Papers by Kishan Maharaj
Eyes Show the Way: Modelling Gaze Behaviour for Hallucination Detection (2023.findings-emnlp)
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| Challenge: | Existing methods for hallucination detection depend on knowledge sources that are explicit such as Wikipedia or knowledge graphs. |
| Approach: | They propose a cognitive approach that leverages gaze signals from humans to detect hallucinations in natural language processing (NLP) they collect and introduce an eye tracking corpus consisting of 500 instances, annotated by five annotators for hallucinism detection. |
| Outcome: | The proposed approach achieves a balanced accuracy of 87.1% on a FactCC dataset. |
Understand the Implication: Learning to Think for Pragmatic Understanding (2025.findings-acl)
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| Challenge: | Existing methods rely on annotated labels but overlook the reasoning process humans naturally use to interpret implicit meaning. |
| Approach: | They propose a dataset that includes explicit reasoning for both correct and incorrect interpretations and propose supervised fine-tuning to improve their performance. |
| Outcome: | The proposed dataset improves LLMs' pragmatic understanding by 11.12% across model families and 16.10% over label trained models. |
Mental Disorder Classification via Temporal Representation of Text (2024.findings-emnlp)
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| Challenge: | Current methods for mental disorder prediction split data into chunks and use limited context length . mental health professionals lack the skills to diagnose and treat mental disorders . |
| Approach: | They propose a framework which compresses chronologically ordered social media posts into a series of numbers and uses this time variant representation for mental disorder classification. |
| Outcome: | The proposed framework outperforms existing models in depression, self-harm and anorexia . it also shows that the proposed framework can be used across domains . |
ETF: An Entity Tracing Framework for Hallucination Detection in Code Summaries (2025.acl-long)
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Kishan Maharaj, Vitobha Munigala, Srikanth G. Tamilselvam, Prince Kumar, Sayandeep Sen, Palani Kodeswaran, Abhijit Mishra, Pushpak Bhattacharyya
| Challenge: | Recent advances in large language models have significantly enhanced their ability to understand both natural language and code, but are prone to hallucinations. |
| Approach: | They propose a first-of-its-kind dataset, CodeSumEval, with 10K samples, curated specifically for hallucination detection in code summarisation. |
| Outcome: | The proposed framework has a 73% F1 score and is curated specifically for detection of hallucinations in code summarisation. |
Addressing Bias and Hallucination in Large Language Models (2024.lrec-tutorials)
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Nihar Ranjan Sahoo, Ashita Saxena, Kishan Maharaj, Arif A. Ahmad, Abhijit Mishra, Pushpak Bhattacharyya
| Challenge: | This tutorial provides a comprehensive overview of two critical aspects of Large Language Models: bias and hallucination. |
| Approach: | This tutorial provides an overview of two critical aspects of Large Language Models: bias and hallucination. |
| Outcome: | This tutorial delves into the complex dimensions of Large Language Models (LLMs) it outlines ethical considerations pertinent to their development and discusses hallucination, a prevalent issue in generative AI systems such as LLMs. |