Papers by Run-Ze Fan
Merging Experts into One: Improving Computational Efficiency of Mixture of Experts (2023.emnlp-main)
Copied to clipboard
| Challenge: | Extensive experiments show that MEO significantly improves computational efficiency . compared to dense networks, sparsely activated networks only employ a few parameters for each input . |
| Approach: | They propose a method that merges multiple experts into one to reduce computation costs . they demonstrate that a sparse Mixture of Experts (MoE) can reduce the cost by activating a small subset of parameters for each input . |
| Outcome: | The proposed approach reduces the computational cost to that of a single expert by 83.3% compared to 82.6% in vanilla MoE. |
Reformatted Alignment (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Current methods to improve data quality are labor-intensive or prone to factual errors caused by LLM hallucinations. |
| Approach: | They propose a method which reformats the responses of instruction data into a format that better aligns with pre-established criteria and the collated evidence. |
| Outcome: | The proposed approach minimizes human annotation, hallucination, and the difficulty in scaling, remaining orthogonal to existing alignment techniques. |