Papers by Zhiqiang Rao
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. |
X-CoT: Explainable Text-to-Video Retrieval via LLM-based Chain-of-Thought Reasoning (2025.emnlp-main)
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| Challenge: | Existing text-to-video retrieval systems use embedding models for feature extraction and compute cosine similarities for ranking. |
| Approach: | They propose an explainable retrieval framework upon LLM CoT reasoning to replace embedding models for feature extraction and ranking. |
| Outcome: | The proposed retrieval framework improves retrieval performance and produces detailed rationales. |
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. |
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. |
Visual Self-Refinement for Autoregressive Models (2025.findings-emnlp)
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Jiamian Wang, Ziqi Zhou, Chaithanya Kumar Mummadi, Sohail Dianat, Majid Rabbani, Raghuveer Rao, Chen Qiu, Zhiqiang Tao
| Challenge: | Autoregressive models excel in sequential modeling but the spatial nature of visual signals conflicts with the sequential dependencies of next-token prediction, leading to suboptimal results. |
| Approach: | They propose a plug-and-play refinement module to enhance the spatial correspondence modeling within the generated visual sequence. |
| Outcome: | The proposed module enhances vision-language modeling under a shared sequential prediction framework. |