Papers by Sumuk Shashidhar
Democratizing LLMs: An Exploration of Cost-Performance Trade-offs in Self-Refined Open-Source Models (2023.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have been experiencing seismic growth in size and capabilities, radically transforming the field of NLP. |
| Approach: | They propose a generalized variant of iterative self-critique and self-refinement devoid of external influence and a ranking metric to find the optimal model for a given task considering refined performance and cost. |
| Outcome: | The proposed model improves 8.2% from baseline and even with extremely small memory footprints, outperforms ChatGPT post-refinement. |
Unsupervised Human Preference Learning (2024.emnlp-main)
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| Challenge: | Existing methods for in-context learning and parameter-efficient fine-tuning fail to capture the complexity of human preferences, especially given the small, personal datasets individuals possess. |
| Approach: | They propose a method that uses small parameter models as preference agents to generate natural language rules that guide a larger, pre-trained model, enabling efficient personalization. |
| Outcome: | The proposed method outperforms baseline personalization methods on email and article datasets and significantly outperformed existing methods. |