Never Lost in the Middle: Mastering Long-Context Question Answering with Position-Agnostic Decompositional Training

• URL: http://arxiv.org/abs/2311.09198v2
• Date: Tue, 13 Aug 2024 19:04:18 GMT
• Title: Never Lost in the Middle: Mastering Long-Context Question Answering with Position-Agnostic Decompositional Training
• Authors: Junqing He, Kunhao Pan, Xiaoqun Dong, Zhuoyang Song, Yibo Liu, Qianguo Sun, Yuxin Liang, Hao Wang, Enming Zhang, Jiaxing Zhang,
• Abstract summary: Large language models (LLMs) are struggling to seek correct information in long contexts. This paper proposes to enhance the information searching and reflection ability of LLMs in long contexts via specially designed tasks. Experimental results show substantial improvement in Multi-doc QA and other benchmarks, superior to state-of-the-art models by 13.7% absolute gain in shuffled settings.
• Score: 9.128501882000315
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While large language models (LLMs) are equipped with longer text input capabilities than before, they are struggling to seek correct information in long contexts. The "lost in the middle" problem challenges most LLMs, referring to the dramatic decline in accuracy when correct information is located in the middle. To overcome this crucial issue, this paper proposes to enhance the information searching and reflection ability of LLMs in long contexts via specially designed tasks called Attention Strengthening Multi-doc QA (ASM QA). Following these tasks, our model excels in focusing more precisely on the desired information. Experimental results show substantial improvement in Multi-doc QA and other benchmarks, superior to state-of-the-art models by 13.7% absolute gain in shuffled settings, by 21.5% in passage retrieval task. We release our model, Ziya-Reader to promote related research in the community.

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