Papers with influence functions
RELexED: Retrieval-Enhanced Legal Summarization with Exemplar Diversity (2025.findings-naacl)
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| Challenge: | Current approaches to legal summarization struggle with content theme deviation and inconsistent writing styles due to the content of the source document. |
| Approach: | They propose a retrieval-augmented framework that utilizes exemplar summaries along with the source document to guide the model. |
| Outcome: | The proposed model outperforms models that do not utilize exemplars and those that rely on similarity-based exemplar selection. |
Interpreting Twitter User Geolocation (2020.acl-main)
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| Challenge: | Existing methods for identifying user geolocation suffer from a lack of interpretability on the corresponding results. |
| Approach: | They adopt influence functions to interpret the behavior of GNN-based models by identifying the importance of training users when predicting locations. |
| Outcome: | The proposed method provides meaningful explanations on prediction results and also uncovers the so-called "black-box" GNN-based models by investigating the effect of individual nodes. |
SOUL: Unlocking the Power of Second-Order Optimization for LLM Unlearning (2024.emnlp-main)
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Jinghan Jia, Yihua Zhang, Yimeng Zhang, Jiancheng Liu, Bharat Runwal, James Diffenderfer, Bhavya Kailkhura, Sijia Liu
| Challenge: | Large Language Models (LLMs) have highlighted the need for effective unlearning mechanisms to comply with data regulations and ethical AI practices. |
| Approach: | They propose a second-order optimization-based LLM unlearning framework which extends the static, one-shot model update using influence unlearning to a dynamic, iterative unlearning process. |
| Outcome: | The proposed framework outperforms first-order methods across unlearning tasks, models, and metrics. |
Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions (2020.acl-main)
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| Challenge: | Modern deep learning models for NLP are notoriously opaque, and this has motivated efforts to design example-specific approaches to interpret such models. |
| Approach: | They propose to use influence functions to explain models by highlighting important words in input text to provide models with an explanation. |
| Outcome: | The proposed approach is particularly useful for natural language inference, a task in which ‘saliency maps’ may not have clear interpretation. |
Data Efficient RLVR via Off-Policy Influence Guidance (2026.acl-long)
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Erle Zhu, Dazhi Jiang, Yuan Wang, Xujun Li, Jiale Cheng, Yuxian Gu, Yilin Niu, Aohan Zeng, Jie Tang, Minlie Huang, Hongning Wang
| Challenge: | Existing data selection methods for RLVR are heuristic-based, lacking theoretical guarantees and generalizability. |
| Approach: | They propose an off-policy influence estimation method that approximates data influence using offline trajectories. |
| Outcome: | The proposed method reduces the computational cost of policy rollouts and improves storage and computation efficiency. |