1 citations · 1 across the 5 of their papers we have counts for
6 papers
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…
Neural Approaches to SAT Solving: Design Choices and Interpretability
David Mojžíšek, Jan Hůla, Ziwei Li +2
In this contribution, we provide a comprehensive evaluation of graph neural networks applied to Boolean satisfiability problems, accompanied by an intuitive explanation of the mech…
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…
HYBRIDMIND: Meta Selection of Natural Language and Symbolic Language for Enhanced LLM Reasoning
Simeng Han, Tianyu Liu, Chuhan Li +2
LLMs approach logical and mathematical reasoning through natural or symbolic languages. While natural language offers human-accessible flexibility but suffers from ambiguity, symbo…