Papers by Junqing He
SG-FSM: A Self-Guiding Zero-Shot Prompting Paradigm for Multi-Hop Question Answering Based on Finite State Machine (2025.findings-naacl)
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Xiaochen Wang, Junqing He, Liang Chen, Gholamreza Haffari, Yiru Wang, Zhe Yang, Xiangdi Meng, Kunhao Pan, Zhifang Sui
| Challenge: | Multi-hop Question Answering (MHQA) is a challenging task that requires models to answer multiple questions with multiple passages. |
| Approach: | They propose a self-guided prompting finite state machine to improve multi-hop reasoning abilities by iterating over multiple questions and correcting itself to improve accuracy. |
| Outcome: | The proposed approach outperforms baselines on Musique and other datasets. |
MADial-Bench: Towards Real-world Evaluation of Memory-Augmented Dialogue Generation (2025.naacl-long)
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| Challenge: | Existing evaluation metrics for memory-augmented dialogue systems lack practical value . current evaluation methods only consider passive memory retrieval while ignoring diverse memory recall with rich triggering factors. |
| Approach: | They propose to use long-term memory to create human-like dialogues using chatbots. |
| Outcome: | The proposed benchmark covers memory retrieval and memory recognition tasks with both passive and proactive memory recall data. |
Orca: A Few-shot Benchmark for Chinese Conversational Machine Reading Comprehension (2023.findings-emnlp)
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Nuo Chen, Hongguang Li, Junqing He, Yinan Bao, Xinshi Lin, Qi Yang, Jianfeng Liu, Ruyi Gan, Jiaxing Zhang, Baoyuan Wang, Jia Li
| Challenge: | Existing benchmarks for conversational machine reading comprehension are inconsistent with real scenarios. |
| Approach: | They propose to use a Chinese CMRC benchmark to evaluate model's generalization ability towards diverse domains by using zero-shot/few-shot settings. |
| Outcome: | The proposed benchmarks are based on 831 hot-topic driven conversations with 4,742 turns and cover 33 domains. |
Never Lost in the Middle: Mastering Long-Context Question Answering with Position-Agnostic Decompositional Training (2024.acl-long)
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Junqing He, Kunhao Pan, Xiaoqun Dong, Zhuoyang Song, LiuYiBo LiuYiBo, Qianguosun Qianguosun, Yuxin Liang, Hao Wang, Enming Zhang, Jiaxing Zhang
| Challenge: | Large language models suffer from severe hallucinations, compromising performance in knowledge-oriented QA, dialogue, and writing. |
| Approach: | They propose to enhance the information searching and reflection ability of large language models by training them in position-agnostic multi-step QA tasks to improve their model's accuracy. |
| Outcome: | The proposed model improves in multi-doc QA and other benchmarks by 13.7% absolute gain in shuffled settings, by 21.5% in passage retrieval task. |
EMO-RL: Emotion-Rule-Based Reinforcement Learning Enhanced Audio-Language Model for Generalized Speech Emotion Recognition (2025.findings-emnlp)
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| Challenge: | Recent advances in reinforcement learning (RL) have shown promise in improving LALMs’ reasoning abilities, but their performance in affective computing tasks remains suboptimal. |
| Approach: | They propose a framework incorporating reinforcement learning with two key innovations: Emotion Similarity-Weighted Reward (ESWR) and Explicit Structured Reasoning (ESR). |
| Outcome: | The proposed framework improves LALMs' reasoning abilities on MELD and IEMOCAP datasets and shows strong generalization. |
Discriminating between Similar Languages on Imbalanced Conversational Texts (L18-1)
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| Challenge: | Empirical results suggest that our system achieves an accuracy of 95.7% on our Uyghur and Kazakh dataset, which is higher than that of the CNN classifier. |
| Approach: | They propose to build a balanced Uyghur and Kazakh corpus and build morphological classifiers to discriminate between the two languages. |
| Outcome: | The proposed system outperforms the champions on both test sets B1 and B2. |