1 citations · 1 across the 4 of their papers we have counts for
5 papers
Learning What to Keep: Transfer-Aware Personal Skill Learning from User Feedback
Li Wang, Chen Zhang, Yirong Yang +4
User feedback enables LLMs to refine reusable personalized skills, but often mixes persistent preferences with request-specific corrections. Indiscriminately retaining both risks n…
TPCD: Tone-Pressure Contrastive Decoding and the Label-Free Gating Bottleneck in Vision-Language Models
Jinkun Zhao, Kui Zhang, Wenjun Wu
High-pressure prompts can push vision-language models (VLMs) into unsupported commitments, such as reading illegible text, reporting indeterminate times, or affirming absent object…
What Color Is the Text? A Benchmark for Hallucination Induced by Image-Embedded Prompt
Jinkun Zhao, Lei Huang, Haixin Ge +1
We introduce Embedded Stroop, a controlled diagnostic paradigm for measuring image-embedded prompt interference in Multimodal Large Language Models (MLLMs), where the query is rend…
CoE-Ops: Collaboration of LLM-based Experts for AIOps Question-Answering
Jinkun Zhao, Yuanshuai Wang, Xingjian Zhang +6
With the rapid evolution of artificial intelligence, AIOps has emerged as a prominent paradigm in DevOps. Lots of work has been proposed to improve the performance of different AIO…
Bench-CoE: a Framework for Collaboration of Experts from Benchmark
Yuanshuai Wang, Xingjian Zhang, Jinkun Zhao +5
Large Language Models (LLMs) are key technologies driving intelligent systems to handle multiple tasks. To meet the demands of various tasks, an increasing number of LLMs-driven ex…