74 citations · 106 across the 26 of their papers we have counts for
12 papers · 1 filter
Mind the Gap in Cultural Alignment: Task-Aware Culture Management for Large Language Models
Binchi Zhang, Xujiang Zhao, Jundong Li +2
Large language models (LLMs) are increasingly deployed in culturally sensitive real-world tasks. However, existing cultural alignment approaches fail to align LLMs' broad cultural…
Human Texts Are Outliers: Detecting LLM-generated Texts via Out-of-distribution Detection
Cong Zeng, Shengkun Tang, Yuanzhou Chen +6
The rapid advancement of large language models (LLMs) such as ChatGPT, DeepSeek, and Claude has significantly increased the presence of AI-generated text in digital communication.…
DeepSieve: Information Sieving via LLM-as-a-Knowledge-Router
Minghao Guo, Qingcheng Zeng, Xujiang Zhao +5
Large Language Models (LLMs) excel at many reasoning tasks but struggle with knowledge-intensive queries due to their inability to dynamically access up-to-date or domain-specific…
MixLLM: Dynamic Routing in Mixed Large Language Models
Xinyuan Wang, Yanchi Liu, Wei Cheng +5
Large Language Models (LLMs) exhibit potential artificial generic intelligence recently, however, their usage is costly with high response latency. Given mixed LLMs with their own…
SAUP: Situation Awareness Uncertainty Propagation on LLM Agent
Qiwei Zhao, Xujiang Zhao, Yanchi Liu +7
Large language models (LLMs) integrated into multistep agent systems enable complex decision-making processes across various applications. However, their outputs often lack reliabi…
Pruning as a Domain-specific LLM Extractor
Nan Zhang, Yanchi Liu, Xujiang Zhao +5
Large Language Models (LLMs) have exhibited remarkable proficiency across a wide array of NLP tasks. However, the escalation in model size also engenders substantial deployment cos…
Uncertainty Quantification for In-Context Learning of Large Language Models
Chen Ling, Xujiang Zhao, Xuchao Zhang +10
In-context learning has emerged as a groundbreaking ability of Large Language Models (LLMs) and revolutionized various fields by providing a few task-relevant demonstrations in the…
Open-ended Commonsense Reasoning with Unrestricted Answer Scope
Chen Ling, Xuchao Zhang, Xujiang Zhao +7
Open-ended Commonsense Reasoning is defined as solving a commonsense question without providing 1) a short list of answer candidates and 2) a pre-defined answer scope. Conventional…
PrivacyMind: Large Language Models Can Be Contextual Privacy Protection Learners
Yijia Xiao, Yiqiao Jin, Yushi Bai +10
The proliferation of Large Language Models (LLMs) has driven considerable interest in fine-tuning them with domain-specific data to create specialized language models. Nevertheless…
Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty
Chen Ling, Xujiang Zhao, Xuchao Zhang +8
Open Information Extraction (OIE) task aims at extracting structured facts from unstructured text, typically in the form of (subject, relation, object) triples. Despite the potenti…
Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey
Chen Ling, Xujiang Zhao, Jiaying Lu +21
Large language models (LLMs) have significantly advanced the field of natural language processing (NLP), providing a highly useful, task-agnostic foundation for a wide range of app…
Dynamic Prompting: A Unified Framework for Prompt Tuning
Xianjun Yang, Wei Cheng, Xujiang Zhao +3
It has been demonstrated that the art of prompt tuning is highly effective in efficiently extracting knowledge from pretrained foundation models, encompassing pretrained language m…