1 citations · 1 across the 2 of their papers we have counts for
5 papers
Distilling Opinions at Scale: Incremental Opinion Summarization using XL-OPSUMM
Sri Raghava Muddu, Rupasai Rangaraju, Tejpalsingh Siledar +8
Opinion summarization in e-commerce encapsulates the collective views of numerous users about a product based on their reviews. Typically, a product on an e-commerce platform has t…
Transformers are Expressive, But Are They Expressive Enough for Regression?
Swaroop Nath, Harshad Khadilkar, Pushpak Bhattacharyya
Transformers have become pivotal in Natural Language Processing, demonstrating remarkable success in applications like Machine Translation and Summarization. Given their widespread…
Leveraging Domain Knowledge for Efficient Reward Modelling in RLHF: A Case-Study in E-Commerce Opinion Summarization
Swaroop Nath, Tejpalsingh Siledar, Sankara Sri Raghava Ravindra Muddu +8
Reinforcement Learning from Human Feedback (RLHF) has become a dominating strategy in aligning Language Models (LMs) with human values/goals. The key to the strategy is learning a…
One Prompt To Rule Them All: LLMs for Opinion Summary Evaluation
Tejpalsingh Siledar, Swaroop Nath, Sankara Sri Raghava Ravindra Muddu +8
Evaluation of opinion summaries using conventional reference-based metrics rarely provides a holistic evaluation and has been shown to have a relatively low correlation with human…
Reinforcement Replaces Supervision: Query focused Summarization using Deep Reinforcement Learning
Swaroop Nath, Harshad Khadilkar, Pushpak Bhattacharyya
Query-focused Summarization (QfS) deals with systems that generate summaries from document(s) based on a query. Motivated by the insight that Reinforcement Learning (RL) provides a…