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Edward Y. Chang

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6 papers
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6

ICML Conference 2025 Conference Paper

A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment

  • Edward Y. Chang

This paper introduces a checks-and-balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation. Beyond structural separation, we address a fundamental challenge: regulating emotion to shape behaviors. Drawing from psychological theories where managing emotional responses prevents harmful behaviors, we develop a self-supervised learning pipeline that maps emotions to linguistic behaviors, enabling precise behavioral modulation through emotional conditioning. By integrating this approach with adversarial testing, our framework demonstrates how DIKE and ERIS direct linguistic behaviors toward ethical outcomes while preserving independence throughout knowledge generation, ethical oversight, and contextual interpretation.

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AAAI Conference 2019 Conference Paper

EA-CG: An Approximate Second-Order Method for Training Fully-Connected Neural Networks

  • Sheng-Wei Chen
  • Chun-Nan Chou
  • Edward Y. Chang

For training fully-connected neural networks (FCNNs), we propose a practical approximate second-order method including: 1) an approximation of the Hessian matrix and 2) a conjugate gradient (CG) based method. Our proposed approximate Hessian matrix is memory-efficient and can be applied to any FCNNs where the activation and criterion functions are twice differentiable. We devise a CG-based method incorporating one-rank approximation to derive Newton directions for training FCNNs, which significantly reduces both space and time complexity. This CG-based method can be employed to solve any linear equation where the coefficient matrix is Kroneckerfactored, symmetric and positive definite. Empirical studies show the efficacy and efficiency of our proposed method.

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AAAI Conference 2012 Conference Paper

A Data-Driven Approach to Question Subjectivity Identification in Community Question Answering

  • Tom Chao Zhou
  • Xiance Si
  • Edward Y. Chang
  • Irwin King
  • Michael R. Lyu

Automatic Subjective Question Answering (ASQA), which aims at answering users’ subjective questions using summaries of multiple opinions, becomes increasingly important. One challenge of ASQA is that expected answers for subjective questions may not readily exist in the Web. The rising and popularity of Community Question Answering (CQA) sites, which provide platforms for people to post and answer questions, provides an alternative to ASQA. One important task of ASQA is question subjectivity identification, which identifies whether a user is asking a subjective question. Unfortunately, there has been little labeled training data available for this task. In this paper, we propose an approach to collect training data automatically by utilizing social signals in CQA sites without involving any manual labeling. Experimental results show that our data-driven approach achieves 9. 37% relative improvement over the supervised approach using manually labeled data, and achieves 5. 15% relative gain over a stateof-the-art semi-supervised approach. In addition, we propose several heuristic features for question subjectivity identification. By adding these features, we achieve 11. 23% relative improvement over word n-gram feature under the same experimental setting.

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TIST Journal 2011 Journal Article

PLDA+

  • Zhiyuan Liu
  • Yuzhou Zhang
  • Edward Y. Chang
  • Maosong Sun

Previous methods of distributed Gibbs sampling for LDA run into either memory or communication bottlenecks. To improve scalability, we propose four strategies: data placement, pipeline processing, word bundling, and priority-based scheduling. Experiments show that our strategies significantly reduce the unparallelizable communication bottleneck and achieve good load balancing, and hence improve scalability of LDA.

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