Papers by Anku Rani
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. |
Counter Turing Test (CT2): Investigating AI-Generated Text Detection for Hindi - Ranking LLMs based on Hindi AI Detectability Index (ADI_hi) (2024.findings-emnlp)
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| Challenge: | a growing number of large language models are being used to detect AI-generated text . a recent study has found that some techniques to bypass detection are fragile . |
| Approach: | They propose to use 26 LLMs to evaluate their proficiency in generating Hindi text . they propose to introduce a Hindi AI Detectability Index to assess and rank LLM models based on their detectability levels. |
| Outcome: | The proposed methods are effective in English, but struggle in Hindi . the proposed methods show that they are susceptible to fragility . |
Use of Formal Ethical Reviews in NLP Literature: Historical Trends and Current Practices (2021.findings-acl)
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| Challenge: | Ethical aspects of research in language technologies have received much attention recently . do we observe a rise in formal ethical reviews of NLP studies? |
| Approach: | They conduct a qualitative and quantitative analysis of the ethics of NLP research . they compare the ethical reviews of NLAs to those of related disciplines . |
| Outcome: | The results compare the ACL Anthology to other related disciplines in the field . the results show that there is a heightened awareness of ethical issues that was previously lacking . |
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 . |
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. |
RADAR: A Reasoning-Guided Attribution Framework for Explainable Visual Data Analysis (2026.findings-eacl)
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| Challenge: | Multimodal Large Language Models (MLLMs) provide no visibility into which parts of visual data informed their conclusions. |
| Approach: | They propose a semi-automatic approach to attribute reasoning process by highlighting regions in charts and graphs that justify model answers. |
| Outcome: | The proposed method improves attribution accuracy by up to 15 percentage points compared to baseline methods and achieves high semantic similarity with ground truth responses. |