Papers by Kripabandhu Ghosh
Legal Judgment Reimagined: PredEx and the Rise of Intelligent AI Interpretation in Indian Courts (2024.findings-acl)
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| Challenge: | Prediction with Explanation is the largest expert-annotated dataset for legal judgment prediction and explanation in the Indian context . |
| Approach: | They propose to use an annotated legal judgment prediction corpus to improve models' accuracy . they employ transformer-based models tailored for both general and Indian legal contexts . |
| Outcome: | The proposed system improves the accuracy and explanatory depth of models for legal judgments. |
LLMs in Sarcasm Detection? It’s elementary! (Or is it?) (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) are often cited for their sophisticated pragmatic reasoning, but they collapse to random guessing on organic human speech. |
| Approach: | They propose that LLMs have near-human competence in sarcasm detection . authors propose that this proficiency may be deceptive . |
| Outcome: | The proposed model performance on synthetic leaderboards is a statistical mirage of competence. |
SELF-PERCEPT: Introspection Improves Large Language Models’ Detection of Multi-Person Mental Manipulation in Conversations (2025.acl-short)
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Danush Khanna, Pratinav Seth, Sidhaarth Sredharan Murali, Aditya Kumar Guru, Siddharth Shukla, Tanuj Tyagi, Sandeep Chaurasia, Kripabandhu Ghosh
| Challenge: | Mental manipulation is subtle yet pervasive form of abuse in interpersonal communication, making its detection critical for safeguarding potential victims. |
| Approach: | They propose a dataset of 220 multi-turn, multi-person dialogues balanced between manipulative and non-manipulative interactions drawn from reality shows that mimic real-life scenarios. |
| Outcome: | The proposed framework shows that it can detect multi-person, multi-turn mental manipulation in multi-people conversations. |
LLMs – the Good, the Bad or the Indispensable?: A Use Case on Legal Statute Prediction and Legal Judgment Prediction on Indian Court Cases (2023.findings-emnlp)
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Shaurya Vats, Atharva Zope, Somsubhra De, Anurag Sharma, Upal Bhattacharya, Shubham Nigam, Shouvik Guha, Koustav Rudra, Kripabandhu Ghosh
| Challenge: | Large Language Models have touched upon many real-life tasks. |
| Approach: | They apply Large Language Models to two popular tasks: Statute Prediction and Judgment Prediction. |
| Outcome: | The proposed model performs well in Statute Prediction and Judgment Prediction on Indian Supreme Court cases. |
Legal Case Document Summarization: Extractive and Abstractive Methods and their Evaluation (2022.aacl-main)
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Abhay Shukla, Paheli Bhattacharya, Soham Poddar, Rajdeep Mukherjee, Kripabandhu Ghosh, Pawan Goyal, Saptarshi Ghosh
| Challenge: | Summarization of legal case judgement documents is a challenging problem in Legal NLP. |
| Approach: | They propose to use extractive and abstractive summarization methods to evaluate legal document summarizing systems. |
| Outcome: | The proposed methods have been evaluated on three legal summarization datasets. |
ILDC for CJPE: Indian Legal Documents Corpus for Court Judgment Prediction and Explanation (2021.acl-long)
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Vijit Malik, Rishabh Sanjay, Shubham Kumar Nigam, Kripabandhu Ghosh, Shouvik Kumar Guha, Arnab Bhattacharya, Ashutosh Modi
| Challenge: | a system that could assist a judge in predicting the outcome of a case should be explainable. |
| Approach: | They propose to use a corpus of 35k Indian Supreme Court cases annotated with original court decisions to promote research in this area. |
| Outcome: | The proposed system has an accuracy of 78% versus 94% for human legal experts. |
Can LLMs be Literary Companions?: Analysing LLMs on Bengali Figures of Speech Identification (2025.emnlp-main)
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| Challenge: | despite Bengali being among the most spoken languages, the NLP efforts on it remain limited. |
| Approach: | They present a dataset that includes Bengali figures of speech on six poets . they deploy state-of-the-art Large Language Models to the dataset and fine-tune the best models . |
| Outcome: | The proposed dataset reveals that two open-source LLMs perform better than others in Bengali . the framework can be reproduced for English and other low-resource languages . |
Don’t Judge a Book by its Cover: Testing LLMs’ Robustness Under Logical Obfuscation (2026.eacl-long)
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| Challenge: | obfuscated questions pose significant challenges for large language models . current models parse questions without deep understanding, MIT researchers say . |
| Approach: | They propose a structure-preserving framework for logical obfuscation to test models . they use a logically equivalent framework to obliviate questions to logical equivalents . |
| Outcome: | The proposed framework is a first-of-its-kind diagnostic benchmark with 1,108 questions . obfuscation severely degrades zero-shot performance, the authors show . |
NYAYAANUMANA and INLEGALLLAMA: The Largest Indian Legal Judgment Prediction Dataset and Specialized Language Model for Enhanced Decision Analysis (2025.coling-main)
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Shubham Kumar Nigam, Deepak Patnaik Balaramamahanthi, Shivam Mishra, Noel Shallum, Kripabandhu Ghosh, Arnab Bhattacharya
| Challenge: | In India, a significant backlog of cases burdens the legal system. |
| Approach: | They present a corpus of 7,02,945 preprocessed Indian legal cases compiled for LJP . they use a domain-specific generative large language model tailored to the intricacies of the legal system . |
| Outcome: | The proposed dataset surpasses existing datasets like PredEx and ILDC, and improves prediction accuracy and comprehensible explanations. |
LegalSeg: Unlocking the Structure of Indian Legal Judgments Through Rhetorical Role Classification (2025.findings-naacl)
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Shubham Kumar Nigam, Tanmay Dubey, Govind Sharma, Noel Shallum, Kripabandhu Ghosh, Arnab Bhattacharya
| Challenge: | a lack of large-scale annotated datasets hinders effective training of ML models . despite advances in semantic segmentation, challenges persist in distinguishing between closely related roles . |
| Approach: | They propose a large annotated dataset for semantic segmentation of legal documents . they use a rhetorical role classification model to compare performance against other models . |
| Outcome: | The largest annotated dataset for this task outperforms models relying on sentence-level features. |
CryptOpiQA: A new Opinion and Question Answering dataset on Cryptocurrency (2025.coling-main)
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| Challenge: | Using a dataset of tweets and Reddit, we investigate the public opinion on cryptocurrency and bitcoin on Twitter and RedDit. |
| Approach: | They create a dataset to investigate the public opinion on cryptocurrency and bitcoin on Twitter and Reddit. |
| Outcome: | The proposed dataset contains gold standard and silver standard labels and a question-answering sub-corpus. |
Causal-LLM: A Unified One-Shot Framework for Prompt- and Data-Driven Causal Graph Discovery (2025.findings-emnlp)
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| Challenge: | Current causal discovery methods rely on pairwise or iterative strategies that fail to capture global dependencies, amplify local biases, and reduce overall accuracy. |
| Approach: | They propose a framework for one-step full causal graph discovery using prompt-based discovery and a data-driven method for settings without metadata. |
| Outcome: | The proposed framework outperforms state-of-the-art models by approximately 40% in edge accuracy on datasets like Asia and Sachs while maintaining strong performance on more complex graphs. |