Prince Kumar — accepted AI-conference 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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2024

DocCGen: Document-based Controlled Code Generation

Sameer Pimparkhede, Mehant Kammakomati, Srikanth G. Tamilselvam, Prince Kumar, Ashok Pon Kumar, Pushpak Bhattacharyya

EMNLP 2024main

Recent developments show that Large Language Models (LLMs) produce state-of-the-art performance on natural language (NL) to code generation for resource-rich general-purpose languages like C++, Java, and Python. However, their practical usage for structured domain-specific languages (DSLs) such as Y…

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2024

Read between the lines - Functionality Extraction From READMEs

Prince Kumar, Srikanth Tamilselvam, Dinesh Garg

NAACL 2024findings

While text summarization is a well-known NLP task, in this paper, we introduce a novel and useful variant of it called functionality extraction from Git README files. Though this task is a text2text generation at an abstract level, it involves its own peculiarities and challenges making existing tex…

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2023

Prompting with Pseudo-Code Instructions

Mayank Mishra, Prince Kumar, Riyaz Ahmad Bhat, Rudra Murthy, Danish Contractor, Srikanth G. Tamilselvam

EMNLP 2023long main

Prompting with natural language instructions has recently emerged as a popular method of harnessing the capabilities of large language models (LLM). Given the inherent ambiguity present in natural language, it is intuitive to consider the possible advantages of prompting with less ambiguous prompt s…

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