Papers by Jianbo Zhao
Encoding Spreadsheets for Large Language Models (2024.emnlp-main)
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Haoyu Dong, Jianbo Zhao, Yuzhang Tian, Junyu Xiong, Mengyu Zhou, Yun Lin, José Cambronero, Yeye He, Shi Han, Dongmei Zhang
| Challenge: | Spreadsheets are characterized by their extensive two-dimensional grids, flexible layouts, and varied formatting options, which pose significant challenges for large language models (LLMs). |
| Approach: | They propose a structural-anchor-based compression, inverse index translation, and data-format-aware aggregation module to compress spreadsheets effectively. |
| Outcome: | The proposed method outperforms the existing model in GPT4 and achieves a state-of-the-art 78.9% F1 score. |
Collaborative Chain-of-Agents for Parametric-Retrieved Knowledge Synergy (2026.acl-long)
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| Challenge: | Existing RAG methods focus on external retrieval, while ignoring the rich content of the model. |
| Approach: | They propose a framework that enhances explicit synergy over parametric and retrieved knowledge by integrating external retrieval components into the input context of the LLMs. |
| Outcome: | The proposed framework enhances explicit synergy over parametric and retrieved knowledge. |
GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis (2025.acl-long)
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| Challenge: | Existing approaches to retrieve information from large language models (LLMs) but they fail to address the preference gap between retrievers and LLMs. |
| Approach: | They propose a retrieval module that dynamically injects retrieved information into the input context of large language models (LLMs) This approach aligns the retriever’s and LLM’s preferences by defining a new metric, “gain”, which measure how well an input passage contributes to correct outputs. |
| Outcome: | The proposed approach has shown significant success in various NLP tasks, but there is a preference gap between retrievers and LLMs. |
LICHEE: Improving Language Model Pre-training with Multi-grained Tokenization (2021.findings-acl)
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| Challenge: | Pre-trained language models are trained based on single-grained tokenization, making it hard to learn the precise meaning of coarse-grain words and phrases. |
| Approach: | They propose a language model pretraining method that incorporates multi-grained information of input text into pre-trained language models. |
| Outcome: | The proposed method improves performance on CLUE and SuperGLUE in Chinese and English with little extra inference cost. |
Investigating Capsule Networks with Dynamic Routing for Text Classification (D18-1)
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| Challenge: | Earlier efforts in text modeling have achieved limited success on word meanings . convolutional neural networks (CNNs) are used to model higher level concepts and facts in texts . |
| Approach: | They propose three strategies to stabilize dynamic routing process to alleviate disturbance of noise capsules. |
| Outcome: | The proposed methods achieve state-of-the-art on 4 out of 6 datasets . they show that capsule networks exhibit significant improvement over baseline methods . |