Papers by Youcheng Huang
TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance (2021.acl-long)
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Fengbin Zhu, Wenqiang Lei, Youcheng Huang, Chao Wang, Shuo Zhang, Jiancheng Lv, Fuli Feng, Tat-Seng Chua
| Challenge: | Existing QA systems focus on unstructured text, structured knowledge base, or semi-structured tables. |
| Approach: | They propose a large-scale question answering model based on financial reports . numerical reasoning is usually required to infer the answer . |
| Outcome: | The proposed model achieves 58.0% inF1, an 11.1% increase over the baseline model, but still lags behind the best human model. |
Lying with Truths: Open-Channel Multi-Agent Collusion for Belief Manipulation via Generative Montage (2026.acl-long)
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| Challenge: | Large language models (LLMs) evolve to autonomous agents synthesizing real-time information, but their reasoning capabilities introduce an unexpected attack surface. |
| Approach: | They propose a framework that constructs deceptive narratives through adversarial debate and coordinated posting of evidence fragments, causing victims to internalize and propagate fabricated conclusions. |
| Outcome: | The proposed framework constructs deceptive narratives through adversarial debate and coordinated posting of evidence fragments, causing victims to internalize and propagate fabricated conclusions. |
Reconciliation of Pre-trained Models and Prototypical Neural Networks in Few-shot Named Entity Recognition (2022.findings-emnlp)
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| Challenge: | Existing methods do not consider that pre-trained models contain a prominently large amount of information regarding word frequencies, thus biasing prototypical neural networks against learning word entities. |
| Approach: | They propose a one-line-code normalization method to reconcile such a mismatch with empirical and theoretical grounds and propose 'references' for the model enhancement. |
| Outcome: | The proposed method outperforms the state-of-the-art models on nine benchmark datasets and is comparable to the state of the art. |
Cross-model Transferability among Large Language Models on the Platonic Representations of Concepts (2025.acl-long)
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| Challenge: | Prior work has shown that a single LLM’s concept representations can be captured as steering vectors (SVs) prior work has demonstrated that SVs extracted from smaller LLMs can effectively control the behavior of larger LLM. |
| Approach: | They propose a linear transformation method to bridge LLM concept representations using simple linear transformations to enable efficient cross-model transfer and behavioral control via SVs. |
| Outcome: | The proposed method bridges concept representations across different LLMs and enables efficient cross-model transfer and behavioral control via SVs. |