Papers by Yuanhao Xiong
Extreme Zero-Shot Learning for Extreme Text Classification (2022.naacl-main)
Copied to clipboard
| Challenge: | Experimental results show that MACLR achieves superior performance compared to other baseline methods. |
| Approach: | They propose to pre-train Transformer-based encoders with self-supervised contrastive losses to learn the semantic embeddings of instances and labels with raw text. |
| Outcome: | The proposed method improves on the EZ-XMC model with a limited number of ground-truth positive pairs. |
ETR: Entropy Trend Reward for Efficient Chain-of-Thought Reasoning (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods to shorten CoTs use length penalties or global entropy reduction . Existing approaches to CoT reasoning have significant practical drawbacks . |
| Approach: | They propose a method that shortens CoTs by length penalties or global entropy reduction . they integrate ETR into Group Relative Policy Optimization and evaluate it . |
| Outcome: | The proposed objective improves accuracy–efficiency trade-off by +9.9% while reducing CoT length by 67% across four benchmarks. |
UNICORN: A Unified Causal Video-Oriented Language-Modeling Framework for Temporal Video-Language Tasks (2024.emnlp-main)
Copied to clipboard
Yuanhao Xiong, Yixin Nie, Haotian Liu, Boxin Wang, Jun Chen, Rong Jin, Cho-Jui Hsieh, Lorenzo Torresani, Jie Lei
| Challenge: | Recent advances in large multimodal models have encouraged the development of large multi-modal models . however, it is unclear how to extend these models to the more complex video domain . |
| Approach: | They propose a visual instruction tuning framework to address temporal video-language tasks . they collect a dataset and fine-tune the framework on instruction-following data . |
| Outcome: | The proposed model can perform better on established temporal video-language tasks without training objectives and intensive pre-training. |
AdvancedIF: Rubric-Based Benchmarking and Reinforcement Learning for Advancing LLM Instruction Following (2026.acl-long)
Copied to clipboard
Yun He, Wenzhe Li, Hejia Zhang, Songlin Li, Karishma Mandyam, Sopan Khosla, Yuanhao Xiong, Nanshu Wang, Xiaoliang Peng, Beibin Li, Shengjie Bi, Shishir G Patil, Qi Qi, Shengyu Feng, Julian Katz-Samuels, Richard Yuanzhe Pang, Sujan Kumar Gonugondla, Hunter Lang, Yue Yu, Yundi Qian, Maryam Fazel-Zarandi, Licheng Yu, Amine Benhalloum, Hany Hassan Awadalla, Manaal Faruqui
| Challenge: | Recent advances in large language models (LLMs) have shown impressive performance on a range of tasks, yet advanced instruction following (IF) remains a significant challenge. |
| Approach: | They propose a benchmark that features over 1,600 prompts and expert-curated rubrics that assess LLMs’ ability to follow complex, multi-turn, and system-level instructions. |
| Outcome: | The proposed framework improves instruction-following abilities of large language models, achieving a 6.7% gain on AdvancedIF and strong results on public benchmarks. |