2 citations · 2 across the 9 of their papers we have counts for
10 papers
Do LLM Tutors Actually Teach? A Process-Centered Synthesis of the Tutoring Loop
Sayantan Adak, Rima Hazra, Koyel Mukherjee +2
Large language models can produce fluent tu- toring dialogue, but whether they actually help students learn remains unclear. We present a process-centered synthesis of LLM tutoring…
ProSocialAlign: Preference Conditioned Test Time Alignment in Language Models
Somnath Banerjee, Sayan Layek, Sayantan Adak +3
Current language model safety paradigms often fall short in emotionally charged or high-stakes settings, where refusal-only approaches may alienate users and naive compliance can a…
AURA: Affordance-Understanding and Risk-aware Alignment Technique for Large Language Models
Sayantan Adak, Pratyush Chatterjee, Somnath Banerjee +3
Present day LLMs face the challenge of managing affordance-based safety risks-situations where outputs inadvertently facilitate harmful actions due to overlooked logical implicatio…
REVERSUM: A Multi-staged Retrieval-Augmented Generation Method to Enhance Wikipedia Tail Biographies through Personal Narratives
Sayantan Adak, Pauras Mangesh Meher, Paramita Das +1
Wikipedia is an invaluable resource for factual information about a wide range of entities. However, the quality of articles on less-known entities often lags behind that of the we…
RA-MTR: A Retrieval Augmented Multi-Task Reader based Approach for Inspirational Quote Extraction from Long Documents
Sayantan Adak, Animesh Mukherjee
Inspirational quotes from famous individuals are often used to convey thoughts in news articles, essays, and everyday conversations. In this paper, we propose a novel context-based…
MemeSense: An Adaptive In-Context Framework for Social Commonsense Driven Meme Moderation
Sayantan Adak, Somnath Banerjee, Rajarshi Mandal +4
Online memes are a powerful yet challenging medium for content moderation, often masking harmful intent behind humor, irony, or cultural symbolism. Conventional moderation systems…
TEXT2AFFORD: Probing Object Affordance Prediction abilities of Language Models solely from Text
Sayantan Adak, Daivik Agrawal, Animesh Mukherjee +1
We investigate the knowledge of object affordances in pre-trained language models (LMs) and pre-trained Vision-Language models (VLMs). A growing body of literature shows that PTLMs…
Placing (Historical) Facts on a Timeline: A Classification cum Coref Resolution Approach
Sayantan Adak, Altaf Ahmad, Aditya Basu +1
A timeline provides one of the most effective ways to visualize the important historical facts that occurred over a period of time, presenting the insights that may not be so appar…
cs60075_team2 at SemEval-2021 Task 1 : Lexical Complexity Prediction using Transformer-based Language Models pre-trained on various text corpora
Abhilash Nandy, Sayantan Adak, Tanurima Halder +1
This paper describes the performance of the team cs60075_team2 at SemEval 2021 Task 1 - Lexical Complexity Prediction. The main contribution of this paper is to fine-tune transform…
Mining the online infosphere: A survey
Sayantan Adak, Souvic Chakraborty, Paramtia Das +7
The evolution of AI-based system and applications had pervaded everyday life to make decisions that have momentous impact on individuals and society. With the staggering growth of…