2 citations · 5 across the 22 of their papers we have counts for
23 papers
Sparse Autoencoders Encode Both Concepts and Functions: The Downstream Geometry of Feature Effects
Phu Gia Hoang, Anwoy Chatterjee, Tanmoy Chakraborty +2
The wide-scale use of sparse autoencoders (SAEs) as interpretability tools is limited by inconsistent links between SAE features and model behavior. Features with clear activation…
Co-FactChecker: A Framework for Human-AI Collaborative Claim Verification Using Large Reasoning Models
Dhruv Sahnan, Subhabrata Dutta, Tanmoy Chakraborty +2
Professional fact-checkers rely on domain knowledge and deep contextual understanding to verify claims. Large language models (LLMs) and large reasoning models (LRMs) lack such gro…
The CLEF-2026 CheckThat! Lab: Advancing Multilingual Fact-Checking
Julia Maria Struß, Sebastian Schellhammer, Stefan Dietze +9
The CheckThat! lab aims to advance the development of innovative technologies combating disinformation and manipulation efforts in online communication across a multitude of langua…
Innocence in the Crossfire: Roles of Skip Connections in Jailbreaking Visual Language Models
Palash Nandi, Maithili Joshi, Tanmoy Chakraborty
Language models are highly sensitive to prompt formulations - small changes in input can drastically alter their output. This raises a critical question: To what extent can prompt…
FinChain: A Symbolic Benchmark for Verifiable Chain-of-Thought Financial Reasoning
Zhuohan Xie, Daniil Orel, Rushil Thareja +22
Multi-step symbolic reasoning is essential for robust financial analysis; yet, current benchmarks largely overlook this capability. Existing datasets such as FinQA and ConvFinQA em…
Community Moderation and the New Epistemology of Fact Checking on Social Media
Isabelle Augenstein, Michiel Bakker, Tanmoy Chakraborty +13
Social media platforms have traditionally relied on internal moderation teams and partnerships with independent fact-checking organizations to identify and flag misleading content.…
The CLEF-2025 CheckThat! Lab: Subjectivity, Fact-Checking, Claim Normalization, and Retrieval
Firoj Alam, Julia Maria Struß, Tanmoy Chakraborty +11
The CheckThat! lab aims to advance the development of innovative technologies designed to identify and counteract online disinformation and manipulation efforts across various lang…
Self-Evolved Preference Optimization for Enhancing Mathematical Reasoning in Small Language Models
Joykirat Singh, Tanmoy Chakraborty, Akshay Nambi
Large language models (LLMs) have significantly improved their reasoning capabilities; however, they still struggle with complex multi-step mathematical problem-solving due to erro…
Can LLMs Automate Fact-Checking Article Writing?
Dhruv Sahnan, David Corney, Irene Larraz +7
Automatic fact-checking aims to support professional fact-checkers by offering tools that can help speed up manual fact-checking. Yet, existing frameworks fail to address the key s…
Towards Privacy-aware Mental Health AI Models: Advances, Challenges, and Opportunities
Aishik Mandal, Tanmoy Chakraborty, Iryna Gurevych
Mental health disorders create profound personal and societal burdens, yet conventional diagnostics are resource-intensive and limit accessibility. Advances in artificial intellige…
Temporally Consistent Factuality Probing for Large Language Models
Ashutosh Bajpai, Aaryan Goyal, Atif Anwer +1
The prolific use of Large Language Models (LLMs) as an alternate knowledge base requires them to be factually consistent, necessitating both correctness and consistency traits for…
Mechanistic Behavior Editing of Language Models
Joykirat Singh, Subhabrata Dutta, Tanmoy Chakraborty
Large Language Models trained on web-scale text acquire language generation abilities that can solve a wide range of tasks, particularly when task knowledge is refined into the gen…
Can LLMs replace Neil deGrasse Tyson? Evaluating the Reliability of LLMs as Science Communicators
Prasoon Bajpai, Niladri Chatterjee, Subhabrata Dutta +1
Large Language Models (LLMs) and AI assistants driven by these models are experiencing exponential growth in usage among both expert and amateur users. In this work, we focus on ev…
Self-similarity of temporal interaction networks arises from hyperbolic geometry with time-varying curvature
Subhabrata Dutta, Dipankar Das, Tanmoy Chakraborty
The self-similarity of complex systems has been studied intensely across different domains due to its potential applications in system modeling, complexity analysis, etc., as well…
Multilingual Needle in a Haystack: Investigating Long-Context Behavior of Multilingual Large Language Models
Amey Hengle, Prasoon Bajpai, Soham Dan +1
While recent large language models (LLMs) demonstrate remarkable abilities in responding to queries in diverse languages, their ability to handle long multilingual contexts is unex…
Language Models can Exploit Cross-Task In-context Learning for Data-Scarce Novel Tasks
Anwoy Chatterjee, Eshaan Tanwar, Subhabrata Dutta +1
Large Language Models (LLMs) have transformed NLP with their remarkable In-context Learning (ICL) capabilities. Automated assistants based on LLMs are gaining popularity; however,…
MemeMQA: Multimodal Question Answering for Memes via Rationale-Based Inferencing
Siddhant Agarwal, Shivam Sharma, Preslav Nakov +1
Memes have evolved as a prevalent medium for diverse communication, ranging from humour to propaganda. With the rising popularity of image-focused content, there is a growing need…
: A Simple Society of Language Models Solves Complex Reasoning
Gurusha Juneja, Subhabrata Dutta, Tanmoy Chakraborty
Despite demonstrating emergent reasoning abilities, Large Language Models (LLMS) often lose track of complex, multi-step reasoning. Existing studies show that providing guidance vi…
How to think step-by-step: A mechanistic understanding of chain-of-thought reasoning
Subhabrata Dutta, Joykirat Singh, Soumen Chakrabarti +1
Despite superior reasoning prowess demonstrated by Large Language Models (LLMs) with Chain-of-Thought (CoT) prompting, a lack of understanding prevails around the internal mechanis…
Recent Advances in Hate Speech Moderation: Multimodality and the Role of Large Models
Ming Shan Hee, Shivam Sharma, Rui Cao +4
In the evolving landscape of online communication, moderating hate speech (HS) presents an intricate challenge, compounded by the multimodal nature of digital content. This compreh…
Frugal LMs Trained to Invoke Symbolic Solvers Achieve Parameter-Efficient Arithmetic Reasoning
Subhabrata Dutta, Joykirat Singh, Ishan Pandey +3
Large Language Models (LLM) exhibit zero-shot mathematical reasoning capacity as a behavior emergent with scale, commonly manifesting as chain-of-thoughts (CoT) reasoning. However,…
Small Language Models Fine-tuned to Coordinate Larger Language Models improve Complex Reasoning
Gurusha Juneja, Subhabrata Dutta, Soumen Chakrabarti +2
Large Language Models (LLMs) prompted to generate chain-of-thought (CoT) exhibit impressive reasoning capabilities. Recent attempts at prompt decomposition toward solving complex,…
From Chaos to Clarity: Claim Normalization to Empower Fact-Checking
Megha Sundriyal, Tanmoy Chakraborty, Preslav Nakov
With the rise of social media, users are exposed to many misleading claims. However, the pervasive noise inherent in these posts presents a challenge in identifying precise and pro…