2 citations · 2 across the 15 of their papers we have counts for
16 papers
Demystifying the Privacy-Utility Trade-off in LLM Interactions
Zhenhua Liu, Zhanxu Xie, Junjie Yu +3
The integration of Large Language Models into daily tasks relies on context-rich instructions, inevitably exposing sensitive user information. Current privacy-preserving methods ty…
What Makes Agent Memory Useful for Reliable Unanswerable Question Handling?
Chuanyuan Tan, Junjie Yu, Yuxin Wang +3
Reliable handling of unanswerable questions (UAQs) is critical for trustworthy LLM-based agents. Although memory is widely used in agent systems, its role in reliable UAQ handling…
Speed Always Wins: A Survey on Efficient Architectures for Large Language Models
Weigao Sun, Jiaxi Hu, Yucheng Zhou +12
Large Language Models (LLMs) have delivered impressive results in language understanding, generation, reasoning, and pushes the ability boundary of multimodal models. Transformer m…
Is Fine-Tuning an Effective Solution? Reassessing Knowledge Editing for Unstructured Data
Hao Xiong, Chuanyuan Tan, Wenliang Chen
Unstructured Knowledge Editing (UKE) is crucial for updating the relevant knowledge of large language models (LLMs). It focuses on unstructured inputs, such as long or free-form te…
UAQFact: Evaluating Factual Knowledge Utilization of LLMs on Unanswerable Questions
Chuanyuan Tan, Wenbiao Shao, Hao Xiong +4
Handling unanswerable questions (UAQ) is crucial for LLMs, as it helps prevent misleading responses in complex situations. While previous studies have built several datasets to ass…
Chain-of-Tools: Utilizing Massive Unseen Tools in the CoT Reasoning of Frozen Language Models
Mengsong Wu, Tong Zhu, Han Han +3
Tool learning can further broaden the usage scenarios of large language models (LLMs). However most of the existing methods either need to finetune that the model can only use tool…
Evolutionary Guided Decoding: Iterative Value Refinement for LLMs
Zhenhua Liu, Lijun Li, Ruizhe Chen +5
While guided decoding, especially value-guided methods, has emerged as a cost-effective alternative for controlling language model outputs without re-training models, its effective…
NesTools: A Dataset for Evaluating Nested Tool Learning Abilities of Large Language Models
Han Han, Tong Zhu, Xiang Zhang +3
Large language models (LLMs) combined with tool learning have gained impressive results in real-world applications. During tool learning, LLMs may call multiple tools in nested ord…
Learning to Refuse: Towards Mitigating Privacy Risks in LLMs
Zhenhua Liu, Tong Zhu, Chuanyuan Tan +1
Large language models (LLMs) exhibit remarkable capabilities in understanding and generating natural language. However, these models can inadvertently memorize private information,…
Dynamic Data Mixing Maximizes Instruction Tuning for Mixture-of-Experts
Tong Zhu, Daize Dong, Xiaoye Qu +3
Mixture-of-Experts (MoE) models have shown remarkable capability in instruction tuning, especially when the number of tasks scales. However, previous methods simply merge all train…
Probing Language Models for Pre-training Data Detection
Zhenhua Liu, Tong Zhu, Chuanyuan Tan +3
Large Language Models (LLMs) have shown their impressive capabilities, while also raising concerns about the data contamination problems due to privacy issues and leakage of benchm…
Seal-Tools: Self-Instruct Tool Learning Dataset for Agent Tuning and Detailed Benchmark
Mengsong Wu, Tong Zhu, Han Han +3
This paper presents a new tool learning dataset Seal-Tools, which contains self-instruct API-like tools. Seal-Tools not only offers a large number of tools, but also includes insta…
MoPE: Mixture of Prefix Experts for Zero-Shot Dialogue State Tracking
Tianwen Tang, Tong Zhu, Haodong Liu +3
Zero-shot dialogue state tracking (DST) transfers knowledge to unseen domains, reducing the cost of annotating new datasets. Previous zero-shot DST models mainly suffer from domain…
DiffusionDialog: A Diffusion Model for Diverse Dialog Generation with Latent Space
Jianxiang Xiang, Zhenhua Liu, Haodong Liu +3
In real-life conversations, the content is diverse, and there exists the one-to-many problem that requires diverse generation. Previous studies attempted to introduce discrete or G…
Controllable and Diverse Data Augmentation with Large Language Model for Low-Resource Open-Domain Dialogue Generation
Zhenhua Liu, Tong Zhu, Jianxiang Xiang +1
Data augmentation (DA) is crucial to mitigate model training instability and over-fitting problems in low-resource open-domain dialogue generation. However, traditional DA methods…
Mirror: A Universal Framework for Various Information Extraction Tasks
Tong Zhu, Junfei Ren, Zijian Yu +7
Sharing knowledge between information extraction tasks has always been a challenge due to the diverse data formats and task variations. Meanwhile, this divergence leads to informat…