2 citations · 6 across the 12 of their papers we have counts for
12 papers
Can Hallucinations Be Useful? Solving Multi-Hop Questions With SLMs By Chaining System-I/II Reasoning
Saptarshi Sengupta, Suhang Wang
Recently, there has been increased interest in Small Language Models (SLMs), which are fast, show good performance, and have lower hardware demands than large language models (LLMs…
GaLoRA: Parameter-Efficient Graph-Aware LLMs for Node Classification
Mayur Choudhary, Saptarshi Sengupta, Katerina Potika
The rapid rise of large language models (LLMs) and their ability to capture semantic relationships has led to their adoption in a wide range of applications. Text-attributed graphs…
ToolDreamer: Instilling LLM Reasoning Into Tool Retrievers
Saptarshi Sengupta, Zhengyu Zhou, Jun Araki +4
Tool calling has become increasingly popular for Large Language Models (LLMs). However, for large tool sets, the resulting tokens would exceed the LLM's context window limit, makin…
Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs
Jaydip Sen, Saptarshi Sengupta, Subhasis Dasgupta
This chapter explores advancements in decoding strategies for large language models (LLMs), focusing on enhancing the Locally Typical Sampling (LTS) algorithm. Traditional decoding…
BioMol-MQA: A Multi-Modal Question Answering Dataset For LLM Reasoning Over Bio-Molecular Interactions
Saptarshi Sengupta, Shuhua Yang, Paul Kwong Yu +2
Retrieval augmented generation (RAG) has shown great power in improving Large Language Models (LLMs). However, most existing RAG-based LLMs are dedicated to retrieving single modal…
MAG-V: A Multi-Agent Framework for Synthetic Data Generation and Verification
Saptarshi Sengupta, Harsh Vashistha, Kristal Curtis +4
Extending the capabilities of Large Language Models (LLMs) with functions or tools for environment interaction has led to the emergence of the agent paradigm. In industry, training…
Exploring Language Model Generalization in Low-Resource Extractive QA
Saptarshi Sengupta, Wenpeng Yin, Preslav Nakov +2
In this paper, we investigate Extractive Question Answering (EQA) with Large Language Models (LLMs) under domain drift, i.e., can LLMs generalize to domains that require specific k…
Milestones in Bengali Sentiment Analysis leveraging Transformer-models: Fundamentals, Challenges and Future Directions
Saptarshi Sengupta, Shreya Ghosh, Prasenjit Mitra +1
Sentiment Analysis (SA) refers to the task of associating a view polarity (usually, positive, negative, or neutral; or even fine-grained such as slightly angry, sad, etc.) to a giv…
Towards Efficient Methods in Medical Question Answering using Knowledge Graph Embeddings
Saptarshi Sengupta, Connor Heaton, Suhan Cui +2
In Natural Language Processing (NLP), Machine Reading Comprehension (MRC) is the task of answering a question based on a given context. To handle questions in the medical domain, m…
TOP-Training: Target-Oriented Pretraining for Medical Extractive Question Answering
Saptarshi Sengupta, Connor Heaton, Shreya Ghosh +3
We study extractive question-answering in the medical domain (Medical-EQA). This problem has two main challenges: (i) domain specificity, as most AI models lack necessary domain kn…
Spatio-temporal Storytelling? Leveraging Generative Models for Semantic Trajectory Analysis
Shreya Ghosh, Saptarshi Sengupta, Prasenjit Mitra
In this paper, we lay out a vision for analysing semantic trajectory traces and generating synthetic semantic trajectory data (SSTs) using generative language model. Leveraging the…
Investigating Antigram Behaviour using Distributional Semantics
Saptarshi Sengupta
The field of computational linguistics constantly presents new challenges and topics for research. Whether it be analyzing word usage changes over time or identifying relationships…