1 citations · 1 across the 14 of their papers we have counts for
22 papers
CL-bench Life: Can Language Models Learn from Real-Life Context?
Shihan Dou, Yujiong Shen, Chenhao Huang +35
Today's AI assistants such as OpenClaw are designed to handle context effectively, making context learning an increasingly important capability for models. As these systems move be…
Reward Hacking in the Era of Large Models: Mechanisms, Emergent Misalignment, Challenges
Xiaohua Wang, Muzhao Tian, Yuqi Zeng +20
Reinforcement Learning from Human Feedback (RLHF) and related alignment paradigms have become central to steering large language models (LLMs) and multimodal large language models…
Parallel Training in Spiking Neural Networks
Yanbin Huang, Man Yao, Yuqi Pan +5
The bio-inspired integrate-fire-reset mechanism of spiking neurons constitutes the foundation for efficient processing in Spiking Neural Networks (SNNs). Recent progress in large m…
BatCoder: Self-Supervised Bidirectional Code-Documentation Learning via Back-Translation
Jingwen Xu, Yiyang Lu, Zisu Huang +9
Training LLMs for code-related tasks typically depends on high-quality code-documentation pairs, which are costly to curate and often scarce for niche programming languages. We int…
CSSG: Measuring Code Similarity with Semantic Graphs
Yiyang Lu, Jingwen Xu, Changze Lv +6
Existing code similarity metrics, such as BLEU, CodeBLEU, and TSED, largely rely on surface-level string overlap or abstract syntax tree structures, and often fail to capture deepe…
Benchmark^2: Systematic Evaluation of LLM Benchmarks
Qi Qian, Chengsong Huang, Jingwen Xu +13
The rapid proliferation of benchmarks for evaluating large language models (LLMs) has created an urgent need for systematic methods to assess benchmark quality itself. We propose B…