E2LLM: Encoder Elongated Large Language Models for Long-Context Understanding and Reasoning

• URL: http://arxiv.org/abs/2409.06679v1
• Date: Tue, 10 Sep 2024 17:44:35 GMT
• Title: E2LLM: Encoder Elongated Large Language Models for Long-Context Understanding and Reasoning
• Authors: Zihan Liao, Jun Wang, Hang Yu, Lingxiao Wei, Jianguo Li, Jun Wang, Wei Zhang,
• Abstract summary: We introduce E2LLM (Encodergated Large Language Models), a novel approach that effectively navigates the "impossible triangle" The method involves splitting long contexts into chunks, compressing each into embedding vectors via a pretrained text encoder, and utilizing an adapter to align these representations with a decoder-only LLM. Experimental results demonstrate that E2LLM achieves superior performance in long-context scenarios while balancing efficiency, performance, and compatibility with pretrained models.
• Score: 20.660297311025417
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the realm of Large Language Models (LLMs), the ability to process long contexts is increasingly crucial for tasks such as multi-round dialogues, code generation, and document summarization. This paper addresses the challenges of enhancing the long-context performance, reducing computational complexity, and leveraging pretrained models collectively termed the "impossible triangle." We introduce E2LLM (Encoder Elongated Large Language Models), a novel approach that effectively navigates this paradox. The method involves splitting long contexts into chunks, compressing each into embedding vectors via a pretrained text encoder, and utilizing an adapter to align these representations with a decoder-only LLM. Two training objectives, focusing on reconstruction of the encoder output and long-context instruction fine-tuning, are employed to facilitate the understanding of soft prompts by the LLM. Experimental results demonstrate that E2LLM achieves superior performance in long-context scenarios while balancing efficiency, performance, and compatibility with pretrained models. Our framework thus represents a significant advancement in the field, contributing to effective long-text modeling.

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