Publications (15)
Adaptive Retrieval-Augmented Generation for Conversational Systems
Xi Wang, Procheta Sen, Ruizhe Li +1
Despite the success of integrating large language models into the development of conversational systems, many studies have shown the effectiveness of retrieving and augmenting exte…
LIPEx-Locally Interpretable Probabilistic Explanations-To Look Beyond The True Class
Hongbo Zhu, Angelo Cangelosi, Procheta Sen +1
In this work, we instantiate a novel perturbation-based multi-class explanation framework, LIPEx (Locally Interpretable Probabilistic Explanation). We demonstrate that LIPEx not on…
MedFact: A Large-scale Chinese Dataset for Evidence-based Medical Fact-checking of LLM Responses
Tong Chen, Zimu Wang, Yiyi Miao +6
Medical fact-checking has become increasingly critical as more individuals seek medical information online. However, existing datasets predominantly focus on human-generated conten…
Towards Socially Responsible AI: Cognitive Bias-Aware Multi-Objective Learning
Procheta Sen, Debasis Ganguly
Human society had a long history of suffering from cognitive biases leading to social prejudices and mass injustice. The prevalent existence of cognitive biases in large volumes of…
Towards a Foundation Model for the Martian Atmosphere
Sujit Roy, Udayshankar Nair, Yuling Wu +16
The martian atmosphere hosts dynamical phenomena ranging from planet-encircling dust storms to mesoscale orographic clouds and nocturnal low-level jets. General circulation model s…
Multi-Objective Few-shot Learning for Fair Classification
Ishani Mondal, Procheta Sen, Debasis Ganguly
In this paper, we propose a general framework for mitigating the disparities of the predicted classes with respect to secondary attributes within the data (e.g., race, gender etc.)…
Task2KB: A Public Task-Oriented Knowledge Base
Procheta Sen, Xi Wang, Ruiqing Xu +1
Search engines and conversational assistants are commonly used to help users complete their every day tasks such as booking travel, cooking, etc. While there are some existing data…
Dissecting Bias in LLMs: A Mechanistic Interpretability Perspective
Bhavik Chandna, Zubair Bashir, Procheta Sen
Large Language Models (LLMs) are known to exhibit social, demographic, and gender biases, often as a consequence of the data on which they are trained. In this work, we adopt a mec…
Regularized Gradient Clipping Provably Trains Wide and Deep Neural Networks
Matteo Tucat, Anirbit Mukherjee, Procheta Sen +2
We present and analyze a novel regularized form of the gradient clipping algorithm, proving that it converges to global minima of the loss surface of deep neural networks under the…
Automated Attribute Extraction from Legal Proceedings
Subinay Adhikary, Sagnik Das, Sagnik Saha +3
The escalating number of pending cases is a growing concern world-wide. Recent advancements in digitization have opened up possibilities for leveraging artificial intelligence (AI)…
Lexical Entrainment for Conversational Systems
Zhengxiang Shi, Procheta Sen, Aldo Lipani
Conversational agents have become ubiquitous in assisting with daily tasks, and are expected to possess human-like features. One such feature is lexical entrainment (LE), a phenome…
Automated Argument Generation from Legal Facts
Oscar Tuvey, Procheta Sen
The count of pending cases has shown an exponential rise across nations (e.g., with more than 10 million pending cases in India alone). The main issue lies in the fact that the num…
Analyzing the Effect of Noise in LLM Fine-tuning
Lingfang Li, Procheta Sen
Fine-tuning is the dominant paradigm for adapting pretrained large language models (LLMs) to downstream NLP tasks. In practice, fine-tuning datasets may contain various forms of no…
A Counterfactual Explanation Framework for Retrieval Models
Bhavik Chandna, Procheta Sen
Explainability has become a crucial concern in today's world, aiming to enhance transparency in machine learning and deep learning models. Information retrieval is no exception to…
Can Word Sense Distribution Detect Semantic Changes of Words?
Xiaohang Tang, Yi Zhou, Taichi Aida +2
Semantic Change Detection (SCD) of words is an important task for various NLP applications that must make time-sensitive predictions. Some words are used over time in novel ways to…