Papers with compression method
Pruning Pre-trained Language Models Without Fine-Tuning (2023.acl-long)
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| Challenge: | Existing methods to prune Pre-trained Language Models (PLMs) are overparameterized and require fine-tuning. |
| Approach: | They propose a pruning method that uses first-order pruning to prune PLMs while fine-tuning the remaining weights. |
| Outcome: | The proposed method outperforms first-order pruning and zero-order methods at sparsity levels. |
A Formal Perspective on Byte-Pair Encoding (2023.findings-acl)
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| Challenge: | Byte-Pair Encoding (BPE) is a popular algorithm used for tokenizing data in NLP, but the underlying optimization problem that BPE seeks to solve has not yet been laid down. |
| Approach: | They propose an algorithm which is a 1/sigma*(1-e(-sigma))-approximation of an optimal merge sequence. |
| Outcome: | The proposed algorithm improves the runtime complexity from O(NM) to O(N log M) and the lower bound of the approximation is approx0.37. |
Task-specific Compression for Multi-task Language Models using Attribution-based Pruning (2023.findings-eacl)
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| Challenge: | Existing compression methods for multi-task language models use large number of parameter parameters even when performing only a specific task. |
| Approach: | They propose a training-free compression method for multi-task language models using pruning method . they use an attribution method to determine which neurons are essential for performing a specific task . |
| Outcome: | The proposed method outperforms baseline pruning methods on six widely-used datasets. |
A Simple yet Efficient Prompt Compression Method for Text Classification Data Annotation Using LLM (2025.coling-industry)
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| Challenge: | Existing methods to improve the accuracy of large language models (LLMs) are often impractical due to high costs and time consumption. |
| Approach: | They propose a method that uses keyword extraction to reduce prompt tokens in text annotation tasks. |
| Outcome: | The proposed method reduces prompt tokens while maintaining high accuracy. |
Efficient One-shot Compression via Low-Rank Local Feature Distillation (2025.naacl-long)
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| Challenge: | Existing structured pruning approaches for large language models require calibration data and costly continued pretraining on billions of tokens to recover lost performance. |
| Approach: | They propose a method that locally distills activations with low-rank weights . they compress Mixtral-8x7B on a single GPU and Phi-2 3B by 40% . |
| Outcome: | The proposed method compresses Mixtral-8x7B on a single A100 GPU, removing 10 billion parameters while retaining over 95% of its original performance. |
Do We Really Need All Those Dimensions? An Intrinsic Evaluation Framework for Compressed Embeddings (2025.findings-emnlp)
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| Challenge: | Existing evaluation methods for compressed text embeddings are either expensive or too simplistic. |
| Approach: | They propose a task-agnostic intrinsic evaluation framework that provides a reliable proxy for downstream performance. |
| Outcome: | The proposed framework provides a reliable proxy for downstream performance. |
Beyond Perplexity: Multi-dimensional Safety Evaluation of LLM Compression (2024.findings-emnlp)
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| Challenge: | Prior work on compression prioritizes preserving perplexity, which is analogous to training loss. |
| Approach: | They examine the impact of model compression along four dimensions: degeneration harm, representational harm, dialect bias, and language modeling and downstream task performance. |
| Outcome: | The proposed compression methods can lead to unexpected consequences, the authors show . quantization preserves bias while pruning degrades quickly. |
Are Compressed Language Models Less Subgroup Robust? (2023.emnlp-main)
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| Challenge: | Existing methods to reduce model size and latency while retaining overall performance are not known about their impact on subgroup robustness. |
| Approach: | They investigate the effects of model compression on subgroup robustness of BERT language models. |
| Outcome: | The proposed compression methods do not worsen the performance on minority subgroups. |
The Pitfalls of KV Cache Compression (2026.acl-long)
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| Challenge: | Recent literature has shown minimal degradation of KV cache in multi-instruction prompts . authors show that certain instructions degrade much more rapidly with compression . |
| Approach: | They propose to change KV cache eviction policies to reduce the impact of KV evict bias . they propose to use a 'simple' evviction policy to reduce ejection bias if the LLM is a multi-instruction model . |
| Outcome: | The proposed methods show that certain instructions degrade much faster with compression, causing them to be ignored by the LLM. |