2 citations · 5 across the 9 of their papers we have counts for
8 papers · 1 filter
Fine-Grained Activation Steering: Steering Less, Achieving More
Zijian Feng, Tianjiao Li, Zixiao Zhu +7
Activation steering has emerged as a cost-effective paradigm for modifying large language model (LLM) behaviors. Existing methods typically intervene at the block level, steering t…
Rethinking Prompt Optimizers: From Prompt Merits to Optimization
Zixiao Zhu, Hanzhang Zhou, Zijian Feng +5
Prompt optimization (PO) provides a practical way to improve response quality when users lack the time or expertise to manually craft effective prompts. Existing methods typically…
Beyond the Next Token: Towards Prompt-Robust Zero-Shot Classification via Efficient Multi-Token Prediction
Junlang Qian, Zixiao Zhu, Hanzhang Zhou +3
Zero-shot text classification typically relies on prompt engineering, but the inherent prompt brittleness of large language models undermines its reliability. Minor changes in prom…
FreeCtrl: Constructing Control Centers with Feedforward Layers for Learning-Free Controllable Text Generation
Zijian Feng, Hanzhang Zhou, Zixiao Zhu +1
Controllable text generation (CTG) seeks to craft texts adhering to specific attributes, traditionally employing learning-based techniques such as training, fine-tuning, or prefix-…
Logit Separability-Driven Samples and Multiple Class-Related Words Selection for Advancing In-Context Learning
Zhu Zixiao, Feng Zijian, Zhou Hanzhang +2
Effective organization of in-context learning (ICL) demonstrations is key to improving the quality of large language model (LLM) responses. To create better sample-label pairs that…
Unveiling and Manipulating Prompt Influence in Large Language Models
Zijian Feng, Hanzhang Zhou, Zixiao Zhu +2
Prompts play a crucial role in guiding the responses of Large Language Models (LLMs). However, the intricate role of individual tokens in prompts, known as input saliency, in shapi…
UniBias: Unveiling and Mitigating LLM Bias through Internal Attention and FFN Manipulation
Hanzhang Zhou, Zijian Feng, Zixiao Zhu +2
Large language models (LLMs) have demonstrated impressive capabilities in various tasks using the in-context learning (ICL) paradigm. However, their effectiveness is often compromi…
LLMs Learn Task Heuristics from Demonstrations: A Heuristic-Driven Prompting Strategy for Document-Level Event Argument Extraction
Hanzhang Zhou, Junlang Qian, Zijian Feng +3
In this study, we investigate in-context learning (ICL) in document-level event argument extraction (EAE) to alleviate the dependency on large-scale labeled data for this task. We…