TCRA-LLM: Token Compression Retrieval Augmented Large Language Model for Inference Cost Reduction (2023.findings-emnlp)
Copied to clipboard
| Challenge: | ChatGPT and GPT-4 are commercial large language models (LLMs) however, they may produce vague responses or incorrect answers in certain specialized domains. |
| Approach: | They propose a token compression scheme that uses summarization and semantic compression to reduce the token size of LLMs. |
| Outcome: | The proposed method reduces token size by doing summarization and semantic compression while reducing token size with only 1.6% accuracy drop. |
Similar Papers
LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models (2023.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) are increasingly lengthy and require longer prompts . this paper presents a coarse-to-fine prompt compression method to reduce cost and increase performance. |
| Approach: | They propose a coarse-to-fine prompt compression method that maintains semantic integrity under high compression ratios and a token-level iterative compression algorithm to better model the interdependence between compressed contents. |
| Outcome: | The proposed method yields state-of-the-art performance and allows for up to 20x compression with little performance loss over four datasets from different scenarios. |
When Compression Meets Model Compression: Memory-Efficient Double Compression for Large Language Models (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models (LLMs) exhibit excellent performance in various tasks, but memory requirements present a challenge when deploying on memory-limited devices. |
| Approach: | They propose a framework to compress LLM after quantization further, achieving about 2.2x compression ratio. |
| Outcome: | The proposed model can achieve 40% reduction in memory size with negligible loss in accuracy and inference speed. |
Scaling Down, Serving Fast: Compressing and Deploying Efficient LLMs for Recommendation Systems (2025.emnlp-industry)
Copied to clipboard
Kayhan Behdin, Ata Fatahibaarzi, Qingquan Song, Yun Dai, Aman Gupta, Zhipeng Wang, Hejian Sang, Shao Tang, Gregory Dexter, Sirou Zhu, Siyu Zhu, Tejas Dharamsi, Vignesh Kothapalli, Zhoutong Fu, Yihan Cao, Pin-Lun Hsu, Fedor Borisyuk, Natesh S. Pillai, Luke Simon, Rahul Mazumder
| Challenge: | Large language models (LLMs) have demonstrated remarkable performance across a wide range of industrial applications. |
| Approach: | They propose two techniques for training and deploying small language models that deliver high performance for a variety of industry use cases. |
| Outcome: | The proposed techniques retain much of the quality of larger models while reducing training/serving costs and latency. |
LLMC: Benchmarking Large Language Model Quantization with a Versatile Compression Toolkit (2024.emnlp-industry)
Copied to clipboard
Ruihao Gong, Yang Yong, Shiqiao Gu, Yushi Huang, Chengtao Lv, Yunchen Zhang, Dacheng Tao, Xianglong Liu
| Challenge: | Existing quantization techniques have been categorized as 'simple' and 'highly efficient' however, their configurations vary from each other and cannot be fairly compared . |
| Approach: | They propose a plug-and-play compression toolkit to explore the impact of quantization. |
| Outcome: | The proposed toolkit explores the impact of quantization on large language models. |
LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression (2024.acl-long)
Copied to clipboard
| Challenge: | Longer prompts introduce irrelevant and redundant information, which can weaken LLMs' performance. |
| Approach: | They propose a prompt compression tool that improves LLMs' perception of key information in input prompts by up to 21.4% with around 4x fewer tokens in GPT-3.5-Turbo. |
| Outcome: | The proposed solution improves performance and reduces costs and latency by up to 21.4% with around 4x fewer tokens in the NaturalQuestions benchmark. |
Retrieval-based Knowledge Transfer: An Effective Approach for Extreme Large Language Model Compression (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Large-scale pre-trained language models (LLMs) have demonstrated exceptional performance in various natural language processing (NLP) tasks. |
| Approach: | They propose a new compression paradigm that extracts knowledge from pre-trained language models to construct a knowledge store from which the model can leverage it for effective inference. |
| Outcome: | The proposed model extracts knowledge from LLMs to construct a knowledge store, which the model can leverage for effective inference. |
The Cost of Compression: Investigating the Impact of Compression on Parametric Knowledge in Language Models (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing research on LLM compression focuses on general metrics like perplexity or downstream task accuracy. |
| Approach: | They propose to quantify the effect of pruning and quantization on model quality . they use the LAMA and LM-Harness benchmarks to quantify compression techniques . |
| Outcome: | The proposed compression techniques provide faster inference, smaller memory footprints, and enables local deployment. |
Text or Pixels? Evaluating Efficiency and Understanding of LLMs with Visual Text Inputs (2025.findings-emnlp)
Copied to clipboard
| Challenge: | *visual text representations* are a practical and surprisingly effective form of input compression for decoder LLMs. |
| Approach: | They exploit visual representations to render long text inputs as a single image and provide it directly to the model. |
| Outcome: | The proposed method reduces token usage while preserving performance. |
Extending Context Window of Large Language Models via Semantic Compression (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing models rely on a quadratic computation to generate long texts . current models impose limitations on the length of text inputs . |
| Approach: | They propose a semantic compression method that extends the context window of large language models . the method reduces the semantic redundancy of long inputs before passing them to the LLMs . |
| Outcome: | The proposed method extends the context window of large language models across tasks . it exhibits consistent fluency in text generation while reducing associated computational overhead. |
Token-Budget-Aware LLM Reasoning (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to enhance reasoning capabilities of large language models incur significant overhead in token usage, leading to increased costs. |
| Approach: | They propose a token-budget-aware LLM reasoning framework that adjusts the number of reasoning tokens based on the reasoning complexity of each problem. |
| Outcome: | The proposed method reduces token costs in CoT reasoning with only a slight performance reduction. |