Papers by Sayandeep Sen
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. |
Transforming Code Understanding: Clustering-Based Retrieval for Improved Summarization in Domain-Specific Languages (2025.coling-industry)
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Baban Gain, Dibyanayan Bandyopadhyay, Samrat Mukherjee, Aryan Sahoo, Saswati Dana, Palanivel Kodeswaran, Sayandeep Sen, Asif Ekbal, Dinesh Garg
| Challenge: | Existing natural language summaries of domain-specific languages are limited due to their recency and complexity. |
| Approach: | They propose a clustering-based technique to retrieve in-context examples that are semantically closer to the test example and propose eBPF prompt generation technique that yields superior-quality code summary generation. |
| Outcome: | The proposed method improves the eBPF code summarization accuracy by 12.9 BLEU points over other prompting techniques. |