Papers by Soham Poddar
Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Models (2025.naacl-long)
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| Challenge: | Large language models (LLMs) are recognized for their exceptional generative capabilities and versatility across various tasks. |
| Approach: | They conduct a comprehensive benchmarking of LLM inference energy across a wide range of NLP tasks to determine the impact of different models, tasks, prompts, and system-related factors on inference. |
| Outcome: | The proposed model energy benchmarks show that quantization and optimal batch sizes can significantly reduce energy usage. |
Legal Case Document Summarization: Extractive and Abstractive Methods and their Evaluation (2022.aacl-main)
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Abhay Shukla, Paheli Bhattacharya, Soham Poddar, Rajdeep Mukherjee, Kripabandhu Ghosh, Pawan Goyal, Saptarshi Ghosh
| Challenge: | Summarization of legal case judgement documents is a challenging problem in Legal NLP. |
| Approach: | They propose to use extractive and abstractive summarization methods to evaluate legal document summarizing systems. |
| Outcome: | The proposed methods have been evaluated on three legal summarization datasets. |
Brevity is the soul of sustainability: Characterizing LLM response lengths (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) consume significant energy and carbon emissions due to their inference processes. |
| Approach: | They first benchmark 12 decoder-only LLMs across 5 datasets and then analyze LLM responses to determine their quality. |
| Outcome: | The proposed methods can reduce the length of responses while preserving the quality of the LLMs. |
Benchmarking the Energy Savings with Speculative Decoding Strategies (2026.findings-eacl)
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Rohit Dutta, Paramita Koley, Soham Poddar, Janardan Misra, Sanjay Podder, Naveen Balani, Saptarshi Ghosh, Niloy Ganguly
| Challenge: | Existing studies on speculative decoding have focused on the energy requirements of these models, despite their utility and utility. |
| Approach: | They propose to analyze the energy requirements of speculative decoding strategies and analyze how various factors influence the energy optimizations. |
| Outcome: | The proposed approach reduces decoding time while offloading a substantial portion of the sequential generation to a smaller, more efficient model. |