7 papers
Comparative Personalization for Multi-document Summarization
Haoyuan Li, Snigdha Chaturvedi
Personalized multi-document summarization (MDS) is essential for meeting individual user preferences of writing style and content focus for summaries. In this paper, we propose tha…
Improving Fairness of Large Language Models in Multi-document Summarization
Haoyuan Li, Rui Zhang, Snigdha Chaturvedi
Fairness in multi-document summarization (MDS) is crucial for providing comprehensive views across documents with diverse social attribute values, which can significantly impact de…
Fundamental Limits of Perfect Concept Erasure
Somnath Basu Roy Chowdhury, Avinava Dubey, Ahmad Beirami +4
Concept erasure is the task of erasing information about a concept (e.g., gender or race) from a representation set while retaining the maximum possible utility -- information from…
Coverage-based Fairness in Multi-document Summarization
Haoyuan Li, Yusen Zhang, Rui Zhang +1
Fairness in multi-document summarization (MDS) measures whether a system can generate a summary fairly representing information from documents with different social attribute value…
Towards Scalable Exact Machine Unlearning Using Parameter-Efficient Fine-Tuning
Somnath Basu Roy Chowdhury, Krzysztof Choromanski, Arijit Sehanobish +2
Machine unlearning is the process of efficiently removing the influence of a training data instance from a trained machine learning model without retraining it from scratch. A popu…
Structured Unrestricted-Rank Matrices for Parameter Efficient Fine-tuning
Arijit Sehanobish, Avinava Dubey, Krzysztof Choromanski +4
Recent efforts to scale Transformer models have demonstrated rapid progress across a wide range of tasks (Wei et al., 2022). However, fine-tuning these models for downstream tasks…