Papers by Chengfei Lv
BiKT: Enabling Bidirectional Knowledge Transfer Between Pretrained Models and Sequential Downstream Tasks (2024.findings-emnlp)
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| Challenge: | Existing frameworks adapt from initial pretrained model to each downstream task directly, but ignore sequential nature of downstream tasks and feedback effect on pretrained models. |
| Approach: | They propose a framework to enable bidirectional knowledge transfer between pretrained models and downstream tasks in rounds. |
| Outcome: | The proposed framework improves on 9 GLUE datasets and 6 SuperGLUEs. |
AutoAct: Automatic Agent Learning from Scratch for QA via Self-Planning (2024.acl-long)
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Shuofei Qiao, Ningyu Zhang, Runnan Fang, Yujie Luo, Wangchunshu Zhou, Yuchen Jiang, Chengfei Lv, Huajun Chen
| Challenge: | Existing language agent systems struggle with costly data reliance and need multiple models for multiple functions. |
| Approach: | They propose an automatic agent learning framework for QA that synthesizes planning trajectories without human intervention. |
| Outcome: | The proposed framework outperforms existing models on question-answering tasks with a division-of-labor strategy. |
Making Language Models Better Tool Learners with Execution Feedback (2024.naacl-long)
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| Challenge: | Existing tool learning methodologies induce large language models to utilize tools indiscriminately . Existing frameworks that teach language models when and how to use tools propagate errors rather than enhance performance. |
| Approach: | They propose a framework that enables large language models to continually learn through feedback derived from tool execution. |
| Outcome: | The proposed framework can make large language models selectively use tools . it improves accuracy while enhancing insufficient tool learning, it shows . |
MadaKV: Adaptive Modality-Perception KV Cache Eviction for Efficient Multimodal Long-Context Inference (2025.acl-long)
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Kunxi Li, Zhonghua Jiang, Zhouzhou Shen, ZhaodeWang ZhaodeWang, Chengfei Lv, Shengyu Zhang, Fan Wu, Fei Wu
| Challenge: | Existing KV cache eviction methods fail to capture modality-specific information, resulting in suboptimal performance. |
| Approach: | They propose a modality-adaptive key-value (KV) cache eviction strategy to enhance the efficiency of multimodal large language models in long-context inference. |
| Outcome: | The proposed method reduces the KV cache memory footprint and model inference latency while maintaining high accuracy across multimodal long-context tasks. |