3 papers
cs.SE2025
CodeIF: Benchmarking the Instruction-Following Capabilities of Large Language Models for Code Generation
Kaiwen Yan, Hongcheng Guo, Xuanqing Shi +3
With the rapid advancement of Large Language Models (LLMs), the demand for robust instruction-following capabilities in code generation tasks has grown significantly. Code generati…
cs.CL2024
IW-Bench: Evaluating Large Multimodal Models for Converting Image-to-Web
Hongcheng Guo, Wei Zhang, Junhao Chen +9
Recently advancements in large multimodal models have led to significant strides in image comprehension capabilities. Despite these advancements, there is a lack of the robust benc…
cs.LG2024
Securing Reliability: A Brief Overview on Enhancing In-Context Learning for Foundation Models
Yunpeng Huang, Yaonan Gu, Jingwei Xu +3
As foundation models (FMs) continue to shape the landscape of AI, the in-context learning (ICL) paradigm thrives but also encounters issues such as toxicity, hallucination, dispari…