Papers by Peijun Qing
Prompt Space Optimizing Few-shot Reasoning Success with Large Language Models (2024.findings-naacl)
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| Challenge: | Prompt engineering is an essential technique for enhancing the abilities of large language models (LLMs) by providing explicit and specific instructions. |
| Approach: | They propose a new approach that uses text embeddings to obtain basis vectors by matrix decomposition and constructs a space for representing all prompts. |
| Outcome: | The proposed approach significantly outperforms state-of-the-art prompt paradigms on ten public reasoning benchmarks. |
Temporal Working Memory: Query-Guided Segment Refinement for Enhanced Multimodal Understanding (2025.findings-naacl)
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Xingjian Diao, Chunhui Zhang, Weiyi Wu, Zhongyu Ouyang, Peijun Qing, Ming Cheng, Soroush Vosoughi, Jiang Gui
| Challenge: | Multimodal foundation models have demonstrated significant success in tasks such as visual captioning, question answering, and image-text retrieval. |
| Approach: | They propose a specialized cognitive module, temporal working memory, which selectively retains task-relevant information across temporal dimensions. |
| Outcome: | The module retains task-relevant information across temporal dimensions, ensuring that critical details are preserved throughout the processing of video and audio content. |
Tailoring Memory Granularity for Multi-Hop Reasoning over Long Contexts (2026.findings-eacl)
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| Challenge: | Extensive experiments on long-context multi-hop question answering benchmarks show TAG achieves state-of-the-art performance. |
| Approach: | They propose a framework that prestructures memory into diverse granularities and employs a reward-guided navigator to adaptively compose hybrid memory tailored to each query. |
| Outcome: | Experiments on long-context multi-hop question answering show that the framework achieves state-of-the-art performance. |
AlphaLoRA: Assigning LoRA Experts Based on Layer Training Quality (2024.emnlp-main)
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| Challenge: | Recent studies combine LoRA with Mixture-of-Experts (MoE) to improve performance in Large Language Models. |
| Approach: | They propose a method to combine LoRA and Mixture-of-Experts (MoE) to improve performance in Large Language Models. |
| Outcome: | The proposed method reduces redundancy in LoRA experts within the MoE architecture, and improves training quality across layers. |
Judging with Many Minds: Do More Perspectives Mean Less Prejudice? On Bias Amplification and Resistance in Multi-Agent Based LLM-as-Judge (2025.findings-emnlp)
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Chiyu Ma, Enpei Zhang, Yilun Zhao, Wenjun Liu, Yaning Jia, Peijun Qing, Lin Shi, Arman Cohan, Yujun Yan, Soroush Vosoughi
| Challenge: | LLM-as-Judge frameworks provide scalable alternative to human evaluation . but the question of how intrinsic biases manifest in these settings remains unexplored . |
| Approach: | They conduct systematic analysis of four bias types in multi-agent LLM-as-Judge frameworks . they find debate framework amplifies biases sharply after initial debate . |
| Outcome: | The proposed frameworks amplify biases after debate and show they are stronger in meta-judge scenarios. |
SoundMind: RL-Incentivized Logic Reasoning for Audio-Language Models (2025.emnlp-main)
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Xingjian Diao, Chunhui Zhang, Keyi Kong, Weiyi Wu, Chiyu Ma, Zhongyu Ouyang, Peijun Qing, Soroush Vosoughi, Jiang Gui
| Challenge: | Recent large language models have demonstrated impressive reasoning abilities, but their extension to the audio modality remains underexplored. |
| Approach: | They propose a rule-based reinforcement learning algorithm to equip LALMs with robust reasoning capabilities. |
| Outcome: | The proposed algorithm improves on the SoundMind benchmark. |
ProtoVQA: An Adaptable Prototypical Framework for Explainable Fine-Grained Visual Question Answering (2025.emnlp-main)
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| Challenge: | Visual Question Answering (VQA) is increasingly used in diverse applications where models must provide accurate answers and explanations that humans can easily understand and verify. |
| Approach: | They propose a unified prototypical framework that learns question-aware prototypes that serve as reasoning anchors and applies spatially constrained matching to ensure that the selected evidence is coherent and semantically relevant. |
| Outcome: | The proposed framework yields faithful, fine-grained explanations while maintaining competitive accuracy. |
GammaE: Gamma Embeddings for Logical Queries on Knowledge Graphs (2022.emnlp-main)
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| Challenge: | Existing methods for embedding knowledge graphs are difficult due to complicated query structures and incomplete graph data. |
| Approach: | They propose a probabilistic embedding model for encoding entities and queries to answer different types of FOL queries on KGs. |
| Outcome: | The proposed model outperforms state-of-the-art models on public benchmarks on three large logical query datasets. |