Extending Context Window of Large Language Models via Semantic Compression

• URL: http://arxiv.org/abs/2312.09571v1
• Date: Fri, 15 Dec 2023 07:04:33 GMT
• Title: Extending Context Window of Large Language Models via Semantic Compression
• Authors: Weizhi Fei, Xueyan Niu, Pingyi Zhou, Lu Hou, Bo Bai, Lei Deng, Wei Han
• Abstract summary: Large Language Models (LLMs) often impose limitations on the length of the text input to ensure the generation of fluent and relevant responses. We propose a novel semantic compression method that enables generalization to texts 6-8 times longer, without incurring significant computational costs or requiring fine-tuning.
• Score: 21.35020344956721
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Transformer-based Large Language Models (LLMs) often impose limitations on the length of the text input to ensure the generation of fluent and relevant responses. This constraint restricts their applicability in scenarios involving long texts. We propose a novel semantic compression method that enables generalization to texts that are 6-8 times longer, without incurring significant computational costs or requiring fine-tuning. Our proposed framework draws inspiration from source coding in information theory and employs a pre-trained model to reduce the semantic redundancy of long inputs before passing them to the LLMs for downstream tasks. Experimental results demonstrate that our method effectively extends the context window of LLMs across a range of tasks including question answering, summarization, few-shot learning, and information retrieval. Furthermore, the proposed semantic compression method exhibits consistent fluency in text generation while reducing the associated computational overhead.

Related papers

• BRIEF: Bridging Retrieval and Inference for Multi-hop Reasoning via Compression [91.23933111083389]
  BRIEF (Bridging Retrieval and Inference through Evidence Fusion) is a lightweight approach that performs query-aware multi-hop reasoning. Based on our synthetic data built entirely by open-source models, BRIEF generates more concise summaries.
  arXiv  Detail & Related papers  (2024-10-20T04:24:16Z)
• LLM$\times$MapReduce: Simplified Long-Sequence Processing using Large Language Models [73.13933847198395]
  We propose a training-free framework for processing long texts, utilizing a divide-and-conquer strategy to achieve comprehensive document understanding. The proposed LLM$times$MapReduce framework splits the entire document into several chunks for LLMs to read and then aggregates the intermediate answers to produce the final output.
  arXiv  Detail & Related papers  (2024-10-12T03:13:44Z)
• A Novel LLM-based Two-stage Summarization Approach for Long Dialogues [9.835499880812646]
  This study proposes a hierarchical framework that segments and condenses information from long documents. The condensation stage utilizes an unsupervised generation model to generate condensed data. The summarization stage fine-tunes the abstractive summarization model on the condensed data to generate the final results.
  arXiv  Detail & Related papers  (2024-10-09T03:42:40Z)
• Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language Models [79.41139393080736]
  Large language models (LLMs) have rapidly advanced and demonstrated impressive capabilities. In-Context Learning (ICL) and. Efficient Fine-Tuning (PEFT) are currently two mainstream methods for augmenting. LLMs to downstream tasks. We propose Reference Trustable Decoding (RTD), a paradigm that allows models to quickly adapt to new tasks without fine-tuning.
  arXiv  Detail & Related papers  (2024-09-30T10:48:20Z)
• Scaling Up Summarization: Leveraging Large Language Models for Long Text Extractive Summarization [0.27624021966289597]
  This paper introduces EYEGLAXS, a framework that leverages Large Language Models (LLMs) for extractive summarization. EYEGLAXS focuses on extractive summarization to ensure factual and grammatical integrity. The system sets new performance benchmarks on well-known datasets like PubMed and ArXiv.
  arXiv  Detail & Related papers  (2024-08-28T13:52:19Z)
• Let Me Speak Freely? A Study on the Impact of Format Restrictions on Performance of Large Language Models [59.970391602080205]
  This study investigates whether such constraints on generation space impact LLMs abilities, including reasoning and domain knowledge comprehension. We evaluate LLMs performance when restricted to adhere to structured formats versus generating free-form responses across various common tasks. We find that stricter format constraints generally lead to greater performance degradation in reasoning tasks.
  arXiv  Detail & Related papers  (2024-08-05T13:08:24Z)
• KV Cache Compression, But What Must We Give in Return? A Comprehensive Benchmark of Long Context Capable Approaches [52.02764371205856]
  Long context capability is a crucial competency for large language models (LLMs) This work provides a taxonomy of current methods and evaluating 10+ state-of-the-art approaches across seven categories of long context tasks.
  arXiv  Detail & Related papers  (2024-07-01T17:59:47Z)
• Adapting LLMs for Efficient Context Processing through Soft Prompt Compression [1.1550486371582305]
  This article presents an innovative framework that strategically tailors Large Language Models for streamlined context processing. Our methodology, dubbed SoftPromptComp, amalgamates natural language prompts with dynamically generated soft prompts to forge a concise yet semantically robust depiction of protracted contexts. We substantiate that our framework markedly diminishes computational overhead and enhances LLMs' efficacy across various benchmarks.
  arXiv  Detail & Related papers  (2024-04-07T15:44:20Z)
• Evaluating, Understanding, and Improving Constrained Text Generation for Large Language Models [49.74036826946397]
  This study investigates constrained text generation for large language models (LLMs) Our research mainly focuses on mainstream open-source LLMs, categorizing constraints into lexical, structural, and relation-based types. Results illuminate LLMs' capacity and deficiency to incorporate constraints and provide insights for future developments in constrained text generation.
  arXiv  Detail & Related papers  (2023-10-25T03:58:49Z)
• Compressing Context to Enhance Inference Efficiency of Large Language Models [26.75216730927996]
  This paper proposes a method called Selective Context to enhance the inference efficiency of large language models (LLMs) We test our approach using common data sources requiring long context processing: arXiv papers, news articles, and long conversations. Experimental results show that Selective Context significantly reduces memory cost and decreases generation latency.
  arXiv  Detail & Related papers  (2023-10-09T23:03:24Z)
• LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models [22.06402870816756]
  Large language models (LLMs) have been applied in various applications due to their astonishing capabilities. This paper presents LLMLingua, a coarse-to-fine prompt compression method that involves a budget controller to maintain semantic integrity. We show that the proposed approach yields state-of-the-art performance and allows for up to 20x compression with little performance loss.
  arXiv  Detail & Related papers  (2023-10-09T14:10:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.