2 citations · 8 across the 20 of their papers we have counts for
21 papers
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…
LLaMA-MoE v2: Exploring Sparsity of LLaMA from Perspective of Mixture-of-Experts with Post-Training
Xiaoye Qu, Daize Dong, Xuyang Hu +3
Recently, inspired by the concept of sparsity, Mixture-of-Experts (MoE) models have gained increasing popularity for scaling model size while keeping the number of activated parame…
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…
CLIP-MoE: Towards Building Mixture of Experts for CLIP with Diversified Multiplet Upcycling
Jihai Zhang, Xiaoye Qu, Tong Zhu +1
Contrastive Language-Image Pre-training (CLIP) has become a cornerstone in multimodal intelligence. However, recent studies discovered that CLIP can only encode one aspect of the f…
ConflictBank: A Benchmark for Evaluating the Influence of Knowledge Conflicts in LLM
Zhaochen Su, Jun Zhang, Xiaoye Qu +6
Large language models (LLMs) have achieved impressive advancements across numerous disciplines, yet the critical issue of knowledge conflicts, a major source of hallucinations, has…
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,…
LLaMA-MoE: Building Mixture-of-Experts from LLaMA with Continual Pre-training
Tong Zhu, Xiaoye Qu, Daize Dong +4
Mixture-of-Experts (MoE) has gained increasing popularity as a promising framework for scaling up large language models (LLMs). However, training MoE from scratch in a large-scale…
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…
Living in the Moment: Can Large Language Models Grasp Co-Temporal Reasoning?
Zhaochen Su, Juntao Li, Jun Zhang +6
Temporal reasoning is fundamental for large language models (LLMs) to comprehend the world. Current temporal reasoning datasets are limited to questions about single or isolated ev…
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…
Timo: Towards Better Temporal Reasoning for Language Models
Zhaochen Su, Jun Zhang, Tong Zhu +4
Reasoning about time is essential for Large Language Models (LLMs) to understand the world. Previous works focus on solving specific tasks, primarily on time-sensitive question ans…
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…
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…
CED: Catalog Extraction from Documents
Tong Zhu, Guoliang Zhang, Zechang Li +7
Sentence-by-sentence information extraction from long documents is an exhausting and error-prone task. As the indicator of document skeleton, catalogs naturally chunk documents int…
Efficient Document-level Event Extraction via Pseudo-Trigger-aware Pruned Complete Graph
Tong Zhu, Xiaoye Qu, Wenliang Chen +4
Most previous studies of document-level event extraction mainly focus on building argument chains in an autoregressive way, which achieves a certain success but is inefficient in b…
Towards Accurate and Consistent Evaluation: A Dataset for Distantly-Supervised Relation Extraction
Tong Zhu, Haitao Wang, Junjie Yu +4
In recent years, distantly-supervised relation extraction has achieved a certain success by using deep neural networks. Distant Supervision (DS) can automatically generate large-sc…
CCKS 2019 Shared Task on Inter-Personal Relationship Extraction
Haitao Wang, Zhengqiu He, Tong Zhu +3
The CCKS2019 shared task was devoted to inter-personal relationship extraction. Given two person entities and at least one sentence containing these two entities, participating tea…