Papers with optimization techniques
KV Pareto: Systems-Level Optimization of KV Cache and Model Compression for Long Context Inference (2026.eacl-industry)
Copied to clipboard
| Challenge: | Long-context Large Language Models (LLMs) face significant memory bottlenecks due to the linear growth of key-value (KV) cache with sequence length. |
| Approach: | They propose a framework that maps the trade-off frontier between total memory consumption and task accuracy across three complementary optimization techniques. |
| Outcome: | The proposed model-specific configurations achieve 68-78% total memory reduction with minimal (1-3%) accuracy degradation on long-context tasks. |
FastSeq: Make Sequence Generation Faster (2021.acl-demo)
Copied to clipboard
Yu Yan, Fei Hu, Jiusheng Chen, Nikhil Bhendawade, Ting Ye, Yeyun Gong, Nan Duan, Desheng Cui, Bingyu Chi, Ruofei Zhang
| Challenge: | Transformer-based models have made tremendous impact in natural language generation, but inference speed is still a bottleneck due to large model size and intensive computing involved in auto-regressive decoding process. |
| Approach: | They propose an attention cache optimization, an efficient algorithm for detecting repeated n-grams, and an asynchronous generation pipeline with parallel I/O to accelerate sequence generation without loss of accuracy. |
| Outcome: | The proposed framework can accelerate the sequence generation by 4x to 9x with a simple one-line code change for a set of widely used and diverse models. |
Mitigating Dataset Artifacts in Natural Language Inference Through Automatic Contextual Data Augmentation and Learning Optimization (2022.lrec-1)
Copied to clipboard
| Challenge: | In recent years, natural language inference has been an emerging research area . a new data augmentation technique is used to augment pre-trained language models . |
| Approach: | They propose to combine automatic contextual data augmentation with a learning procedure for natural language inference. |
| Outcome: | The proposed method outperforms baseline pre-trained language models on benchmark datasets and adversarial examples. |
Augmenting Compliance-Guaranteed Customer Service Chatbots: Context-Aware Knowledge Expansion with Large Language Models (2025.emnlp-industry)
Copied to clipboard
| Challenge: | Retrieval-based chatbots leverage human-verified Q&A knowledge to deliver accurate, verifiable responses. |
| Approach: | They propose a similar question generation task for LLM training and inference to enable comprehensive semantic exploration and enhanced alignment with source question-answer relationships. |
| Outcome: | The proposed methods achieve 92% user satisfaction rate in a deployed chatbot system, reflecting an 18% improvement over the baseline. |
Beyond Instruction Optimization: Multi-Agent Error-Driven Class Description Refinement for LLM-Based Classification (2026.acl-industry)
Copied to clipboard
| Challenge: | Large Language Models have demonstrated considerable efficacy in classification tasks . however, their performance depends on two critical prompt components: Task Instructions (HOW to classify) and Class Descriptions (WHAT defines each class). |
| Approach: | They propose a multi-agent framework for iteratively refining class descriptions based on classification errors. |
| Outcome: | Empirical evaluation shows up to 20.71% accuracy improvements over static class descriptions. |
EventWeave: A Dynamic Framework for Capturing Core and Supporting Events in Dialogue Systems (2026.acl-long)
Copied to clipboard
Zhengyi Zhao, Shubo Zhang, Yiming Du, Bin Liang, Baojun Wang, Zhongyang Li, Binyang Li, Kam-Fai Wong
| Challenge: | Existing dialogue systems process conversational turns in isolation, overlooking event structures that guide natural interactions. |
| Approach: | They propose a framework that explicitly models relationships between conversational events to generate more contextually appropriate dialogue responses. |
| Outcome: | Experiments on three dialogue datasets show that the proposed approach produces more natural responses while requiring less computational overhead. |
Enhancing Language Model Hypernetworks with Restart: A Study on Optimization (2025.naacl-long)
Copied to clipboard
| Challenge: | a comprehensive investigation into optimization strategies for hypernetworks remains lacking. |
| Approach: | They propose restart optimization strategies to improve hypernetworks' performance for language models. |
| Outcome: | The proposed restart strategy improves hypernetworks' performance for language models, compared to conventional deep neural networks. |
LLaST: Improved End-to-end Speech Translation System Leveraged by Large Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | ***LLaST*** is a framework for building high-performance Large Language model based Speech-to-text Translation systems. |
| Approach: | They propose a framework for building high-performance Large Language model based Speech-to-text Translation systems. |
| Outcome: | The proposed model outperforms the CoVoST-2 benchmark and showcases exceptional scaling capabilities powered by LLMs. |
JAWAHER: A Multidialectal Dataset of Arabic Proverbs for LLM Benchmarking (2025.naacl-long)
Copied to clipboard
Samar Mohamed Magdy, Sang Yun Kwon, Fakhraddin Alwajih, Safaa Taher Abdelfadil, Shady Shehata, Muhammad Abdul-Mageed
| Challenge: | Recent advances in instruction fine-tuning and alignment methods have enhanced the adaptability of large language models to user preferences. |
| Approach: | They propose a benchmark to assess LLMs’ capacity to comprehend and interpret Arabic proverbs. |
| Outcome: | The proposed model can generate accurate translations, but struggle to produce culturally nuanced and contextually relevant explanations. |
Adaptive Spatial and Temporal Redundancy Optimization for Efficient Reasoning in Large Language Models (2026.acl-long)
Copied to clipboard
Tianle Chen, Pengyu Cheng, Qiyuan Zhu, Jiacheng Wang, Bei Liu, Hao Gu, Ruijie Shen, Xiaofeng Hou, Sirui Han, Jiacheng Liu
| Challenge: | Existing research to improve CoT efficiency falls into three categories, each with distinct limitations. |
| Approach: | They propose a training-free framework that addresses both dimensions of CoT reasoning by applying a progressive precision reduction strategy coupled with an entropy-based confidence mechanism for adaptive termination. |
| Outcome: | Empirical results show that the proposed framework achieves 11.3 efficiency gain without compromising accuracy. |
Edit Once, Update Everywhere: A Simple Framework for Cross-Lingual Knowledge Synchronization in LLMs (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to update large language models focus on single-language editing or basic multilingual editing, failing to achieve true cross-linguistic knowledge synchronization. |
| Approach: | They propose a cross-linguistic knowledge democracy edit technique to improve cross-lingual performance. |
| Outcome: | The proposed method improves cross-lingual performance while maintaining high accuracy in monolingual settings. |