1 citations · 1 across the 4 of their papers we have counts for
7 papers
Reconciling In-Context and In-Weight Learning via Dual Representation Space Encoding
Guanyu Chen, Ruichen Wang, Tianren Zhang +1
In-context learning (ICL) is a valuable capability exhibited by Transformers pretrained on diverse sequence tasks. However, previous studies have observed that ICL often conflicts…
Exploring the Hidden Reasoning Process of Large Language Models by Misleading Them
Guanyu Chen, Peiyang Wang, Yizhou Jiang +5
Large language models (LLMs) have been able to perform various forms of reasoning tasks in a wide range of scenarios, but are they truly engaging in task abstraction and rule-based…
When Do Neural Networks Learn World Models?
Tianren Zhang, Guanyu Chen, Feng Chen
Humans develop world models that capture the underlying generation process of data. Whether neural networks can learn similar world models remains an open problem. In this work, we…
Learning Invariable Semantical Representation from Language for Extensible Policy Generalization
Yihan Li, Jinsheng Ren, Tianrun Xu +3
Recently, incorporating natural language instructions into reinforcement learning (RL) to learn semantically meaningful representations and foster generalization has caught many co…
Non-autoregressive Transformer with Unified Bidirectional Decoder for Automatic Speech Recognition
Chuan-Fei Zhang, Yan Liu, Tian-Hao Zhang +3
Non-autoregressive (NAR) transformer models have been studied intensively in automatic speech recognition (ASR), and a substantial part of NAR transformer models is to use the casu…
CRIL: Continual Robot Imitation Learning via Generative and Prediction Model
Chongkai Gao, Haichuan Gao, Shangqi Guo +2
Imitation learning (IL) algorithms have shown promising results for robots to learn skills from expert demonstrations. However, they need multi-task demonstrations to be provided a…