\% in both semantic and token-level scores. Additionally, context-shortening reduces computational overhead. Explainability techniques like LIME and SHAP reveal that when the APS model confidently identifies the paragraph containing the answer, nearly all tokens within the selected text receive high relevance scores. However, the study also highlights the limitations of LLM-based QA systems in addressing non-factoid questions, particularly those requiring reasoning or debate. Moreover, verbalizing OIE-generated triples does not enhance system performance. These findings emphasize the potential of context-shortening techniques to improve the efficiency and effectiveness of LLM-based QA systems, especially for low-resource languages. The source code and resources are available at https://github.com/ritwikmishra/IndicGenQA.

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