Papers by Zongyao Li
Generative Annotation for ASR Named Entity Correction (2025.emnlp-main)
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Yuanchang Luo, Daimeng Wei, Shaojun Li, Hengchao Shang, Jiaxin Guo, Zongyao Li, Zhanglin Wu, Xiaoyu Chen, Zhiqiang Rao, Jinlong Yang, Hao Yang
| Challenge: | Existing named entity correction models fail to transcribe domain-speciffcnamed entities when theforms of the wrongly-transcribed words and the ground-truth entity are signiffcantly different. |
| Approach: | They propose a method that utilizes speech sound features to retrieve candidate entities . it uses speech sound feature to annotate entityerrors in ASR transcripts . |
| Outcome: | The proposed method can bring signiffcant improvement to entity accuracy. |
INarIG: Iterative Non-autoregressive Instruct Generation Model For Word-Level Auto Completion (2023.findings-emnlp)
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| Challenge: | Existing models for word-level autocompletion (WLAC) only use human typed sequences as prefixes in decoding module. |
| Approach: | They propose a novel iterative nonautoregressive instruct generation model for WLAC task . it uses human typed sequences and iterating decoding with subwords to fully utilize input information. |
| Outcome: | The proposed model is more competent in dealing with low-frequency words, and achieves state-of-the-art results on the WMT22 and benchmark datasets. |
M-Ped: Multi-Prompt Ensemble Decoding for Large Language Models (2025.findings-emnlp)
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Jiaxin Guo, Daimeng Wei, Yuanchang Luo, Hengchao Shang, Zongyao Li, Jinlong Yang, Zhanglin Wu, Zhiqiang Rao, Shimin Tao, Hao Yang
| Challenge: | a new ensemble decoding approach enhances the performance of Large Language Models. |
| Approach: | They propose a multi-prompt ensemble decoding approach to enhance LLM performance . they submit n variations of prompts with X to LLMs in batch mode to decode and derive probability distributions . |
| Outcome: | The proposed method improves pass@k rates, LENS metrics and BLEU scores on diverse NLP tasks. |
Text Style Transfer Back-Translation (2023.acl-long)
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Daimeng Wei, Zhanglin Wu, Hengchao Shang, Zongyao Li, Minghan Wang, Jiaxin Guo, Xiaoyu Chen, Zhengzhe Yu, Hao Yang
| Challenge: | Current methods require large amount of bilingual training data, which is challenging and sometimes impossible task. |
| Approach: | They propose a method to modify the style of inputs by modifying the source side of BT data. |
| Outcome: | The proposed method significantly improves translation quality against popular BT benchmarks on high-resource and low-resourced language pairs. |
A Novel Paradigm Boosting Translation Capabilities of Large Language Models (2024.findings-naacl)
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| Challenge: | Existing studies on LLMs focused on supervised fine-tuning but their effectiveness has been limited. |
| Approach: | They propose a paradigm consisting of three stages: Secondary Pre-training using extensive monolingual data, Continual Pre- training with interlinear text format documents, and Leveraging source-language consistent instruction for supervised fine-tuning. |
| Outcome: | The proposed approach surpasses previous work and achieves superior performance compared to models such as NLLB-54B(CITATION) and GPT3.5-text-davinci-003. |
Enhancing Large Language Models for Document-Level Translation Post-Editing Using Monolingual Data (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) have excellent performance in many tasks, but they still face challenges in document translation. |
| Approach: | They propose a method that leverages the capabilities of Large Language Models to optimize document translation using only monolingual data. |
| Outcome: | The proposed method improves translation quality and improves contextual consistency in document translation using only monolingual data. |
Combining the Best of Both Worlds: A Method for Hybrid NMT and LLM Translation (2025.findings-acl)
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Zhanglin Wu, Daimeng Wei, Xiaoyu Chen, Hengchao Shang, Jiaxin Guo, Zongyao Li, Yuanchang Luo, Jinlong Yang, Zhiqiang Rao, Hao Yang
| Challenge: | Large language models have advantages over neural machine translation systems, but they suffer from high computational costs and significant latency. |
| Approach: | They propose a scheduling policy that optimizes translation result while ensuring fast speed and as little LLM usage as possible. |
| Outcome: | The proposed model achieves optimal translation performance with less LLM usage on multilingual test sets. |