Papers by Peiji Li
FastMCTS: A Simple Sampling Strategy for Data Synthesis (2025.acl-long)
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| Challenge: | Existing methods for generating multi-step reasoning data rely on rejection sampling, which generates trajectories independently and suffers from inefficiency and imbalanced sampling across problems of varying difficulty levels. |
| Approach: | They propose a data synthesis strategy inspired by Monte Carlo Tree Search . it offers step-level evaluation signals and promotes balanced sampling . |
| Outcome: | Experiments show that FastMCTS generates 30% more correct reasoning paths than rejection sampling. |
Eliciting Implicit Acoustic Styles from Open-domain Instructions to Facilitate Fine-grained Controllable Generation of Speech (2025.emnlp-main)
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| Challenge: | Current work relies on pre-defined rules or templates to control the style of speech. |
| Approach: | They propose to use open-domain instructions to generate speech with the acoustic style that meets users’ needs based on their instructions. |
| Outcome: | The proposed model can be used to generate speech with the acoustic style that meets users’ needs based on open-domain instructions. |
UnitCoder: Scalable Code Synthesis from Pre-training Corpora (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) excel at code understanding and generation, yet code generation remains a challenge. |
| Approach: | They propose a model that supervises pre-training data quality through automatically generated unit tests while ensuring correctness via an iterative fix and refine flow. |
| Outcome: | The proposed model improves performance on a large dataset with high quality pre-training data. |
Timely Machine: Awareness of Time Makes Test-Time Scaling Agentic (2026.acl-long)
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| Challenge: | Large language models (LLMs) tackle complex reasoning tasks, but test-time scaling is becoming expensive. |
| Approach: | They propose to redefine test-time as wall-clock time, where models dynamically adjust strategies based on time budgets. |
| Outcome: | The proposed model improves time budget awareness and boosts performance across Timely-Eval. |
F-Eval: Asssessing Fundamental Abilities with Refined Evaluation Methods (2024.acl-long)
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Yu Sun, Keyuchen Keyuchen, Shujie Wang, Peiji Li, Qipeng Guo, Hang Yan, Xipeng Qiu, Xuanjing Huang, Dahua Lin
| Challenge: | Large language models (LLMs) have been evaluated for their instruction-following capabilities but lack references to their fundamental abilities. |
| Approach: | They propose a bilingual evaluation benchmark to evaluate the fundamental abilities of large language models including expression, commonsense and logic. |
| Outcome: | The proposed evaluation methods show higher correlation coefficients and larger distinction than other evaluators. |
Case2Code: Scalable Synthetic Data for Code Generation (2025.coling-main)
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Yunfan Shao, Linyang Li, Yichuan Ma, Peiji Li, Demin Song, Qinyuan Cheng, Shimin Li, Xiaonan Li, Pengyu Wang, Qipeng Guo, Hang Yan, Xipeng Qiu, Xuanjing Huang, Dahua Lin
| Challenge: | Large Language Models (LLMs) have shown outstanding breakthroughs in code generation. |
| Approach: | They propose a case-to-code induction task that exploits the expressiveness and correctness of programs by incorporating LLMs into their training. |
| Outcome: | The proposed task improves distribution case-to-code induction and various coding generation tasks. |