Papers by Zhibin Wang
Resonating with RoPE: Spectral Quantization for High-Fidelity Key Cache Compression (2026.acl-long)
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| Challenge: | Existing approaches to reduce memory footprint of long-context LLMs rely on RoPE-induced oscillations. |
| Approach: | They propose a frequency-domain framework that converts RoPE-induced oscillations into sparse spectral representations. |
| Outcome: | The proposed framework achieves efficient compression with performance comparable to FP16 benchmarks. |
Data Interpreter: An LLM Agent for Data Science (2025.findings-acl)
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Sirui Hong, Yizhang Lin, Bang Liu, Bangbang Liu, Binhao Wu, Ceyao Zhang, Danyang Li, Jiaqi Chen, Jiayi Zhang, Jinlin Wang, Li Zhang, Lingyao Zhang, Min Yang, Mingchen Zhuge, Taicheng Guo, Tuo Zhou, Wei Tao, Robert Tang, Xiangtao Lu, Xiawu Zheng, Xinbing Liang, Yaying Fei, Yuheng Cheng, Yongxin Ni, Zhibin Gou, Zongze Xu, Yuyu Luo, Chenglin Wu
| Challenge: | Large Language Models (LLMs) excel in various domains but face challenges when applied to data science workflows due to their complex, multi-stage nature. |
| Approach: | They propose a hierarchical graph-based agent that represents complexity and a progressive strategy for step-by-step verification, refinement, and consistent context management. |
| Outcome: | The proposed agent surpasses state-of-the-art baselines on the MATH dataset and performs better on InfiAgent-DABench. |
LipoAgent: Coordinating Fine-Tuned LLM Agents for Safer Lipid Design (2026.findings-acl)
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| Challenge: | Lipid nanoparticles (LNPs) are among the most clinically mature platforms for nucleic acid delivery, yet designing lipids that are effective and biologically safe remains a major bottleneck. |
| Approach: | They propose a safety-aware multi-agent LLM framework for lipid discovery that enforces toxicity as a prerequisite for efficiency prediction. |
| Outcome: | The proposed framework achieves an average improvement in mRNA transfection efficiency prediction across multiple foundation models. |
Friendly Topic Assistant for Transformer Based Abstractive Summarization (2020.emnlp-main)
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| Challenge: | Abstractive document summarization is a comprehensive task in natural language processing. |
| Approach: | They propose a topic assistant that rearranges and learns document semantics . they propose TA that is compatible with Transformer-based models and user-friendly . |
| Outcome: | The proposed model is compatible with Transformer-based models and user-friendly. |
Knowledge Aware Conversation Generation with Explainable Reasoning over Augmented Graphs (D19-1)
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| Challenge: | Existing knowledge-based open domain conversation generation models are limited by the use of unstructured knowledge texts. |
| Approach: | They propose a knowledge aware chatting machine with three components, an augmented knowledge graph with both triples and texts, knowledge selector, and knowledge aware response generator. |
| Outcome: | The proposed system is more explainable and flexible than state-of-the-art models. |
Long Time No See! Open-Domain Conversation with Long-Term Persona Memory (2022.findings-acl)
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| Challenge: | Existing persona dialogue datasets and models can build long-term relationships with humans . however, current open-domain dialogue systems cannot build long relationships with users . |
| Approach: | They propose a long-term memory conversation dataset and a dialogue generation framework with long-Term memory mechanism to extract and continuously update long-time persona memory. |
| Outcome: | The proposed system outperforms baselines in terms of long-term dialogue consistency . the proposed system can build long-lasting relationships between humans and bots . |
Enhanced Visual Instruction Tuning with Synthesized Image-Dialogue Data (2024.findings-acl)
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Yanda Li, Chi Zhang, Gang Yu, Wanqi Yang, Zhibin Wang, Bin Fu, Guosheng Lin, Chunhua Shen, Ling Chen, Yunchao Wei
| Challenge: | OpenAI's GPT-4 has demonstrated remarkable multimodal capabilities, but specific mechanics of GPT4 remain unknown. |
| Approach: | They propose a data collection methodology that synchronously synthesizes images and dialogues for visual instruction tuning. |
| Outcome: | The proposed method improves on ten commonly assessed models and provides greater flexibility compared to existing methods. |
Exploring Better Text Image Translation with Multimodal Codebook (2023.acl-long)
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| Challenge: | Current studies on text image translation face bottlenecks due to lack of a publicly available dataset and poor optical character recognition. |
| Approach: | They propose a text image translation model with a multimodal codebook and an OCR dataset for Chinese-English translation. |
| Outcome: | The proposed model can associate the image with relevant texts, providing useful supplementary information for translation. |
SciAgent: Tool-augmented Language Models for Scientific Reasoning (2024.emnlp-main)
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Yubo Ma, Zhibin Gou, Junheng Hao, Ruochen Xu, Shuohang Wang, Liangming Pan, Yujiu Yang, Yixin Cao, Aixin Sun
| Challenge: | SciAgent surpasses other LLMs with the comparable size by more than 8.0% in absolute accuracy. |
| Approach: | They propose a tool-augmented scientific reasoning setting that supplements LLMs with scalable toolsets and builds a benchmark to evaluate LLM’s abilities with tool assistance. |
| Outcome: | The proposed setting augments LLMs with scalable toolsets and shifts the focus from pursuing an omniscient problem solver to a proficient tool-user. |
EnsLM: Ensemble Language Model for Data Diversity by Semantic Clustering (2021.acl-long)
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| Challenge: | Existing studies have shown that data diversity affects the performance of LMs if we train a single LM over the entire dataset. |
| Approach: | They propose an autoencoding topic model with a mixture prior to perform clustering for the data. |
| Outcome: | The proposed model can learn knowledge from different samples while extracting cluster-specific features. |
PLATO-2: Towards Building an Open-Domain Chatbot via Curriculum Learning (2021.findings-acl)
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| Challenge: | PLATO-2 is a high-quality open-domain chatbot that can generate one-to-many mappings and improve response quality. |
| Approach: | They propose a curriculum learning process to build a high-quality open-domain chatbot . they use a coarse-grained generation model and latent variables to train a generative model . |
| Outcome: | The proposed model improves on Chinese and English data and can generate diverse responses and select the best response. |