ALinFiK: Learning to Approximate Linearized Future Influence Kernel for Scalable Third-Parity LLM Data Valuation (2025.naacl-long)
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Yanzhou Pan, Huawei Lin, Yide Ran, Jiamin Chen, Xiaodong Yu, Weijie Zhao, Denghui Zhang, Zhaozhuo Xu
| Challenge: | Large Language Models (LLMs) heavily rely on high-quality training data, making data valuation crucial for optimizing model performance. |
| Approach: | They propose a third-party data valuation approach that assesses the value of individual data samples and proposes a learning strategy to approximate LinFiK. |
| Outcome: | The proposed approach surpasses baselines in effectiveness and efficiency, showing significant scalability advantages as LLM parameters increase. |
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| Challenge: | Large language models (LLMs) have been widely used in various industries due to their unprecedented scale and impressive capabilities derived from the massive training dataset. |
| Approach: | They propose a framework that can estimate the influence of training data by caching and retrieval. |
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Attribute Controlled Fine-tuning for Large Language Models: A Case Study on Detoxification (2024.findings-emnlp)
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Tao Meng, Ninareh Mehrabi, Palash Goyal, Anil Ramakrishna, Aram Galstyan, Richard Zemel, Kai-Wei Chang, Rahul Gupta, Charith Peris
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LLM Braces: Straightening Out LLM Predictions with Relevant Sub-Updates (2025.acl-long)
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| Challenge: | Recent studies reveal that much of the knowledge in a Transformer-based Large Language Model (LLM) is encoded in its feed-forward (FFN) layers, where each FNN layer can be interpreted as the summation of sub-updates, each corresponding to a weighted column vector from the FFN’s value parameter matrix. |
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Improving the Language Understanding Capabilities of Large Language Models Using Reinforcement Learning (2025.findings-emnlp)
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| Challenge: | Instruction-fine-tuned large language models (LLMs) under 14B parameters underperform on NLU tasks . we explore a framework to improve the NLU capabilities of LLMs . |
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Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning (2025.emnlp-main)
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| Challenge: | Existing approaches to improve data quality face limitations in static dataset curation that fail to adapt to evolving model capabilities. |
| Approach: | They propose a self-evolving framework that uses model-aware data selection and context-preserving data refinement to improve LLM performance. |
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LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models (2023.emnlp-main)
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Zhiqiang Hu, Lei Wang, Yihuai Lan, Wanyu Xu, Ee-Peng Lim, Lidong Bing, Xing Xu, Soujanya Poria, Roy Lee
| Challenge: | Large language models (LLMs) have shown unprecedented performance across various tasks. |
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LLM-powered Data Augmentation for Enhanced Cross-lingual Performance (2023.emnlp-main)
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| Challenge: | Existing training data for multilingual commonsense reasoning datasets is limited. |
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Enhancing LLM Capabilities Beyond Scaling Up (2024.emnlp-tutorials)
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| Challenge: | general-purpose large language models (LLMs) are expanding in scale and access to unpublic training data. |
| Approach: | This tutorial aims to examine the capabilities of general-purpose large language models . authors discuss adaptation of LLMs to address conflicts, defense against attacks . |
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LLMeBench: A Flexible Framework for Accelerating LLMs Benchmarking (2024.eacl-demo)
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Fahim Dalvi, Maram Hasanain, Sabri Boughorbel, Basel Mousi, Samir Abdaljalil, Nizi Nazar, Ahmed Abdelali, Shammur Absar Chowdhury, Hamdy Mubarak, Ahmed Ali
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