Cmprsr: Abstractive Token-Level Question-Agnostic Prompt Compressor

• URL: http://arxiv.org/abs/2511.12281v1
• Date: Sat, 15 Nov 2025 16:28:03 GMT
• Title: Cmprsr: Abstractive Token-Level Question-Agnostic Prompt Compressor
• Authors: Ivan Zakazov, Alexander Sharipov, Berke Argin, Oussama Gabouj, Kamel Charaf, Alexi Semiz, Lorenzo Drudi, Nicolas Baldwin, Robert West,
• Abstract summary: We present the first comprehensive LLM-as-a-compressor benchmark spanning 25 open- and closed-source models.<n>We improve the performance of the best overall vanilla compressor, with Textgrad-based compression meta-prompt optimization.<n>We call the resulting model Cmprsr and demonstrate its superiority over both extractive and vanilla abstractive compression.
• Score: 36.57824786347272
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Motivated by the high costs of using black-box Large Language Models (LLMs), we introduce a novel prompt compression paradigm, under which we use smaller LLMs to compress inputs for the larger ones. We present the first comprehensive LLM-as-a-compressor benchmark spanning 25 open- and closed-source models, which reveals significant disparity in models' compression ability in terms of (i) preserving semantically important information (ii) following the user-provided compression rate (CR). We further improve the performance of gpt-4.1-mini, the best overall vanilla compressor, with Textgrad-based compression meta-prompt optimization. We also identify the most promising open-source vanilla LLM - Qwen3-4B - and post-train it with a combination of supervised fine-tuning (SFT) and Group Relative Policy Optimization (GRPO), pursuing the dual objective of CR adherence and maximizing the downstream task performance. We call the resulting model Cmprsr and demonstrate its superiority over both extractive and vanilla abstractive compression across the entire range of compression rates on lengthy inputs from MeetingBank and LongBench as well as short prompts from GSM8k. The latter highlights Cmprsr's generalizability across varying input lengths and domains. Moreover, Cmprsr closely follows the requested compression rate, offering fine control over the cost-quality trade-off.

Related papers

• Arbitrary Ratio Feature Compression via Next Token Prediction [52.10426317889982]
  Arbitrary Ratio Feature Compression (ARFC) framework supports any compression ratio with a single model.<n>ARC is an auto-regressive model that performs compression via next-gressive prediction.<n>MoS module refines the compressed tokens by utilizing multiple compression results.<n>ERGC is integrated into the training process to preserve semantic and structural relationships during compression.
  arXiv  Detail & Related papers  (2026-02-12T02:38:57Z)
• Context Compression via Explicit Information Transmission [25.078241611630585]
  Long-context inference with Large Language Models (LLMs) is costly due to quadratic attention and growing key-value caches.<n>We propose ComprExIT, a lightweight framework that formulates soft compression into a new paradigm.
  arXiv  Detail & Related papers  (2026-02-03T17:44:12Z)
• VisionSelector: End-to-End Learnable Visual Token Compression for Efficient Multimodal LLMs [82.72388893596555]
  Multimodal Large Language Models (MLLMs) encounter significant computational and memory bottlenecks.<n>Previous token compression techniques are often constrained by rules that risk discarding critical information.<n>We reformulate token compression as a lightweight plug-and-play framework that reformulates token compression into an end-to-end learnable decision process.
  arXiv  Detail & Related papers  (2025-10-18T17:54:18Z)
• Compressing Many-Shots in In-Context Learning [61.231471139896506]
  We study an approach to improve the memory and computational efficiency of ICL inference by compressing the many-shot prompts.<n>We first show that existing prompt compression methods are ineffective for many-shot compression.<n>We propose MemCom, a layer-wise compression method.
  arXiv  Detail & Related papers  (2025-10-17T16:57:42Z)
• Large Language Models for Lossless Image Compression: Next-Pixel Prediction in Language Space is All You Need [53.584140947828004]
  Language large model (LLM) with unprecedented intelligence is a general-purpose lossless compressor for various data modalities. We propose P$2$-LLM, a next-pixel prediction-based LLM, which integrates various elaborated insights and methodologies. Experiments on benchmark datasets demonstrate that P$2$-LLM can beat SOTA classical and learned codecs.
  arXiv  Detail & Related papers  (2024-11-19T12:15:40Z)
• LoRC: Low-Rank Compression for LLMs KV Cache with a Progressive Compression Strategy [59.1298692559785]
  Key-Value ( KV) cache is crucial component in serving transformer-based autoregressive large language models (LLMs) Existing approaches to mitigate this issue include: (1) efficient attention variants integrated in upcycling stages; (2) KV cache compression at test time; and (3) KV cache compression at test time. We propose a low-rank approximation of KV weight matrices, allowing plug-in integration with existing transformer-based LLMs without model retraining. Our method is designed to function without model tuning in upcycling stages or task-specific profiling in test stages.
  arXiv  Detail & Related papers  (2024-10-04T03:10:53Z)
• Retaining Key Information under High Compression Ratios: Query-Guided Compressor for LLMs [35.91962517513945]
  Performance of previous methods degrades dramatically as compression ratios increase, sometimes even falling to the closed-book level. We introduce Query-Guided (QGC) which leverages queries to guide the context compression process. We validate the effectiveness of our proposed QGC on the Question Answering task, including NaturalQuestions, TriviaQA, and HotpotQA datasets.
  arXiv  Detail & Related papers  (2024-06-04T14:53:24Z)
• LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression [43.048684907893104]
  This paper focuses on task-agnostic prompt compression for better generalizability and efficiency. We formulate prompt compression as a token classification problem to guarantee the faithfulness of the compressed prompt to the original one. Our approach leads to lower latency by explicitly learning the compression objective with smaller models such as XLM-RoBERTa-large and mBERT.
  arXiv  Detail & Related papers  (2024-03-19T17:59:56Z)
• Long Context Compression with Activation Beacon [22.054232261437186]
  Activation Beacon is a plug-in module for transformer-based LLMs. It targets effective, efficient, and flexible compression of long contexts. It achieves a 2x acceleration in inference time and an 8x reduction of memory costs for KV cache.
  arXiv  Detail & Related papers  (2024-01-07T11:57:40Z)
• GAN Slimming: All-in-One GAN Compression by A Unified Optimization Framework [94.26938614206689]
  We propose the first unified optimization framework combining multiple compression means for GAN compression, dubbed GAN Slimming. We apply GS to compress CartoonGAN, a state-of-the-art style transfer network, by up to 47 times, with minimal visual quality degradation.
  arXiv  Detail & Related papers  (2020-08-25T14:39:42Z)

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.