Papers by Peijie Chen
ECLM: Entity Level Language Model for Spoken Language Understanding with Chain of Intent (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) have demonstrated impressive capabilities in language generation and general task performance, but their application to spoken language understanding remains challenging. |
| Approach: | They propose an Entity-level Language Model framework which reformulates slot-filling as an entity recognition task and introduces a new concept, Chain of Intent, to enable step-by-step multi-intent recognition. |
| Outcome: | The proposed framework outperforms strong baselines such as Uni-MIS and achieves gains of 3.7% and 3.1% on MixATIS and MixSNIPS. |
Type Enhanced BERT for Correcting NER Errors (2023.findings-acl)
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| Challenge: | Named entity recognition (NER) is the task of identifying spans that belong to particular categories, such as person, location, organization, etc. |
| Approach: | They propose a method that integrates named entity’s type information into BERT by an adapter layer and integrates it into a gazetteer. |
| Outcome: | The proposed method outperforms baselines in multiple corpus. |
PEEB: Part-based Image Classifiers with an Explainable and Editable Language Bottleneck (2024.findings-naacl)
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| Challenge: | CLIP-based classifiers rely on the prompt containing a class name that is known to the text encoder and perform poorly on new classes or the classes whose names rarely appear on the Internet. |
| Approach: | They propose to use a set of text descriptors to express a class name into a textual descriptable and match the embeddings of the detected parts to their textual ones to compute a logit score. |
| Outcome: | The proposed classifier outperforms CLIP-based classifiers on zero-shot and supervised learning settings by 88.80% and 92.20% accuracy on CUB-200 and Stanford Dogs-120. |
AHA: Aligning Large Audio-Language Models for Reasoning Hallucinations via Counterfactual Hard Negatives (2026.findings-acl)
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Yanxi Chen, Wenhui Zhu, Xiwen Chen, Zhipeng Wang, Xin Li, Peijie Qiu, Hao Wang, Xuanzhao Dong, Yujian Xiong, Anderson Schneider, Yuriy Nevmyvaka, Yalin Wang
| Challenge: | Large Audio-Language Models suffer from hallucinations, e.g., generating text not grounded in the audio input. |
| Approach: | They propose a framework to address hallucination problems in large audio-language models . they use a preference dataset to test the model's accuracy . |
| Outcome: | The proposed model outperforms the latest SOTA methods in terms of performance and generalization. |
DRA-GRPO: Your GRPO Needs to Know Diverse Reasoning Paths for Mathematical Reasoning (2026.findings-acl)
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Xiwen Chen, Wenhui Zhu, Peijie Qiu, Xuanzhao Dong, Hao Wang, Haiyu Wu, Huayu Li, Aris Sotiras, Yalin Wang, Abolfazl Razi
| Challenge: | Existing methods for group-relative policy optimization rely on scalar correctness rewards that are often non-injective with respect to semantic content. |
| Approach: | They propose a framework that calibrates the reward signal using the semantic density of sampled groups. |
| Outcome: | The proposed framework outperforms strong baselines on five math benchmarks with 7,000 samples and 55 cost. |
Perovskite-LLM: Knowledge-Enhanced Large Language Models for Perovskite Solar Cell Research (2025.findings-emnlp)
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Xiang Liu, Penglei Sun, Shuyan Chen, Longhan Zhang, Peijie Dong, Huajie You, Yongqi Zhang, Chang Yan, Xiaowen Chu, Tong-yi Zhang
| Challenge: | a rapid advancement of perovskite solar cells has led to an exponential growth in research publications. |
| Approach: | They propose a knowledge-enhanced system for perovskite solar cells that integrates three key components. |
| Outcome: | The proposed system outperforms existing models in domain-specific knowledge retrieval and scientific reasoning tasks. |
DMIN: A Discourse-specific Multi-granularity Integration Network for Conversational Aspect-based Sentiment Quadruple Analysis (2024.findings-acl)
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| Challenge: | Existing studies focus on enhancing token-level interactions, but lack sufficient modeling of discourse structure information. |
| Approach: | They propose to use a discourse structure called "thread" to enhance token interaction among different utterances. |
| Outcome: | The proposed model achieves state-of-the-art on two datasets. |
SafeEraser: Enhancing Safety in Multimodal Large Language Models through Multimodal Machine Unlearning (2025.findings-acl)
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Junkai Chen, Zhijie Deng, Kening Zheng, Yibo Yan, Shuliang Liu, PeiJun Wu, Peijie Jiang, Jia Liu, Xuming Hu
| Challenge: | Existing methods for MU forget quality and model utility are not fully explored for safety in MLLMs. |
| Approach: | They propose a safety unlearning benchmark for MLLMs to measure over-forgetting . they propose MU methods to forget quality and model utility . |
| Outcome: | The proposed method reduces over-forgetting by 79.5% while maintaining forget quality and model utility. |