Papers by Chenming Wu
Ungrammatical-syntax-based In-context Example Selection for Grammatical Error Correction (2024.naacl-long)
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| Challenge: | In-context learning (ICL) has shown impressive results on many tasks, but applying LLMs to grammatical error correction (GEC) is still a challenging task. |
| Approach: | They propose an ungrammatical-syntax-based in-context example selection strategy that measures similarity of sentences based on their syntactic structures and identify optimal ICL examples sharing the most similar ill-formed syntax to the test input. |
| Outcome: | The proposed strategy outperforms word-matching and semantics-based methods on a syntax-oriented task like GEC on benchmark English datasets. |
Think Outside the Policy: In-Context Steered Policy Optimization (2026.findings-acl)
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| Challenge: | Existing Reinforcement Learning from Verifiable Rewards (RLVR) methods exhibit limited exploration due to reliance on on-policy rollouts which are limited to the current policy’s distribution, resulting in narrow trajectory diversity. |
| Approach: | They propose a framework that leverages the in-context learning capability of Large Reasoning Models to provide expert guidance using existing datasets. |
| Outcome: | The proposed framework improves RLVR performance and training stability on mathematical reasoning benchmarks. |
Digging Errors in NMT: Evaluating and Understanding Model Errors from Partial Hypothesis Space (2022.emnlp-main)
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| Challenge: | Current evaluation of neural machine translation systems is limited by one best hypothesis and search errors brought by heuristic decoding algorithms. |
| Approach: | They propose a new evaluation protocol which defines model errors with model’s ranking capability over hypothesis space and Monte Carlo sampling evaluation to tackle the problem of exponentially large space. |
| Outcome: | The proposed evaluation protocol is consistent with what is currently used in the field and is consistent to what is being proposed. |
Do Not Step Into the Same River Twice: Learning to Reason from Trial and Error (2026.acl-long)
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| Challenge: | Existing approaches to RLVR train LMs based on their own on-policy responses and are constrained by the initial capability of LM. |
| Approach: | They propose an approach that hints LMs with their self-made mistakes without external guidance. |
| Outcome: | The proposed approach outperforms the normal group relative policy optimization and requires no external guidance. |
Aligning Language Models with Real-time Knowledge Editing (2026.acl-long)
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| Challenge: | Mainstream knowledge editing methods are static and fail to keep pace with the evolving real-world knowledge. |
| Approach: | They propose a new paradigm for knowledge editing that integrates edit augmentation and self-adaptive post-alignment inference into CRAFT to improve edit success. |
| Outcome: | The proposed method shows significant performance gain on CRAFT and traditional datasets compared to existing methods. |
Are Pre-trained Language Models Useful for Model Ensemble in Chinese Grammatical Error Correction? (2023.acl-short)
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| Challenge: | Model ensemble has been in widespread use for Grammatical Error Correction (GEC), boosting model performance. |
| Approach: | They propose to use model ensembles computed by pre-trained language models to improve model performance. |
| Outcome: | The proposed ensembles do not improve but get worse after the PLM-based ensemble. |
Large Language Models Might Not Care What You Are Saying: Prompt Format Beats Descriptions (2025.findings-emnlp)
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| Challenge: | In-context learning has improved performance of large language models, but descriptive instructions are still under-explored. |
| Approach: | They propose an ensemble prompt framework to describe selection criteria of multiple in-context examples. preliminary experiments on machine translation confirm that this framework boosts ICL performance. |
| Outcome: | The proposed framework improves on commonsense, math, logical reasoning and hallucination tasks with three LLMs. |
SCOI: Syntax-augmented Coverage-based In-context Example Selection for Machine Translation (2024.emnlp-main)
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| Challenge: | In-context learning improves performance of large language models (LLMs) performance of ICL highly depends on quality of demonstrations . |
| Approach: | They propose a syntactic-augmented COverage-based In-context example selection strategy that leverages syntastic knowledge beyond word matching to select better examples for machine translation. |
| Outcome: | The proposed strategy obtains the highest average COMET score among learning-free methods. |