Summary of Leveraging Domain Knowledge For Efficient Reward Modelling in Rlhf: a Case-study in E-commerce Opinion Summarization, by Swaroop Nath et al.
Leveraging Domain Knowledge for Efficient Reward Modelling in RLHF: A Case-Study in E-Commerce Opinion Summarization
by Swaroop Nath, Tejpalsingh Siledar, Sankara Sri Raghava Ravindra Muddu, Rupasai Rangaraju, Harshad Khadilkar, Pushpak Bhattacharyya, Suman Banerjee, Amey Patil, Sudhanshu Shekhar Singh, Muthusamy Chelliah, Nikesh Garera
First submitted to arxiv on: 23 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed novel approach in this paper aims to infuse domain knowledge into the reward model, reducing the need for preference annotations while maintaining state-of-the-art (SOTA) performance. The method is applied to E-Commerce Opinion Summarization, achieving a significant reduction in dataset size and ROUGE-L improvement. This development enables efficient Reinforcement Learning from Human Feedback (RLHF) and opens up avenues for adapting to applications with varying human values. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement Learning from Human Feedback helps align Language Models with human goals. However, this method requires a lot of human preference annotations. The new approach reduces the need for these annotations by infusing domain knowledge into the reward model. This makes it more efficient and suitable for different applications. The researchers tested their method on E-Commerce Opinion Summarization and got better results than before. |
Keywords
* Artificial intelligence * Reinforcement learning from human feedback * Rlhf * Rouge * Summarization
