FltLM: An Intergrated Long-Context Large Language Model for Effective Context Filtering and Understanding

• URL: http://arxiv.org/abs/2410.06886v1
• Date: Wed, 9 Oct 2024 13:47:50 GMT
• Title: FltLM: An Intergrated Long-Context Large Language Model for Effective Context Filtering and Understanding
• Authors: Jingyang Deng, Zhengyang Shen, Boyang Wang, Lixin Su, Suqi Cheng, Ying Nie, Junfeng Wang, Dawei Yin, Jinwen Ma,
• Abstract summary: We propose a novel integrated Long-Context Large Language Model (FltLM) FltLM incorporates a context filter with a soft mask mechanism, identifying and dynamically excluding irrelevant content to concentrate on pertinent information. Experimental results demonstrate that FltLM significantly outperforms supervised fine-tuning and retrieval-based methods in complex QA scenarios.
• Score: 32.197113821638936
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The development of Long-Context Large Language Models (LLMs) has markedly advanced natural language processing by facilitating the process of textual data across long documents and multiple corpora. However, Long-Context LLMs still face two critical challenges: The lost in the middle phenomenon, where crucial middle-context information is likely to be missed, and the distraction issue that the models lose focus due to overly extended contexts. To address these challenges, we propose the Context Filtering Language Model (FltLM), a novel integrated Long-Context LLM which enhances the ability of the model on multi-document question-answering (QA) tasks. Specifically, FltLM innovatively incorporates a context filter with a soft mask mechanism, identifying and dynamically excluding irrelevant content to concentrate on pertinent information for better comprehension and reasoning. Our approach not only mitigates these two challenges, but also enables the model to operate conveniently in a single forward pass. Experimental results demonstrate that FltLM significantly outperforms supervised fine-tuning and retrieval-based methods in complex QA scenarios, suggesting a promising solution for more accurate and reliable long-context natural language understanding applications.

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