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Jin Xiao

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8

AAAI Conference 2024 Conference Paper

Can Large Language Models Understand Real-World Complex Instructions?

  • Qianyu He
  • Jie Zeng
  • Wenhao Huang
  • Lina Chen
  • Jin Xiao
  • Qianxi He
  • Xunzhe Zhou
  • Jiaqing Liang

Large language models (LLMs) can understand human instructions, showing their potential for pragmatic applications beyond traditional NLP tasks. However, they still struggle with complex instructions, which can be either complex task descriptions that require multiple tasks and constraints, or complex input that contains long context, noise, heterogeneous information and multi-turn format. Due to these features, LLMs often ignore semantic constraints from task descriptions, generate incorrect formats, violate length or sample count constraints, and be unfaithful to the input text. Existing benchmarks are insufficient to assess LLMs’ ability to understand complex instructions, as they are close-ended and simple. To bridge this gap, we propose CELLO, a benchmark for evaluating LLMs' ability to follow complex instructions systematically. We design eight features for complex instructions and construct a comprehensive evaluation dataset from real-world scenarios. We also establish four criteria and develop corresponding metrics, as current ones are inadequate, biased or too strict and coarse-grained. We compare the performance of representative Chinese-oriented and English-oriented models in following complex instructions through extensive experiments. Resources of CELLO are publicly available at https://github.com/Abbey4799/CELLO.

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JBHI Journal 2014 Journal Article

Walking-Age Analyzer for Healthcare Applications

  • Bo Jin
  • Tran Hoai Thu
  • Eunhye Baek
  • SungHwan Sakong
  • Jin Xiao
  • Tapas Mondal
  • M. Jamal Deen

This paper describes a walking-age pattern analysis and identification system using a 3-D accelerometer and a gyroscope. First, a walking pattern database from 79 volunteers of ages ranging from 10 to 83 years is constructed. Second, using feature extraction and clustering, three distinct walking-age groups, children of ages 10 and below, adults in 20-60s, and elders in 70s and 80s, were identified. For this study, low-pass filtering, empirical mode decomposition, and K-means were used to process and analyze the experimental results. Analysis shows that volunteers' walking-ages can be categorized into distinct groups based on simple walking pattern signals. This grouping can then be used to detect persons with walking patterns outside their age groups. If the walking pattern puts an individual in a higher “walking age” grouping, then this could be an indicator of potential health/walking problems, such as weak joints, poor musculoskeletal support system or a tendency to fall.

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