Papers by Peiji Chen
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. |
TMATH A Dataset for Evaluating Large Language Models in Generating Educational Hints for Math Word Problems (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) are increasingly being applied in education, showing significant potential in personalized instruction, student feedback, and intelligent tutoring systems (ITSs). |
| Approach: | They propose a dataset specifically designed to evaluate LLMs’ ability to generate high-quality hints for Math Word Problems. |
| Outcome: | The proposed dataset shows that LLMs can generate more accurate and contextually appropriate educational hints for math word problems without offering direct answers. |