Papers by Zongqian Li
ReasonGraph: Visualization of Reasoning Methods and Extended Inference Paths (2025.acl-demo)
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| Challenge: | Large Language Models (LLMs) reasoning processes are complex and lack of organized visualization tools creates barriers to understanding, evaluation, and improvement. |
| Approach: | They propose a web-based platform for visualizing and analyzing LLM reasoning processes. |
| Outcome: | The proposed platform shows high parsing reliability, efficient processing, and excellent usability across various downstream applications. |
500xCompressor: Generalized Prompt Compression for Large Language Models (2025.acl-long)
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| Challenge: | Prompt compression is important for large language models to increase inference speed, reduce computation cost, and improve user experience. |
| Approach: | They propose a method that compresses natural language contexts into a special token . they propose to reduce computations and memory costs by reducing the complexity . |
| Outcome: | The proposed method reduces computations and memory costs by 27-90% . it retains 70-74% and 77-84% of the LLM capabilities at high compression ratios . |
Prompt Compression for Large Language Models: A Survey (2025.naacl-long)
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| Challenge: | Current methods for improving LLM efficiency focus on optimizing the model itself, while prompt-centric methods focus on lowering the complexity of input. |
| Approach: | They propose to use prompt compression to optimize the compression encoder and combine hard and soft prompt methods to improve the efficiency of LLMs. |
| Outcome: | The proposed methods are categorized into hard prompt methods and soft prompt methods. |