Papers by Ruishi Zou
More Samples or More Prompts? Exploring Effective Few-Shot In-Context Learning for LLMs with In-Context Sampling (2024.findings-naacl)
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Bingsheng Yao, Guiming Chen, Ruishi Zou, Yuxuan Lu, Jiachen Li, Shao Zhang, Yisi Sang, Sijia Liu, James Hendler, Dakuo Wang
| Challenge: | Existing studies on LLM prompting focus on selecting a better set of data samples inside one single prompt input, but why not design and leverage multiple ICL prompts together to further improve the LLM’s performance? |
| Approach: | They propose a low-resource LLM prompting technique to optimize the construction of multiple ICL prompt inputs to produce confident predictions. |
| Outcome: | The proposed technique can produce confident predictions by optimizing the construction of multiple ICL prompt inputs on four NLI datasets and one QA dataset. |
Towards a Design Guideline for RPA Evaluation: A Survey of Large Language Model-Based Role-Playing Agents (2025.findings-acl)
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| Challenge: | Role-Playing Agents (RPAs) are increasingly popular due to diverse task requirements and agent designs. |
| Approach: | They propose an evidence-based evaluation design guideline for LLM-based RPAs based on agent attributes, task attributes, and evaluation metrics. |
| Outcome: | The proposed evaluation design guideline is based on a systematic review of 1,676 papers published between Jan. 2021 and Dec. 2024. |