Papers by Srikanth Tamilselvam
Prompting with Pseudo-Code Instructions (2023.emnlp-main)
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| Challenge: | ambiguity in natural language can hinder performance of large language models. |
| Approach: | They manually create a dataset of pseudo-code prompts for 132 different classification, QA, and generative language tasks, sourced from the Super-NaturalInstructions dataset. |
| Outcome: | The pseudo-code prompts improve the performance of two LLM families, BLOOM and CodeGen. |
Read between the lines - Functionality Extraction From READMEs (2024.findings-naacl)
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| Challenge: | While text summarization is a well-known NLP task, functionality extraction from Git README files is based on a novel and useful variant of it. |
| Approach: | They propose a novel task called functionality extraction from Git README files that detects all the functionalities supported by the corresponding application software. |
| Outcome: | The proposed model outperforms baseline models that can be designed using popular black-box or white-box large language models (LLMs) such as ChatGPT and Bard. |
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. |