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

[2409.06679] E2LLM: Encoder Elongated Large Language Models for Long-Context Understanding and Reasoning Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computation and Language arXiv:2409.06679 (cs) [Submitted on 10 Sep 2024 ( v1 ), last revised 14 Apr 2026 (this version, v3)] 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 View a PDF of the paper titled E2LLM: Encoder Elongated Large Language Models for Long-Context Understanding and Reasoning, by Zihan Liao and 6 other authors View PDF HTML (experimental) Abstract: Processing long contexts is increasingly important for Large Language Models (LLMs) in tasks like multi-turn dialogues, code generation, and document summarization. This paper addresses the challenges of achieving high long-context performance, low computational complexity, and compatibility with pretrained models -- collectively termed the ``impossible triangle''. We introduce E2LLM (Encoder Elongated Large Language Models), a novel approach that effectively navigates this paradox. E2LLM divides long contexts into chunks, compresses each into soft prompts using a pretrained text encoder, and aligns these representations with a decoder-only LLM via an adapter. To enhance the LLM's reasoning with these soft prompts, we employ two training objectives: encoder output reconstruction and long-context instruction fine-tuning. Extensive experiments reveal that E2LLM not only outperforms 8 state-of-the-art (SOTA) methods in effectiveness and efficiency for document summarization and question answering, but also achieves the best performance on LongBench v2 among models of comparable size. Comments: Accept by EMNLP'25 Subjects: Computation and Language (cs.CL) Cite as: arXiv:2409.06679 [cs.CL] (or arXiv:2409.06679v3 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2409.06679 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Zihan Liao [ view email ] [v1] Tue, 10 Sep 2024 17:44:35 UTC (637 KB) [v2] Fri, 29 Aug 2025 06:56:02 UTC (383 KB) [v3] Tue, 14 Apr 2026 06:50:10 UTC (363 KB) Full-text links: Access Paper: View a PDF of the paper titled E2LLM: Encoder Elongated Large Language Models for Long-Context Understanding and Reasoning, by Zihan Liao and 6 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL < prev | next > new | recent | 2024-09 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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