23 citations · 37 across the 28 of their papers we have counts for
18 papers · 1 filter
CARD: Towards Conditional Design of Multi-agent Topological Structures
Tongtong Wu, Yanming Li, Ziye Tang +5
Large language model (LLM)-based multi-agent systems have shown strong capabilities in tasks such as code generation and collaborative reasoning. However, the effectiveness and rob…
QuCo-RAG: Quantifying Uncertainty from the Pre-training Corpus for Dynamic Retrieval-Augmented Generation
Dehai Min, Kailin Zhang, Tongtong Wu +1
Dynamic Retrieval-Augmented Generation adaptively determines when to retrieve during generation to mitigate hallucinations in large language models (LLMs). However, existing method…
K-DeCore: Facilitating Knowledge Transfer in Continual Structured Knowledge Reasoning via Knowledge Decoupling
Yongrui Chen, Yi Huang, Yunchang Liu +5
Continual Structured Knowledge Reasoning (CSKR) focuses on training models to handle sequential tasks, where each task involves translating natural language questions into structur…
Pandora: Leveraging Code-driven Knowledge Transfer for Unified Structured Knowledge Reasoning
Yongrui Chen, Junhao He, Linbo Fu +10
Unified Structured Knowledge Reasoning (USKR) aims to answer natural language questions by using structured sources such as tables, databases, and knowledge graphs in a unified way…
OneEval: Benchmarking LLM Knowledge-intensive Reasoning over Diverse Knowledge Bases
Yongrui Chen, Zhiqiang Liu, Jing Yu +21
Large Language Models (LLMs) have demonstrated substantial progress on reasoning tasks involving unstructured text, yet their capabilities significantly deteriorate when reasoning…
Continual Speech Learning with Fused Speech Features
Guitao Wang, Jinming Zhao, Hao Yang +3
Rapid growth in speech data demands adaptive models, as traditional static methods fail to keep pace with dynamic and diverse speech information. We introduce continuous speech lea…
Pandora: A Code-Driven Large Language Model Agent for Unified Reasoning Across Diverse Structured Knowledge
Yongrui Chen, Junhao He, Linbo Fu +10
Unified Structured Knowledge Reasoning (USKR) aims to answer natural language questions (NLQs) by using structured sources such as tables, databases, and knowledge graphs in a unif…
Hengqin-RA-v1: Advanced Large Language Model for Diagnosis and Treatment of Rheumatoid Arthritis with Dataset based Traditional Chinese Medicine
Yishen Liu
Large language models (LLMs) primarily trained on English texts, often face biases and inaccuracies in Chinese contexts. Their limitations are pronounced in fields like Traditional…
Towards LifeSpan Cognitive Systems
Yu Wang, Chi Han, Tongtong Wu +9
Building a human-like system that continuously interacts with complex environments -- whether simulated digital worlds or human society -- presents several key challenges. Central…
Double Mixture: Towards Continual Event Detection from Speech
Jingqi Kang, Tongtong Wu, Jinming Zhao +6
Speech event detection is crucial for multimedia retrieval, involving the tagging of both semantic and acoustic events. Traditional ASR systems often overlook the interplay between…
Continual Learning for Large Language Models: A Survey
Tongtong Wu, Linhao Luo, Yuan-Fang Li +3
Large language models (LLMs) are not amenable to frequent re-training, due to high training costs arising from their massive scale. However, updates are necessary to endow LLMs wit…
Large Language Models Can Better Understand Knowledge Graphs Than We Thought
Xinbang Dai, Yuncheng Hua, Tongtong Wu +3
When we integrate factual knowledge from knowledge graphs (KGs) into large language models (LLMs) to enhance their performance, the cost of injection through training increases wit…
Towards Event Extraction from Speech with Contextual Clues
Jingqi Kang, Tongtong Wu, Jinming Zhao +4
While text-based event extraction has been an active research area and has seen successful application in many domains, extracting semantic events from speech directly is an under-…
Can LLMs Evaluate Complex Attribution in QA? Automatic Benchmarking using Knowledge Graphs
Nan Hu, Jiaoyan Chen, Yike Wu +6
Attributed Question Answering (AQA) has attracted wide attention, but there are still several limitations in evaluating the attributions, including lacking fine-grained attribution…
Variational Autoencoder with Disentanglement Priors for Low-Resource Task-Specific Natural Language Generation
Zhuang Li, Lizhen Qu, Qiongkai Xu +3
In this paper, we propose a variational autoencoder with disentanglement priors, VAE-DPRIOR, for task-specific natural language generation with none or a handful of task-specific l…
Adaptive Knowledge-Enhanced Bayesian Meta-Learning for Few-shot Event Detection
Shirong Shen, Tongtong Wu, Guilin Qi +3
Event detection (ED) aims at detecting event trigger words in sentences and classifying them into specific event types. In real-world applications, ED typically does not have suffi…
Curriculum-Meta Learning for Order-Robust Continual Relation Extraction
Tongtong Wu, Xuekai Li, Yuan-Fang Li +4
Continual relation extraction is an important task that focuses on extracting new facts incrementally from unstructured text. Given the sequential arrival order of the relations, t…
Few-Shot Complex Knowledge Base Question Answering via Meta Reinforcement Learning
Yuncheng Hua, Yuan-Fang Li, Gholamreza Haffari +2
Complex question-answering (CQA) involves answering complex natural-language questions on a knowledge base (KB). However, the conventional neural program induction (NPI) approach e…