34 citations · 97 across the 36 of their papers we have counts for
13 papers · 1 filter
GUI Agents with Foundation Models: A Comprehensive Survey
Shuai Wang, Weiwen Liu, Jingxuan Chen +12
Recent advances in foundation models, particularly Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs), have facilitated the development of intelligent agents…
SPA-Bench: A Comprehensive Benchmark for SmartPhone Agent Evaluation
Jingxuan Chen, Derek Yuen, Bin Xie +14
Smartphone agents are increasingly important for helping users control devices efficiently, with (Multimodal) Large Language Model (MLLM)-based approaches emerging as key contender…
RethinkMCTS: Refining Erroneous Thoughts in Monte Carlo Tree Search for Code Generation
Qingyao Li, Wei Xia, Kounianhua Du +5
Tree search methods have demonstrated impressive performance in code generation. Previous methods combine tree search with reflection that summarizes past mistakes to achieve itera…
ToolACE: Winning the Points of LLM Function Calling
Weiwen Liu, Xu Huang, Xingshan Zeng +24
Function calling significantly extends the application boundary of large language models, where high-quality and diverse training data is critical for unlocking this capability. Ho…
SINKT: A Structure-Aware Inductive Knowledge Tracing Model with Large Language Model
Lingyue Fu, Hao Guan, Kounianhua Du +6
Knowledge Tracing (KT) aims to determine whether students will respond correctly to the next question, which is a crucial task in intelligent tutoring systems (ITS). In educational…
Entropy Law: The Story Behind Data Compression and LLM Performance
Mingjia Yin, Chuhan Wu, Yufei Wang +7
Data is the cornerstone of large language models (LLMs), but not all data is useful for model learning. Carefully selected data can better elicit the capabilities of LLMs with much…
CoIR: A Comprehensive Benchmark for Code Information Retrieval Models
Xiangyang Li, Kuicai Dong, Yi Quan Lee +5
Despite the substantial success of Information Retrieval (IR) in various NLP tasks, most IR systems predominantly handle queries and corpora in natural language, neglecting the dom…
Evaluating the External and Parametric Knowledge Fusion of Large Language Models
Hao Zhang, Yuyang Zhang, Xiaoguang Li +8
Integrating external knowledge into large language models (LLMs) presents a promising solution to overcome the limitations imposed by their antiquated and static parametric memory.…
CELA: Cost-Efficient Language Model Alignment for CTR Prediction
Xingmei Wang, Weiwen Liu, Xiaolong Chen +8
Click-Through Rate (CTR) prediction holds a paramount position in recommender systems. The prevailing ID-based paradigm underperforms in cold-start scenarios due to the skewed dist…
CodeGRAG: Bridging the Gap between Natural Language and Programming Language via Graphical Retrieval Augmented Generation
Kounianhua Du, Jizheng Chen, Renting Rui +7
Utilizing large language models to generate codes has shown promising meaning in software development revolution. Despite the intelligence shown by the large language models, their…
WESE: Weak Exploration to Strong Exploitation for LLM Agents
Xu Huang, Weiwen Liu, Xiaolong Chen +5
Recently, large language models (LLMs) have demonstrated remarkable potential as an intelligent agent. However, existing researches mainly focus on enhancing the agent's reasoning…
Understanding the planning of LLM agents: A survey
Xu Huang, Weiwen Liu, Xiaolong Chen +6
As Large Language Models (LLMs) have shown significant intelligence, the progress to leverage LLMs as planning modules of autonomous agents has attracted more attention. This surve…
Planning, Creation, Usage: Benchmarking LLMs for Comprehensive Tool Utilization in Real-World Complex Scenarios
Shijue Huang, Wanjun Zhong, Jianqiao Lu +10
The recent trend of using Large Language Models (LLMs) as tool agents in real-world applications underscores the necessity for comprehensive evaluations of their capabilities, part…