Papers by Jintao Du
Mirror-Consistency: Harnessing Inconsistency in Majority Voting (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) are a widely-used decoding strategy that relies on the plurality voting rule, which focuses on the most frequent answer while overlooking all other minority responses. |
| Approach: | They propose to incorporate a ‘reflective mirror’ into the self-ensemble decoding process and enables LLMs to critically examine inconsistencies among multiple generations. |
| Outcome: | The proposed method incorporates a ‘reflective mirror’ into the self-ensemble decoding process and enables LLMs to critically examine inconsistencies among multiple generations. |
Gumbel Reranking: Differentiable End-to-End Reranker Optimization (2025.acl-long)
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Siyuan Huang, Zhiyuan Ma, Jintao Du, Changhua Meng, Weiqiang Wang, Jingwen Leng, Minyi Guo, Zhouhan Lin
| Challenge: | Existing distillation-based approaches suffer from training-inference misalignment and fail to capture interdependencies among candidate documents. |
| Approach: | They propose a method to optimize rerankers by learning a stochastic, document-wise Top-k attention mask using the Gumbel Trick and Relaxed Top-K Sampling. |
| Outcome: | The proposed framework minimizes the overall language loss and improves recall on hotpotQA. |
TransAdv: A Translation-based Adversarial Learning Framework for Zero-Resource Cross-Lingual Named Entity Recognition (2022.findings-emnlp)
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| Challenge: | Existing methods for named entity recognition are limited by noise in translation . Existing approaches to named entities recognition are mainly based on labeled data . |
| Approach: | They propose a framework to mitigate lexical and syntactic errors of translated data . they propose to use multi-level adversarial learning and multi-model knowledge distillation to mitigate noise . |
| Outcome: | The proposed framework mitigates lexical and syntactic errors of translated data . it achieves competitive performance to state-of-the-art models . |
Training LLMs to be Better Text Embedders through Bidirectional Reconstruction (2025.emnlp-main)
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Chang Su, Dengliang Shi, Siyuan Huang, Jintao Du, Changhua Meng, Yu Cheng, Weiqiang Wang, Zhouhan Lin
| Challenge: | Existing text embedding approaches often leverage the embeddment of the final token, typically a reserved special token such as ‘[EOS]‘. |
| Approach: | They propose to add a new training stage before contrastive learning to enrich the semantics of the final token embedding. |
| Outcome: | The proposed training stage improves performance on the Massive Text Embedding Benchmark (MTEB), achieving new state-of-the-art results across different LLM base models and scales. |
A Multi-Task Dual-Tree Network for Aspect Sentiment Triplet Extraction (2022.coling-1)
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| Challenge: | Existing methods are poor at detecting complicated relations between aspects and opinions . detecting unclear boundaries of multi-word aspects and opinion is also a challenge . |
| Approach: | They propose a multi-task dual-tree network to extract triplets from a given sentence . they employ a constituency tree and a modified dependency tree to enhance the interaction . |
| Outcome: | The proposed model extracts triplets from a given sentence, and it is effective on four datasets. |
Noise-Robust Training with Dynamic Loss and Contrastive Learning for Distantly-Supervised Named Entity Recognition (2023.findings-acl)
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| Challenge: | Named entity recognition (NER) is a task in natural language processing that aims at locating entity mentions in a given sentence and assigning them to certain types. |
| Approach: | They propose to use a dynamic loss function to better adapt to the changing noise during the training process and incorporate token level contrastive learning to fully utilize the noisy data. |
| Outcome: | The proposed method outperforms existing NER models on three benchmark datasets and outperformed existing models by significant margins. |
TSPO: Breaking the Double Homogenization Dilemma in Multi-turn Search Policy Optimization (2026.findings-acl)
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Shichao Ma, Zhiyuan Ma, Ming Yang, Xiaofan Li, Xing Wu, Jintao Du, Yu Cheng, Weiqiang Wang, Qiliang Liu, Zhengyang Zhou, Yang Wang
| Challenge: | Large Language Models (LLMs) can solve complex tasks through iterative information retrieval. |
| Approach: | They propose a turn-level stage-aware policy optimization approach to solve this problem . they introduce a first-occurrence latent reward mechanism to allocate partial rewards . |
| Outcome: | Experiments show that TSPO outperforms state-of-the-art models on Qwen2.5-3B and 7B models. |
Enhancing Distantly Supervised Named Entity Recognition with Strong Label Guided Lottery Training (2024.lrec-main)
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| Challenge: | Named entity recognition (NER) requires a limited quantity of strongly labeled data . weakly labeles can be acquired through distant supervision, but can cause noise . |
| Approach: | They propose a noise-robust learning framework where safe parameters can be identified . they conduct extensive experiments on multiple datasets and show it outperforms the state-of-the-art methods. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on weakly labeled data. |