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Abhijit Mishra

6 accepted papers

2025

ETF: An Entity Tracing Framework for Hallucination Detection in Code Summaries

Kishan Maharaj, Vitobha Munigala, Srikanth G. Tamilselvam, Prince Kumar, Sayandeep Sen, Palani Kodeswaran +2
ACL 2025long

Recent advancements in large language models (LLMs) have significantly enhanced their ability to understand both natural language and code, driving their use in tasks like natural language-to-code (NL2Code) and code summarisation. However, LLMs are prone to hallucination—outputs that stray from inte…

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2025

Thought2Text: Text Generation from EEG Signal using Large Language Models (LLMs)

Abhijit Mishra, Shreya Shukla, Jose Torres, Jacek Gwizdka, Shounak Roychowdhury
NAACL 2025findings

Decoding and expressing brain activity in a comprehensible form is a challenging frontier in AI. This paper presents *Thought2Text*, which uses instruction-tuned Large Language Models (LLMs) fine-tuned with EEG data to achieve this goal. The approach involves three stages: (1) training an EEG encode…

Cited by 4Source ↗PDF ↗code ↗Scholar ↗
2025

Understand the Implication: Learning to Think for Pragmatic Understanding

Settaluri Lakshmi Sravanthi, Kishan Maharaj, Sravani Gunnu, Abhijit Mishra, Pushpak Bhattacharyya
ACL 2025finding

Pragmatics, the ability to infer meaning beyond literal interpretation, is crucial for social cognition and communication. While LLMs have been benchmarked for their pragmatic understanding, improving their performance remains underexplored. Existing methods rely on annotated labels but overlook the…

Cited by 3Source ↗PDF ↗Scholar ↗
2024

SentinelLMs: Encrypted Input Adaptation and Fine-Tuning of Language Models for Private and Secure Inference

Abhijit Mishra, Mingda Li, Soham Deo
AAAI 2024technical

This paper addresses the privacy and security concerns associated with deep neural language models, which serve as crucial components in various modern AI-based applications. These models are often used after being pre-trained and fine-tuned for specific tasks, with deployment on servers accessed th…

Cited by 6Source ↗PDF ↗code ↗Scholar ↗
2023

Eyes Show the Way: Modelling Gaze Behaviour for Hallucination Detection

Kishan Maharaj, Ashita Saxena, Raja Kumar, Abhijit Mishra, Pushpak Bhattacharyya
EMNLP 2023long findings

Detecting hallucinations in natural language processing (NLP) is a critical undertaking that demands a deep understanding of both the semantic and pragmatic aspects of languages. Cognitive approaches that leverage users’ behavioural signals, such as gaze, have demonstrated effectiveness in addressin…

Cited by 7Source ↗Scholar ↗
2020

A Survey on Using Gaze Behaviour for Natural Language Processing

Sandeep Mathias, Diptesh Kanojia, Abhijit Mishra, Pushpak Bhattacharya
IJCAI 2020poster

Gaze behaviour has been used as a way to gather cognitive information for a number of years. In this paper, we discuss the use of gaze behaviour in solving different tasks in natural language processing (NLP) without having to record it at test time. This is because the collection of gaze behaviour…

Cited by 0Source ↗PDF ↗Scholar ↗