Papers by Zitong Yang
Incomplete Utterance Rewriting by A Two-Phase Locate-and-Fill Regime (2023.findings-acl)
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| Challenge: | Existing models with incomplete utterances have too large search space, resulting in poor quality of rewriting results. |
| Approach: | They propose a 2-phase rewriting framework which predicts empty slots in the utterance that need to be completed and generates the part to be filled into each position. |
| Outcome: | The proposed framework achieves state-of-the-art results on several public rewriting datasets. |
s1: Simple test-time scaling (2025.emnlp-main)
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Niklas Muennighoff, Zitong Yang, Weijia Shi, Xiang Lisa Li, Li Fei-Fei, Hannaneh Hajishirzi, Luke Zettlemoyer, Percy Liang, Emmanuel Candes, Tatsunori Hashimoto
| Challenge: | OpenAI’s o1 model showed this capability but did not publicly share its methodology, leading to many replication efforts. |
| Approach: | They curate a small dataset s1K with 1,000 reasoning questions based on three criteria we validate through ablations: difficulty, diversity, and quality. |
| Outcome: | The proposed model exceeds o1-preview on competition math questions by up to 27% (MATH and AIME24). |
ChatMatch: Evaluating Chatbots by Autonomous Chat Tournaments (2022.acl-long)
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| Challenge: | Existing automated evaluation systems of chatbots rely on static chat scripts as ground truth, which is hard to obtain. |
| Approach: | They propose an interactive chatbot evaluation framework that allows chatbots to compete with each other like in a sports tournament. |
| Outcome: | The proposed framework can rank chatbots independently from their model architectures and domains . existing evaluation systems rely on static chat scripts as ground truth . |