Papers by Aman Chadha
FACTIFY3M: A benchmark for multimodal fact verification with explainability through 5W Question-Answering (2023.emnlp-main)
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Megha Chakraborty, Khushbu Pahwa, Anku Rani, Shreyas Chatterjee, Dwip Dalal, Harshit Dave, Ritvik G, Preethi Gurumurthy, Adarsh Mahor, Samahriti Mukherjee, Aditya Pakala, Ishan Paul, Janvita Reddy, Arghya Sarkar, Kinjal Sensharma, Aman Chadha, Amit Sheth, Amitava Das
| Challenge: | Disinformation can cause disruption in the share market, panic and anxiety in society, and even death during crises. |
| Approach: | a new dataset is being developed to help combat disinformation . the dataset is a multimodal fake news dataset with 5W question-answering . |
| Outcome: | FACTIFY 3M is the largest dataset and benchmark for multimodal fact verification. |
From Prejudice to Parity: A New Approach to Debiasing Large Language Model Word Embeddings (2025.coling-main)
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| Challenge: | Existing work in this field has looked most commonly into gender bias, racial bias, and religious bias. |
| Approach: | They propose an algorithm that uses a neural network to perform ‘soft debiasing’ and build on the seminal work of (CITATION) and (CitATION). |
| Outcome: | The proposed algorithm outperforms current methods on gender, race, and religion metrics on a wide range of metrics. |
KnowledgePrompts: Exploring the Abilities of Large Language Models to Solve Proportional Analogies via Knowledge-Enhanced Prompting (2025.coling-main)
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Thilini Wijesiriwardene, Ruwan Wickramarachchi, Sreeram Reddy Vennam, Vinija Jain, Aman Chadha, Amitava Das, Ponnurangam Kumaraguru, Amit Sheth
| Challenge: | Proportional analogies are used to assess linguistic and cognitive abilities. |
| Approach: | They propose a dataset for proportional analogy completion and evaluate its performance in large-scale learning environments. |
| Outcome: | The proposed model achieves 55% accuracy in knowledge-enhanced prompts. |
ClaimVer: Explainable Claim-Level Verification and Evidence Attribution of Text Through Knowledge Graphs (2024.findings-emnlp)
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Preetam Prabhu Srikar Dammu, Himanshu Naidu, Mouly Dewan, YoungMin Kim, Tanya Roosta, Aman Chadha, Chirag Shah
| Challenge: | Despite the fact that many fact-checking tools lack granularity and explainability, they lack the ability to be useful in various contexts. |
| Approach: | They propose a text validation framework that provides granular explanations for each claim and localizes the specific problematic content to reduce cognitive load. |
| Outcome: | The proposed framework provides granular explanations for each claim prediction and localizes and educates users on the specific content. |
YinYang-Align: A new Benchmark for Competing Objectives and Introducing Multi-Objective Preference based Text-to-Image Alignment (2025.findings-acl)
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Amitava Das, Yaswanth Narsupalli, Gurpreet Singh, Vinija Jain, Vasu Sharma, Suranjana Trivedy, Aman Chadha, Amit Sheth
| Challenge: | Recent controversies highlight the need for robust alignment mechanisms in text-to-image systems. |
| Approach: | They propose a framework to evaluate T2I systems across six contradictory alignment objectives . objectives highlight key trade-offs such as artistic freedom and cultural sensitivity . |
| Outcome: | The proposed framework achieves superior alignment across all objectives. |
Counter Turing Test (CT2): AI-Generated Text Detection is Not as Easy as You May Think - Introducing AI Detectability Index (ADI) (2023.emnlp-main)
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Megha Chakraborty, S.M Towhidul Islam Tonmoy, S M Mehedi Zaman, Shreya Gautam, Tanay Kumar, Krish Sharma, Niyar Barman, Chandan Gupta, Vinija Jain, Aman Chadha, Amit Sheth, Amitava Das
| Challenge: | a number of issues have arisen regarding the risk and consequences of AI-generated text detection. |
| Approach: | They propose a counter-turing test to evaluate the robustness of existing AGTD methods . they propose ADI, a quantifiable spectrum to assess detectability of LLMs . |
| Outcome: | The proposed method evaluates the robustness of existing AGTD methods . it shows that larger LLMs tend to have lower ADI, indicating they are less detectable . |
On the Relationship between Sentence Analogy Identification and Sentence Structure Encoding in Large Language Models (2024.findings-eacl)
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Thilini Wijesiriwardene, Ruwan Wickramarachchi, Aishwarya Naresh Reganti, Vinija Jain, Aman Chadha, Amit Sheth, Amitava Das
| Challenge: | Analogies facilitate the transfer of meaning and knowledge from one domain to another. |
| Approach: | They propose to use large language models to encode syntactic and semantic structures of sentences to identify sentence analogies. |
| Outcome: | The LLMs which capture syntactic structures better, also have higher abilities in identifying sentence analogies. |
DPO Kernels: A Semantically-Aware, Kernel-Enhanced, and Divergence-Rich Paradigm for Direct Preference Optimization (2025.findings-acl)
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Amitava Das, Suranjana Trivedy, Danush Khanna, Yaswanth Narsupalli, Basab Ghosh, Rajarshi Roy, Gurpreet Singh, Vinija Jain, Vasu Sharma, Aishwarya Naresh Reganti, Aman Chadha
| Challenge: | Direct Preference Optimization (DPO) is a cornerstone for preference alignment but is constrained by fixed divergence measures and limited feature transformations. |
| Approach: | They propose a new enhancement of Direct Preference Optimization that integrates kernel methods to overcome these challenges. |
| Outcome: | The proposed model improves divergence measures and features by using kernels . the proposed model achieves state-of-the-art generalization in factuality, safety, reasoning, and instruction following . |
Tutorial Proposal: Hallucination in Large Language Models (2024.lrec-tutorials)
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| Challenge: | Grasping the intricacies of hallucination in LLMs can be daunting, especially for those new to the field. |
| Approach: | This tutorial aims to bridge the gap between the field and the field of hallucination . it will explore the key aspects of hallucinonation, including benchmarking, detection, and mitigation techniques . |
| Outcome: | This tutorial will explore the key aspects of hallucination in LLMs . it will also explore the specific constraints and shortcomings of current approaches . |
Alignment Quality Index (AQI) : Beyond Refusals: AQI as an Intrinsic Alignment Diagnostic via Latent Geometry, Cluster Divergence, and Layer wise Pooled Representations (2025.emnlp-main)
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Abhilekh Borah, Chhavi Sharma, Danush Khanna, Utkarsh Bhatt, Gurpreet Singh, Hasnat Md Abdullah, Raghav Kaushik Ravi, Vinija Jain, Jyoti Patel, Shubham Singh, Vasu Sharma, Arpita Vats, Rahul Raja, Aman Chadha, Amitava Das
| Challenge: | a new metric measures the quality of large language models (LLMs) that detects hidden misalignments and jailbreak risks. |
| Approach: | They propose a decoding-invariant metric that measures latent safety failures . they propose 'Alignment Quality Index' to measure latent activations in latent space . |
| Outcome: | The proposed metric detects latent safety failures overlooked by behavioral benchmarks and jailbreaks. |
Paper Circle: An Open-source Multi-agent Research Discovery and Analysis Framework (2026.acl-long)
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| Challenge: | Recent advances in large language models have demonstrated strong potential for understanding user intent . paper describes system architecture, agent roles, retrieval and scoring methods, knowledge graph schema, and evaluation interfaces . |
| Approach: | They propose a multi-agent research discovery and analysis system that integrates multiple agents to reduce the effort required to find, assess, organize, and understand academic literature. |
| Outcome: | The proposed system reduces the effort required to find, assess, organize, and understand academic literature. |
The Troubling Emergence of Hallucination in Large Language Models - An Extensive Definition, Quantification, and Prescriptive Remediations (2023.emnlp-main)
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Vipula Rawte, Swagata Chakraborty, Agnibh Pathak, Anubhav Sarkar, S.M Towhidul Islam Tonmoy, Aman Chadha, Amit Sheth, Amitava Das
| Challenge: | Recent advances in Large Language Models have generated widespread acclaim, but hallucination has also emerged as a by-product. |
| Approach: | They propose a fine-grained discourse on profiling hallucination based on its degree, orientation, and category . they categorize hallucines into six types: acronym ambiguity, generated golem, virtual voice, geographic erratum, time wrap . |
| Outcome: | The proposed method categorizes hallucination into six types based on their degree, orientation, and category . |
DM-Codec: Distilling Multimodal Representations for Speech Tokenization (2025.findings-emnlp)
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Md Mubtasim Ahasan, Md Fahim, Tasnim Mohiuddin, Akmmahbubur Rahman, Aman Chadha, Tariq Iqbal, M Ashraful Amin, Md Mofijul Islam, Amin Ahsan Ali
| Challenge: | Existing speech tokenization models lack contextual representations for speech synthesis . absence of contextual representation results in elevated WER and WIL scores . |
| Approach: | They propose a language model-guided distillation method that incorporates contextual information into a comprehensive speech tokenizer. |
| Outcome: | The proposed method outperforms state-of-the-art tokenization models in reducing WER and WIL scores. |
Enhancing Adverse Drug Event Detection with Multimodal Dataset: Corpus Creation and Model Development (2024.findings-acl)
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| Challenge: | ADEs are a serious public health concern and cost healthcare systems billions of dollars . despite advancements in healthcare, ADE detection remains a significant challenge . |
| Approach: | They propose a multimodal adverse drug event detection dataset that merges ADE-related textual information with visual aids to enhance patient safety. |
| Outcome: | The proposed dataset integrates ADE-related textual information with visual aids to improve patient safety and healthcare accessibility. |
MemeGuard: An LLM and VLM-based Framework for Advancing Content Moderation via Meme Intervention (2024.acl-long)
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| Challenge: | Existing studies on content moderation of toxic memes focus on text-based content . current research neglects the widespread influence of multimodal content like memes . |
| Approach: | They propose a framework leveraging Large Language Models and Visual Language Model (VLMs) for meme intervention. |
| Outcome: | The proposed framework enables users to generate relevant and effective responses to toxic memes. |
Can Large Language Models Infer Causal Relationships from Real-World Text? (2026.acl-long)
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| Challenge: | Existing work evaluating large language models relies on synthetic or simplified texts with explicit causal relationships. |
| Approach: | They develop a benchmark to evaluate LLMs' ability to infer causal relationships from texts . they use a dataset of texts with different levels of explicitness and complexity . |
| Outcome: | The proposed benchmark is the first-ever real-world dataset for this task. |
Generative Data Augmentation using LLMs improves Distributional Robustness in Question Answering (2024.eacl-srw)
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| Challenge: | Existing domain adaptation methods do not account for unseen natural distribution shifts. |
| Approach: | They perform experiments on 4 different datasets under varying amounts of distribution shift . they analyze how "in-the-wild" generation can help achieve domain generalization . |
| Outcome: | The proposed approach augments reading comprehension datasets with generated data to improve robustness towards natural distribution shifts. |
SEPSIS: I Can Catch Your Lies – A New Paradigm for Deception Detection (2025.acl-srw)
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Anku Rani, Dwip Dalal, Shreya Gautam, Pankaj Gupta, Vinija Jain, Aman Chadha, Amit Sheth, Amitava Das
| Challenge: | a new framework categorizes deception into three forms: lies of omission, lies of commission, and lies of influence . a novel framework for deception detection leveraging NLP techniques is proposed . |
| Approach: | They propose a framework that categorizes deception into three forms: lies of omission, lies of commission, and lies of influence. |
| Outcome: | The proposed framework achieves an impressive F1 score of 0.87 across all layers . it can be used to investigate lies of omission, lies of commission and lies of influence . |
Are Personalized Stochastic Parrots More Dangerous? Evaluating Persona Biases in Dialogue Systems (2023.findings-emnlp)
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| Challenge: | Recent advances in Large Language Models enable them to follow freeform instructions, including imitating generic or specific demographic personas in conversations. |
| Approach: | They propose to investigate persona biases by experimenting with UNIVERSALPERSONA, a model that incorporates both generic and specific personas. |
| Outcome: | The proposed model systematically measures persona biases in harmful expression and harmful agreement. |
FACTIFY-5WQA: 5W Aspect-based Fact Verification through Question Answering (2023.acl-long)
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Anku Rani, S.M Towhidul Islam Tonmoy, Dwip Dalal, Shreya Gautam, Megha Chakraborty, Aman Chadha, Amit Sheth, Amitava Das
| Challenge: | Contemporary fact-checking systems focus on estimating truthfulness using numerical scores which are not human-interpretable. |
| Approach: | They propose a 5W framework for question-answer-based fact explainability that can assist human fact-checkers in asking relevant questions . they propose masked language model which generates QA pairs for claims and a baseline QA system that automatically locates those answers from evidence documents. |
| Outcome: | The proposed framework can assist human fact-checkers in asking relevant questions related to a fact, which can then be validated separately to reach a final verdict. |
Can LLMs Augment Low-Resource Reading Comprehension Datasets? Opportunities and Challenges (2024.acl-srw)
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| Challenge: | Large Language Models (LLMs) have demonstrated impressive zero-shot performance on a wide range of NLP tasks. |
| Approach: | They propose to use large language models to augment extractive reading comprehension datasets by fine-tuning their annotations and comparing their performance to human annotators. |
| Outcome: | The proposed model can be used to augment extractive reading comprehension datasets. |
ANALOGICAL - A Novel Benchmark for Long Text Analogy Evaluation in Large Language Models (2023.findings-acl)
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Thilini Wijesiriwardene, Ruwan Wickramarachchi, Bimal Gajera, Shreeyash Gowaikar, Chandan Gupta, Aman Chadha, Aishwarya Naresh Reganti, Amit Sheth, Amitava Das
| Challenge: | Modern large language models are evaluated on extrinsic measures based on benchmarks such as GLUE and SuperGLUE. |
| Approach: | They propose a benchmark to intrinsically evaluate large language models across a taxonomy of analogies of long text with six levels of complexity. |
| Outcome: | The proposed benchmark evaluates LLMs across a taxonomy of analogies of long text with six levels of complexity. |
A Comprehensive Survey of Hallucination in Large Language, Image, Video and Audio Foundation Models (2024.findings-emnlp)
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| Challenge: | a growing need to understand and alleviate FMs' propensity to produce hallucinated outputs, especially in high-stakes applications. |
| Approach: | They propose a framework for detecting and mitigating hallucination in FMs . they synthesize recent advancements in detection and mitigation techniques . |
| Outcome: | The proposed framework provides valuable insights for researchers, developers, and practitioners. |