Investigating Answerability of LLMs for Long-Form Question Answering

• URL: http://arxiv.org/abs/2309.08210v1
• Date: Fri, 15 Sep 2023 07:22:56 GMT
• Title: Investigating Answerability of LLMs for Long-Form Question Answering
• Authors: Meghana Moorthy Bhat, Rui Meng, Ye Liu, Yingbo Zhou and Semih Yavuz
• Abstract summary: We focus on long-form question answering (LFQA) because it has several practical and impactful applications. We propose a question-generation method from abstractive summaries and show that generating follow-up questions from summaries of long documents can create a challenging setting.
• Score: 35.41413072729483
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As we embark on a new era of LLMs, it becomes increasingly crucial to understand their capabilities, limitations, and differences. Toward making further progress in this direction, we strive to build a deeper understanding of the gaps between massive LLMs (e.g., ChatGPT) and smaller yet effective open-source LLMs and their distilled counterparts. To this end, we specifically focus on long-form question answering (LFQA) because it has several practical and impactful applications (e.g., troubleshooting, customer service, etc.) yet is still understudied and challenging for LLMs. We propose a question-generation method from abstractive summaries and show that generating follow-up questions from summaries of long documents can create a challenging setting for LLMs to reason and infer from long contexts. Our experimental results confirm that: (1) our proposed method of generating questions from abstractive summaries pose a challenging setup for LLMs and shows performance gaps between LLMs like ChatGPT and open-source LLMs (Alpaca, Llama) (2) open-source LLMs exhibit decreased reliance on context for generated questions from the original document, but their generation capabilities drop significantly on generated questions from summaries -- especially for longer contexts (>1024 tokens)

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