Papers by Xiaodi Sun
DynaMaR: Dynamic Prompt with Mask Token Representation (2022.emnlp-industry)
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Xiaodi Sun, Sunny Rajagopalan, Priyanka Nigam, Weiyi Lu, Yi Xu, Iman Keivanloo, Belinda Zeng, Trishul Chilimbi
| Challenge: | Recent research shows that large language models pretrained using unsupervised approaches can achieve significant performance improvement on many downstream tasks. |
| Approach: | They propose an unsupervised approach to fine-tuning large language models using unsupervised approaches to many downstream tasks. |
| Outcome: | The proposed approach improves on four e-commerce applications and can achieve an average improvement of 10% in few-shot settings and 3.7% in data-rich settings over the standard approach. |
StraGo: Harnessing Strategic Guidance for Prompt Optimization (2024.findings-emnlp)
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Yurong Wu, Yan Gao, Bin Zhu, Zineng Zhou, Xiaodi Sun, Sheng Yang, Jian-Guang Lou, Zhiming Ding, Linjun Yang
| Challenge: | Existing methods for prompt optimization often lead to prompt drifting, wherein newly generated prompts canadversely impact previously successful cases while addressing failures. |
| Approach: | They propose a method to mitigate prompt drifting by integrating in-context learning to formulate specific, actionable strategies for prompt optimization. |
| Outcome: | The proposed approach mitigates prompt drifting by leveraging insights from both successful and failed cases to identify critical factors for achieving optimization objectives. |
AMPO: Automatic Multi-Branched Prompt Optimization (2024.emnlp-main)
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Sheng Yang, Yurong Wu, Yan Gao, Zineng Zhou, Bin Zhu, Xiaodi Sun, Jian-Guang Lou, Zhiming Ding, Anbang Hu, Yuan Fang, Yunsong Li, Junyan Chen, Linjun Yang
| Challenge: | Existing prompt engineering techniques are limited to producing single flow instructions, struggling with handling diverse patterns. |
| Approach: | They propose an automatic prompt optimization method that iteratively develops a multi-branched prompt using failure cases as feedback. |
| Outcome: | The proposed method achieves the best results across five tasks and demonstrates significant optimization efficiency due to adoption of a minimal search strategy. |
Asynchronous Convergence in Multi-Task Learning via Knowledge Distillation from Converged Tasks (2022.naacl-industry)
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Weiyi Lu, Sunny Rajagopalan, Priyanka Nigam, Jaspreet Singh, Xiaodi Sun, Yi Xu, Belinda Zeng, Trishul Chilimbi
| Challenge: | Multi-task learning (MTL) aims to solve multiple tasks by sharing a base representation among them. |
| Approach: | They propose an approach that allows for "asynchronous" convergence among the tasks where each task can converge on its own schedule. |
| Outcome: | The proposed method outperforms existing methods in two 5-task MTL setups. |