21 citations · 48 across the 10 of their papers we have counts for
10 papers
PRISM Edit: One Vector for All Temporal Answers
Chen Huang, Qi Zheng, Ruiqin Zheng +2
Model editing keeps large language models (LLMs) up to date without retraining, but temporal facts expose a limitation of the prevailing locate-and-edit paradigm: an update is not…
Beyond Prompt Engineering: Robust Behavior Control in LLMs via Steering Target Atoms
Mengru Wang, Ziwen Xu, Shengyu Mao +4
Precise control over language model generation is vital for ensuring both safety and reliability. Although prompt engineering and steering are commonly used to intervene in model b…
Effective Demonstration Annotation for In-Context Learning via Language Model-Based Determinantal Point Process
Peng Wang, Xiaobin Wang, Chao Lou +3
In-context learning (ICL) is a few-shot learning paradigm that involves learning mappings through input-output pairs and appropriately applying them to new instances. Despite the r…
RaFe: Ranking Feedback Improves Query Rewriting for RAG
Shengyu Mao, Yong Jiang, Boli Chen +7
As Large Language Models (LLMs) and Retrieval Augmentation Generation (RAG) techniques have evolved, query rewriting has been widely incorporated into the RAG system for downstream…
Editing Conceptual Knowledge for Large Language Models
Xiaohan Wang, Shengyu Mao, Ningyu Zhang +6
Recently, there has been a growing interest in knowledge editing for Large Language Models (LLMs). Current approaches and evaluations merely explore the instance-level editing, whi…
A Comprehensive Study of Knowledge Editing for Large Language Models
Ningyu Zhang, Yunzhi Yao, Bozhong Tian +19
Large Language Models (LLMs) have shown extraordinary capabilities in understanding and generating text that closely mirrors human communication. However, a primary limitation lies…
Editing Personality for Large Language Models
Shengyu Mao, Xiaohan Wang, Mengru Wang +4
This paper introduces an innovative task focused on editing the personality traits of Large Language Models (LLMs). This task seeks to adjust the models' responses to opinion-relat…
SPEECH: Structured Prediction with Energy-Based Event-Centric Hyperspheres
Shumin Deng, Shengyu Mao, Ningyu Zhang +1
Event-centric structured prediction involves predicting structured outputs of events. In most NLP cases, event structures are complex with manifold dependency, and it is challengin…
Knowledge Rumination for Pre-trained Language Models
Yunzhi Yao, Peng Wang, Shengyu Mao +4
Previous studies have revealed that vanilla pre-trained language models (PLMs) lack the capacity to handle knowledge-intensive NLP tasks alone; thus, several works have attempted t…
Schema-aware Reference as Prompt Improves Data-Efficient Knowledge Graph Construction
Yunzhi Yao, Shengyu Mao, Ningyu Zhang +4
With the development of pre-trained language models, many prompt-based approaches to data-efficient knowledge graph construction have been proposed and achieved impressive performa…