4 citations · 7 across the 6 of their papers we have counts for
7 papers
Not X, but When: Diagnosing a Mandarin LLM Writing Tic through Contextual Appropriateness
Yihang Peng, Yi Jing, Dewen Liu +2
Recurring expressions in model-written text are often treated as signatures of machine authorship, yet most of them serve legitimate functions. Whether such an expression is a tic…
SAEVerbalizer: Generating Explanations for Sparse Autoencoder Features via Representation Verbalization
Weihan Meng, Hongzhu Guo, Yi Jing +5
Sparse autoencoders (SAEs) are proposed to extract numerous features from large language model (LLM) representations, yet explaining these features still relies primarily on extern…
BrainStratify: Coarse-to-Fine Disentanglement of Intracranial Neural Dynamics
Hui Zheng, Hai-Teng Wang, Yi-Tao Jing +7
Decoding speech directly from neural activity is a central goal in brain-computer interface (BCI) research. In recent years, exciting advances have been made through the growing us…
LinguaLens: Towards Interpreting Linguistic Mechanisms of Large Language Models via Sparse Auto-Encoder
Yi Jing, Zijun Yao, Hongzhu Guo +4
Large language models (LLMs) demonstrate exceptional performance on tasks requiring complex linguistic abilities, such as reference disambiguation and metaphor recognition/generati…
TranSFormer: Slow-Fast Transformer for Machine Translation
Bei Li, Yi Jing, Xu Tan +3
Learning multiscale Transformer models has been evidenced as a viable approach to augmenting machine translation systems. Prior research has primarily focused on treating subwords…
Learning Multiscale Transformer Models for Sequence Generation
Bei Li, Tong Zheng, Yi Jing +3
Multiscale feature hierarchies have been witnessed the success in the computer vision area. This further motivates researchers to design multiscale Transformer for natural language…
ODE Transformer: An Ordinary Differential Equation-Inspired Model for Sequence Generation
Bei Li, Quan Du, Tao Zhou +7
Residual networks are an Euler discretization of solutions to Ordinary Differential Equations (ODE). This paper explores a deeper relationship between Transformer and numerical ODE…