21 citations · 21 across the 7 of their papers we have counts for
5 papers · 2 filters
Evaluating the Effectiveness of Black-Box Prompt Optimization as the Scale of LLMs Continues to Grow
Ziyu Zhou, Yihang Wu, Jingyuan Yang +2
Black-Box prompt optimization methods have emerged as a promising strategy for refining input prompts to better align large language models (LLMs), thereby enhancing their task per…
WorkTeam: Constructing Workflows from Natural Language with Multi-Agents
Hanchao Liu, Rongjun Li, Weimin Xiong +2
Workflows play a crucial role in enhancing enterprise efficiency by orchestrating complex processes with multiple tools or components. However, hand-crafted workflow construction r…
Gradient Co-occurrence Analysis for Detecting Unsafe Prompts in Large Language Models
Jingyuan Yang, Bowen Yan, Rongjun Li +4
Unsafe prompts pose significant safety risks to large language models (LLMs). Existing methods for detecting unsafe prompts rely on data-driven fine-tuning to train guardrail model…
LF-Steering: Latent Feature Activation Steering for Enhancing Semantic Consistency in Large Language Models
Jingyuan Yang, Rongjun Li, Weixuan Wang +3
Large Language Models (LLMs) often generate inconsistent responses when prompted with semantically equivalent paraphrased inputs. Recently, activation steering, a technique that mo…
Enhancing Semantic Consistency of Large Language Models through Model Editing: An Interpretability-Oriented Approach
Jingyuan Yang, Dapeng Chen, Yajing Sun +3
A Large Language Model (LLM) tends to generate inconsistent and sometimes contradictory outputs when presented with a prompt that has equivalent semantics but is expressed differen…