Papers by Minghui Song
Democratizing Reasoning Ability: Tailored Learning from Large Language Model (2023.emnlp-main)
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Zhaoyang Wang, Shaohan Huang, Yuxuan Liu, Jiahai Wang, Minghui Song, Zihan Zhang, Haizhen Huang, Furu Wei, Weiwei Deng, Feng Sun, Qi Zhang
| Challenge: | Large language models (LLMs) exhibit impressive emergent abilities in natural language processing, but their democratization is hindered due to huge computation requirements and closed-source nature. |
| Approach: | They propose a tailored learning approach to distill the exclusive reasoning ability to smaller LMs to facilitate democratization. |
| Outcome: | The proposed approach enables the democratization of the exclusive reasoning ability by leveraging the black-box model as a reasoning teacher. |
Auto Search Indexer for End-to-End Document Retrieval (2023.findings-emnlp)
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| Challenge: | Generative retrieval heavily relies on the “preprocessed” document identifiers, thus limiting its retrieval performance and ability to retrieve new documents. |
| Approach: | They propose a fully end-to-end retrieval paradigm that can learn the best docids for existing and new documents automatically via a semantic indexing module. |
| Outcome: | The proposed model outperforms baselines on public and industrial datasets and can handle new documents. |
ResLoRA: Identity Residual Mapping in Low-Rank Adaption (2024.findings-acl)
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Shuhua Shi, Shaohan Huang, Minghui Song, Zhoujun Li, Zihan Zhang, Haizhen Huang, Furu Wei, Weiwei Deng, Feng Sun, Qi Zhang
| Challenge: | Low-rank adaptation (LoRA) is one of the most popular parameter-efficient fine-tuning methods. |
| Approach: | They propose a low-rank adaptation method that adds residual paths during training and merges them together during inference to achieve better results. |
| Outcome: | The proposed method achieves 2.5x faster convergence speed and improves performance by 14.3% on NLG, NLU, and text-to-image tasks. |