Papers with memory cost
Late Prompt Tuning: A Late Prompt Could Be Better Than Many Prompts (2022.findings-emnlp)
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| Challenge: | Prompt tuning is parameter-efficient but lags behind other state-of-the-art methods. |
| Approach: | They propose a parameter-efficient tuning method that only optimizes a soft prompt to adapt PTMs to downstream tasks. |
| Outcome: | The proposed method is parameter-efficient but lags behind other state-of-the-art methods. |
Efficient Passage Retrieval with Hashing for Open-domain Question Answering (2021.acl-short)
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| Challenge: | Open-domain question answering systems often require large memory to run because of the massive size of their passage index. |
| Approach: | They propose a memory-efficient neural retrieval model that integrates a learning-to-hash technique into the state-of-the-art Dense Passage Retriever to represent the passage index using compact binary codes. |
| Outcome: | The proposed model significantly reduces memory cost from 65GB to 2GB without loss of accuracy on two open-domain question answering benchmarks. |
CoLA: Compute-Efficient Pre-Training of LLMs via Low-Rank Activation (2025.emnlp-main)
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Ziyue Liu, Ruijie Zhang, Zhengyang Wang, Mingsong Yan, Zi Yang, Paul D. Hovland, Bogdan Nicolae, Franck Cappello, Sui Tang, Zheng Zhang
| Challenge: | Large foundation models have become huge, but they consume computational resources in pretraining. |
| Approach: | They propose to replace full-size layers with compute-efficient auto-encoders that enforce low-rank activations throughout training. |
| Outcome: | The proposed method reduces the computing cost by 2pmbtimes and improves training throughput by 1.86pmtime. |
QuZO: Quantized Zeroth-Order Fine-Tuning for Large Language Models (2025.emnlp-main)
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Jiajun Zhou, Yifan Yang, Kai Zhen, Ziyue Liu, Yequan Zhao, Ershad Banijamali, Athanasios Mouchtaris, Ngai Wong, Zheng Zhang
| Challenge: | Large Language Models (LLMs) are quantized to lower precision to reduce memory cost and latency in inference. |
| Approach: | They propose a quantized zeroth-order framework for fine-tuning Large Language Models (LLMs) using low-precision forward passes. |
| Outcome: | The proposed method achieves comparable results to first-order methods in FP8 and superior accuracy in INT8 and INT4 training. |
Compressing Context to Enhance Inference Efficiency of Large Language Models (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) have demonstrated remarkable power and impressive generalisation abilities across various tasks. |
| Approach: | They propose a method that prunes redundancies in the input context to make the input more compact. |
| Outcome: | The proposed method reduces memory and inference time while maintaining comparable performance compared to full context. |
Efficient KL Divergence Estimation via Truncated Top-K Integration for Large Language Models (2026.acl-long)
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| Challenge: | Existing methods for estimating KL divergence using only top-k tokens suffer from high variance or systematic bias. |
| Approach: | They propose a top-k Importance-weighted KL Estimator that exploits the Zipfian structure of language model distributions by integrating only the top-K tokens. |
| Outcome: | The proposed estimator outperforms existing estimators on multiple benchmarks while exhibiting lower variance. |
CAMERO: Consistency Regularized Ensemble of Perturbed Language Models with Weight Sharing (2022.acl-long)
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| Challenge: | Existing work has resorted to sharing weights among models, but results are not affordable for real-world deployment. |
| Approach: | They propose a consistency-regularized ensemble learning approach based on perturbed models to retain ensemble benefits while maintaining a low memory cost. |
| Outcome: | The proposed approach outperforms the standard ensemble of 8 BERT-base models on the GLUE benchmark by 0.7 with a significantly smaller model size. |
Text Anomaly Detection with Simplified Isolation Kernel (2025.findings-emnlp)
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| Challenge: | High-dimensional dense embeddings extracted by large language models pose memory requirements and high computation time. |
| Approach: | They propose a method that maps high-dimensional dense embeddings to lower-dimensional sparse representations while preserving crucial anomaly characteristics. |
| Outcome: | The proposed method achieves better detection performance than 11 SOTA anomaly detection algorithms while maintaining computational efficiency and low memory cost. |
A Survey for Efficient Open Domain Question Answering (2023.acl-long)
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| Challenge: | Open domain question answering (ODQA) is a longstanding task that can answer factoid questions without explicit evidence in natural language processing (NLP). |
| Approach: | They propose to use open domain question answering to answer factual questions from a large knowledge corpus without explicit evidence. |
| Outcome: | The proposed models can answer factoid questions from a large knowledge corpus without explicit evidence. |
Focus on the Core: Efficient Attention via Pruned Token Compression for Document Classification (2023.findings-emnlp)
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| Challenge: | Pre-trained transformers suffer from a computationally expensive self-attention mechanism that interacts with all tokens, including those unfavorable to classification performance. |
| Approach: | They propose to integrate token pruning and token combining strategies to improve model performance and reduce computational demands. |
| Outcome: | Experiments with various datasets show that the proposed model performs better than baseline models, with the best improvement over the existing model. |
What Makes Language Models Good-enough? (2024.findings-acl)
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| Challenge: | Psycholinguistic research suggests that humans may build a representation of linguistic input that is ‘good-enough’ for the task at hand. |
| Approach: | They create a good-enough language processing evaluation dataset to test the effects of two plausibility types, eight construction types, and three degrees of memory cost on language processing. |
| Outcome: | The proposed model evaluation against the annotated GELP reveals that models with shallower depth and fewer heads exhibit good-enough performance. |
Attention Score is not All You Need for Token Importance Indicator in KV Cache Reduction: Value Also Matters (2024.emnlp-main)
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| Challenge: | Recent studies have focused on scaling the context size of large language models (LLMs) however, the enormous inference costs of LLMs limit their applications. |
| Approach: | They propose a method which uses attention scores and the l 1 norm to evaluate token importance. |
| Outcome: | Extensive experiments on LLaMA2-7B-chat and Vicuna-v1.5-7B show that the proposed method outperforms attention-score-only baselines in over 12 tasks. |
Memory-Efficient Fine-Tuning of Transformers via Token Selection (2024.emnlp-main)
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| Challenge: | Existing methods for fine-tuning require caching of intermediate activations to update weights during the backward pass. |
| Approach: | They develop a method to reduce memory usage in fine-tuning of transformers by backpropagating through just a subset of input tokens. |
| Outcome: | The proposed method reduces memory usage and memory footprint on large transformer models . it can be easily combined with existing methods like LoRA, reducing memory cost . |