21 citations · 21 across the 7 of their papers we have counts for
7 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…
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…
A Survey on Hallucination in Large Vision-Language Models
Hanchao Liu, Wenyuan Xue, Yifei Chen +6
Recent development of Large Vision-Language Models (LVLMs) has attracted growing attention within the AI landscape for its practical implementation potential. However, ``hallucinat…
ASR Error Correction with Constrained Decoding on Operation Prediction
Jingyuan Yang, Rongjun Li, Wei Peng
Error correction techniques remain effective to refine outputs from automatic speech recognition (ASR) models. Existing end-to-end error correction methods based on an encoder-deco…