Papers with LLaMA2
MIMIR: A Customizable Agent Tuning Platform for Enhanced Scientific Applications (2024.emnlp-demo)
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Xiangru Tang, Chunyuan Deng, Hanminwang Hanminwang, Haoran Wang, Yilun Zhao, Wenqi Shi, Yi Fung, Wangchunshu Zhou, Jiannan Cao, Heng Ji, Arman Cohan, Mark Gerstein
| Challenge: | Large language models (LLMs) have evolved into interactive agents capable of planning, tool use, and task execution across various tasks. |
| Approach: | They propose a platform that leverages large language models to generate agent-tuning data for fine-tuneing smaller, specialized models. |
| Outcome: | MIMIR enables large models to simulate various roles and create interaction data, which can then be used to fine-tune open-source models like LLaMA2. |
Enhancing Training Data Attribution for Large Language Models with Fitting Error Consideration (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) are difficult to interpret due to their black-box nature and randomness. |
| Approach: | They propose a new method which enhances influence functions by addressing fitting errors by eliminating knowledge bias present in the base model before fine-tuning. |
| Outcome: | The proposed method outperforms existing methods and achieves an average AUC of 91.64%. |
Keys to Robust Edits: From Theoretical Insights to Practical Advances (2025.acl-long)
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| Challenge: | Existing methods for modifying parametric memory are prone to inaccuracies due to conflicting or outdated information. |
| Approach: | They propose a plug-and-play module that disentangles editing keys from native model representations and dynamically adjusts keys via contrastive learning to achieve robustness-specificity balance. |
| Outcome: | The proposed method improves over robustness tests by up to 66.4% while maintaining the success rate unaffected. |