Papers by Xinpeng Li
Mid-Think: Training-Free Intermediate-Budget Reasoning via Token-Level Triggers (2026.findings-acl)
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Van Yang, Shouren Wang, Debargha Ganguly, Xinpeng Li, Chaoda Song, Vikash Singh, Vipin Chaudhary, Xiaotian Han
| Challenge: | Explicit /think> tags are used to expose intermediate reasoning and enable hybrid thinking behaviors. |
| Approach: | They propose a training-free prompting format that combines these triggers to achieve intermediate-budget reasoning, outperforming fixed-token and prompt-based baselines in terms of the accuracy–length trade-off. |
| Outcome: | The proposed method outperforms fixed-token and prompt-based prompts in accuracy–length trade-offs while improving Qwen3-8B on AIME from 69.8% to 72.4% and on GPQA from 58.5% to 61.1%. |
Benchmarking and Enabling Efficient Chinese Medical Retrieval via Asymmetric Encoders (2026.acl-long)
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| 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 . |
Large-Scale and Multi-Perspective Opinion Summarization with Diverse Review Subsets (2023.findings-emnlp)
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| Challenge: | Existing methods for opinion summarization are deficient in epitomizing extensive reviews and offering opinion summaries from various angles. |
| Approach: | They propose a supervised opinion summarization framework that takes sentiment orientation into account and trains the summarizer to learn from sub-optimal and optimal review subsets. |
| Outcome: | The proposed framework generates pros, cons, and verdict summaries from hundreds of input reviews. |
Event Detection without Triggers (N19-1)
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| Challenge: | Existing approaches to event detection require annotated triggers and event types in training data. |
| Approach: | They propose a framework that encodes the representation of a sentence based on target event types. |
| Outcome: | The proposed framework achieves competitive performances compared with state-of-the-art methods. |