3 citations · 7 across the 11 of their papers we have counts for
11 papers
Talking to Itself While Coding: What Makes Comments Help Code Generation?
Dangfeng Pan, Zhensu Sun, Cenyuan Zhang +2
Large Language Models (LLMs) often generate natural-language comments while writing code, and these comments become part of the context used to generate the code that follows. Howe…
Looped World Models
Hongyuan Adam Lu, Z. L. Victor Wei, Qun Zhang +28
Current world models face a fundamental tension: faithful long-horizon simulation demands deep computation, but deeper models are expensive to deploy and prone to compounding error…
The Hidden Cost of Readability: How Code Formatting Silently Consumes Your LLM Budget
Dangfeng Pan, Zhensu Sun, Cenyuan Zhang +2
Source code is usually formatted with elements like indentation and newlines to improve readability for human developers. However, these visual aids do not seem to be beneficial fo…
Enhancing the Capability and Robustness of Large Language Models through Reinforcement Learning-Driven Query Refinement
Xiaohua Wang, Zisu Huang, Feiran Zhang +5
The capacity of large language models (LLMs) to generate honest, harmless, and helpful responses heavily relies on the quality of user prompts. However, these prompts often tend to…
Towards Biologically Plausible Computing: A Comprehensive Comparison
Changze Lv, Yufei Gu, Zhengkang Guo +16
Backpropagation is a cornerstone algorithm in training neural networks for supervised learning, which uses a gradient descent method to update network weights by minimizing the dis…
Promoting Data and Model Privacy in Federated Learning through Quantized LoRA
JianHao Zhu, Changze Lv, Xiaohua Wang +7
Conventional federated learning primarily aims to secure the privacy of data distributed across multiple edge devices, with the global model dispatched to edge devices for paramete…
Decoding Continuous Character-based Language from Non-invasive Brain Recordings
Cenyuan Zhang, Xiaoqing Zheng, Ruicheng Yin +8
Deciphering natural language from brain activity through non-invasive devices remains a formidable challenge. Previous non-invasive decoders either require multiple experiments wit…
Advancing Parameter Efficiency in Fine-tuning via Representation Editing
Muling Wu, Wenhao Liu, Xiaohua Wang +7
Parameter Efficient Fine-Tuning (PEFT) techniques have drawn significant attention due to their ability to yield competitive results while updating only a small portion of the adju…
Aligning Large Language Models with Human Preferences through Representation Engineering
Wenhao Liu, Xiaohua Wang, Muling Wu +7
Aligning large language models (LLMs) with human preferences is crucial for enhancing their utility in terms of helpfulness, truthfulness, safety, harmlessness, and interestingness…
SpikeCLIP: A Contrastive Language-Image Pretrained Spiking Neural Network
Changze Lv, Tianlong Li, Wenhao Liu +6
Spiking Neural Networks (SNNs) have emerged as a promising alternative to conventional Artificial Neural Networks (ANNs), demonstrating comparable performance in both visual and li…
SpikeBERT: A Language Spikformer Learned from BERT with Knowledge Distillation
Changze Lv, Tianlong Li, Jianhan Xu +5
Spiking neural networks (SNNs) offer a promising avenue to implement deep neural networks in a more energy-efficient way. However, the network architectures of existing SNNs for la…