Papers by Zhaohui Li
A Semantic Feature-Wise Transformation Relation Network for Automatic Short Answer Grading (2021.emnlp-main)
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| Challenge: | Multiple choice questions can be graded automatically, but automated short answer grading is time-consuming and has bias and errors. |
| Approach: | They propose a Semantic Feature-wise transformation Relation Network that captures relational knowledge among the questions, reference answers or rubrics and labeled student answers. |
| Outcome: | The proposed model has up to 11% performance improvement over state-of-the-art approaches on the benchmark SemEval-2013 datasets, and surpasses custom approaches designed for a Kaggle challenge. |
Unearthing Gems from Stones: Policy Optimization with Negative Sample Augmentation for LLM Reasoning (2025.findings-emnlp)
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| Challenge: | Recent advances in reasoning language models have witnessed a paradigm shift from short to long CoT pattern. |
| Approach: | They propose a behavior-constrained policy gradient with negative sample augmented (BCPG-NSA) negative steps are valuable components in long CoT models, authors argue . |
| Outcome: | The proposed framework outperforms baselines on math/coding reasoning benchmarks using the same training dataset. |
Beyond the First Error: Process Reward Models for Reflective Mathematical Reasoning (2025.findings-emnlp)
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| Challenge: | Existing methods for training effective PRMs focus on the first incorrect step and all preceding steps, assuming that all subsequent steps are incorrect. |
| Approach: | They propose a data annotation method specifically designed to score the long CoT reasoning process by using an LLM-based judger for annotation. |
| Outcome: | The proposed method improves PRMs' ability to identify effective self-correction behaviors and reasoning based on erroneous steps. |
Answer-state Recurrent Relational Network (AsRRN) for Constructed Response Assessment and Feedback Grouping (2023.findings-emnlp)
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| Challenge: | STEM educators must trade off the ease of assessing selected response (SR) questions with constructed response (CR) questions, where students articulate their own reasoning. |
| Approach: | They propose to use multiple choice questions to construct response questions . they use a relational neural network to relate context, questions, reference responses, and students’ answers to a new dataset of 6,532 student responses to three, two-part CR questions. |
| Outcome: | The proposed network outperforms classifiers based on LLMs and few-shot learning with GPT-3.5 on a new dataset of 6,532 student responses to three, two-part CR questions. |
Fair-CCD: Mitigating Bias in Large Language Models for Tabular Classification Through Context-Contrastive Decoding (2026.acl-long)
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| Challenge: | Prior work to mitigate fairness issues often employs subjective demonstration selection, leading to low controllability and limited stability across different models and tasks. |
| Approach: | They propose to use in-context learning to insert social biases into large language models to create a structured and controllable representation of the relationship between sensitive attributes and predicted labels. |
| Outcome: | Extensive experiments show that Fair-CCD consistently improves fairness metrics without degrading task accuracy. |