Papers by Chuan Xiao
Automatic Table Union Search with Tabular Representation Learning (2023.findings-acl)
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Xuming Hu, Shen Wang, Xiao Qin, Chuan Lei, Zhengyuan Shen, Christos Faloutsos, Asterios Katsifodimos, George Karypis, Lijie Wen, Philip S. Yu
| Challenge: | Existing methods to identify uniability based on column representations are insufficient to reveal latent relational features to describe column relation between pair of columns. |
| Approach: | They propose a self-supervised table union search framework called AutoTUS to learn column relational representations in a multi-stage manner. |
| Outcome: | The proposed framework improves on the SOTA baseline and on real-world datasets. |
GRASPrune: Global Gating for Budgeted Structured Pruning of Large Language Models (2026.acl-long)
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| Challenge: | Large language models are expensive to serve because dense FFN blocks, multi-head attention, and KV caches dominate memory. |
| Approach: | They propose a global budgeted structured pruning framework that prunes FFN channels and attention KV head groups under a single global parameter budget. |
| Outcome: | The proposed model removes 50% of parameters and achieves 12.18 perplexity on WikiText-2 while maintaining competitive average zero-shot accuracy on five downstream benchmarks. |
Legal Fact Prediction: The Missing Piece in Legal Judgment Prediction (2025.emnlp-main)
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Junkai Liu, Yujie Tong, Hui Huang, Bowen Zheng, Yiran Hu, Peicheng Wu, Chuan Xiao, Makoto Onizuka, Muyun Yang, Shuyuan Zheng
| Challenge: | Existing studies use legal facts to predict judgments, but legal facts are difficult to obtain in early stages of litigation. |
| Approach: | They propose a legal fact prediction task that takes evidence from trial as input to make predictions in the absence of ground-truth legal facts. |
| Outcome: | The proposed task can predict court rulings without ground-truth legal facts . the first benchmark dataset, LFPBench, is used to evaluate the task . |
PolyJoin: Semantic Multi-key Joinable Table Search in Data Lakes (2025.findings-naacl)
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| Challenge: | Existing joinable table search methods focus on single key (unary) joins, where a single column is the join key, but are ineffective when dealing with join keys composed of multiple columns (n-ary joins) Existing methods are inefficient when dealing . with joins composed of n-aries, which are prevalent on web table corpora. |
| Approach: | They propose a joinable table search method that finds multi-key joinable tables on the web, given a query table. |
| Outcome: | The proposed method outperforms the state-of-the-art methods on two real-world web table benchmarks. |
Adaptive Layer Selection for Layer-Wise Token Pruning in LLM Inference (2026.findings-acl)
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| Challenge: | Large language models (LLMs) have demonstrated remarkable capabilities in processing long contexts. |
| Approach: | They propose a training-free method that adaptively chooses the selection layer for KV cache reduction . they exploit the variance of token ranks ordered by attention score to optimize decoding . |
| Outcome: | The proposed method outperforms state-of-the-art token pruning methods on InfiniteBench, RULER, and NIAH benchmarks. |
Jellyfish: Instruction-Tuning Local Large Language Models for Data Preprocessing (2024.emnlp-main)
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| Challenge: | Until 2021, most efforts were concentrated on one or two specific tasks such as error detection (ED) and data imputation (DI). |
| Approach: | They propose to instruction tune local LLMs as universal DP task solvers that operate on a local, single, and low-priced GPU, ensuring data security and enabling further customization. |
| Outcome: | The proposed models deliver competitiveness and generalizability to unseen tasks while barely compromising the base models’ abilities in NLP tasks. |
Shall We Team Up: Exploring Spontaneous Cooperation of Competing LLM Agents (2024.findings-emnlp)
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Zengqing Wu, Run Peng, Shuyuan Zheng, Qianying Liu, Xu Han, Brian Kwon, Makoto Onizuka, Shaojie Tang, Chuan Xiao
| Challenge: | Large Language Models (LLMs) are increasingly used in social simulations, where they are guided by carefully crafted instructions to exhibit human-like behaviors. |
| Approach: | They propose to use Large Language Models (LLMs) as agents to simulate the gradual transition from non-cooperative to cooperative behaviors of agents. |
| Outcome: | The proposed model can simulate the gradual transition from non-cooperative to cooperative behaviors in three competitive scenarios. |
DiscoverGPT: Multi-task Fine-tuning Large Language Model for Related Table Discovery (2025.findings-naacl)
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Xuming Hu, Xiao Qin, Chuan Lei, Asterios Katsifodimos, Zhengyuan Shen, Balasubramaniam Srinivasan, Huzefa Rangwala
| Challenge: | Existing methods to learn and evaluate the table semantic relatedness of tabular data are based on pretrain-and-finetune paradigms. |
| Approach: | They propose a multi-task fine-tuning framework that holistically discovers and leverages the intricate relationships among the supervisions to optimize the performance on the data discovery task. |
| Outcome: | The proposed framework outperforms the best performing baseline by up to 7% in F1 score. |