Papers by Soumya Ghosh
ECTSum: A New Benchmark Dataset For Bullet Point Summarization of Long Earnings Call Transcripts (2022.emnlp-main)
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Rajdeep Mukherjee, Abhinav Bohra, Akash Banerjee, Soumya Sharma, Manjunath Hegde, Afreen Shaikh, Shivani Shrivastava, Koustuv Dasgupta, Niloy Ganguly, Saptarshi Ghosh, Pawan Goyal
| Challenge: | ECTSum is a dataset for bullet-point summarization of earnings calls hosted by publicly traded companies. |
| Approach: | They propose a dataset with transcripts of earnings calls and bullet point summaries derived from Reuters articles. |
| Outcome: | The proposed dataset compares transcripts of earnings calls hosted by publicly traded companies with experts-written bullet point summaries derived from Reuters articles . |
Reliable Gradient-free and Likelihood-free Prompt Tuning (2023.findings-eacl)
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| Challenge: | Large pre-trained language models are often offered as black-box APIs due to privacy or commercial constraints. |
| Approach: | They propose to tune the soft prompts without requiring gradient computation and extend the model to include a distribution over prompts. |
| Outcome: | The proposed methods are competitive with gradient-based approaches with full access to the PLM. |
Multi-Level Explanations for Generative Language Models (2025.acl-long)
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Lucas Monteiro Paes, Dennis Wei, Hyo Jin Do, Hendrik Strobelt, Ronny Luss, Amit Dhurandhar, Manish Nagireddy, Karthikeyan Natesan Ramamurthy, Prasanna Sattigeri, Werner Geyer, Soumya Ghosh
| Challenge: | Large language models (LLMs) are being used for context-grounded tasks like summarizing meetings and answering doctors' questions. |
| Approach: | They propose a technique to provide explanations for context-grounded text generation by assigning scores to parts of the context to quantify their influence on the model output. |
| Outcome: | The proposed framework can provide more faithful explanations of generated output than available alternatives, including LLM self-explanations. |