2 citations · 4 across the 13 of their papers we have counts for
13 papers
Inter-dimension Dependence for Multi-Dimensional Evaluation of Open-Ended Text
Haoyuan Li, Snigdha Chaturvedi
LLM-as-a-judge methods are widely used for evaluating the quality of generated open-ended text. Such evaluations are generally multi-dimensional, since the error patterns in texts…
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…
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…
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…
Fast Tree-Field Integrators: From Low Displacement Rank to Topological Transformers
Krzysztof Choromanski, Arijit Sehanobish, Somnath Basu Roy Chowdhury +4
We present a new class of fast polylog-linear algorithms based on the theory of structured matrices (in particular low displacement rank) for integrating tensor fields defined on w…
Rationale-based Opinion Summarization
Haoyuan Li, Snigdha Chaturvedi
Opinion summarization aims to generate concise summaries that present popular opinions of a large group of reviews. However, these summaries can be too generic and lack supporting…
Incremental Extractive Opinion Summarization Using Cover Trees
Somnath Basu Roy Chowdhury, Nicholas Monath, Avinava Dubey +4
Extractive opinion summarization involves automatically producing a summary of text about an entity (e.g., a product's reviews) by extracting representative sentences that capture…
Robust Concept Erasure via Kernelized Rate-Distortion Maximization
Somnath Basu Roy Chowdhury, Nicholas Monath, Avinava Dubey +2
Distributed representations provide a vector space that captures meaningful relationships between data instances. The distributed nature of these representations, however, entangle…
Affective and Dynamic Beam Search for Story Generation
Tenghao Huang, Ehsan Qasemi, Bangzheng Li +4
Storytelling's captivating potential makes it a fascinating research area, with implications for entertainment, education, therapy, and cognitive studies. In this paper, we propose…
Enhancing Group Fairness in Online Settings Using Oblique Decision Forests
Somnath Basu Roy Chowdhury, Nicholas Monath, Ahmad Beirami +4
Fairness, especially group fairness, is an important consideration in the context of machine learning systems. The most commonly adopted group fairness-enhancing techniques are in-…