Papers by Yongcan Wang
Benchmarking and Enabling Efficient Chinese Medical Retrieval via Asymmetric Encoders (2026.acl-long)
Copied to clipboard
| Challenge: | Effective medical text retrieval requires high accuracy and low latency. |
| Approach: | They propose a benchmark for medical text retrieval in Chinese using a symmetric architecture . CARE is a lightweight encoder with an LLM-based encoder for offline document encoding . |
| Outcome: | The proposed benchmark surpasses state-of-the-art symmetric models on CMedTEB . it matches high retrieval quality without increasing latency, and it performs well on a single GPU . |
Understanding and Mitigating Spurious Signal Amplification in Test-Time Reinforcement Learning for Math Reasoning (2026.findings-acl)
Copied to clipboard
| Challenge: | a framework to mitigate spurious optimization signals is proposed for test-time reinforcement learning (TTRL) Reinforcement learning with verifiable rewards (RLVR) is an effective paradigm for improving large language models on structured challenging reasoning tasks. |
| Approach: | They propose a framework to mitigate spurious optimization signals from label noise . they propose to use a frequency-based sampling strategy to exclude ambiguous samples . |
| Outcome: | The proposed framework outperforms existing TTRL baselines on three large language models across multiple mathematical reasoning benchmarks. |
Generative Input: Towards Next-Generation Input Methods Paradigm (2024.findings-acl)
Copied to clipboard
| Challenge: | generative models have been used for various NLP tasks but their application in the field of input methods remains under-explored. |
| Approach: | They propose a novel Generative Input paradigm that uses prompts to handle all input scenarios and other intelligent auxiliary input functions, optimizing the model with user feedback. |
| Outcome: | The proposed paradigm achieves state-of-the-art in the Full-mode Key-sequence to Characters task and surpasses GPT-4 in the other input methods. |