Papers by Mehant Kammakomati
DocCGen: Document-based Controlled Code Generation (2024.emnlp-main)
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Sameer Pimparkhede, Mehant Kammakomati, Srikanth Tamilselvam, Prince Kumar, Ashok Kumar, Pushpak Bhattacharyya
| Challenge: | Large language models (LLMs) produce state-of-the-art performance on natural language to code generation for resource-rich general-purpose languages like C++, Java, and Python. |
| Approach: | They propose a framework that breaks the NL-to-Code generation task into two steps . they use library documentation to detect the correct libraries and schema rules extracted from the documentation to constrain the decoding . |
| Outcome: | The proposed framework improves different sized language models across all six evaluation metrics, reducing syntactic and semantic errors in structured code. |
ConCodeEval: Evaluating Large Language Models for Code Constraints in Domain-Specific Languages (2025.acl-industry)
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Mehant Kammakomati, Sameer Pimparkhede, Srikanth G. Tamilselvam, Prince Kumar, Pushpak Bhattacharyya
| Challenge: | Large Language Models (LLMs) have demonstrated potential in code generation and natural language understanding, but they struggle with code constraints. |
| Approach: | They propose to use Large Language Models to handle constraints represented in code . they use JSON, YAML, XML, Python, and natural language to test their effectiveness . |
| Outcome: | The proposed benchmark shows that LLMs can handle code constraints better than natural language . the results suggest that conscious choice of representations can lead to optimal use of LLM in enterprise use cases involving code constraints. |