2 citations · 2 across the 5 of their papers we have counts for
6 papers
Does Every Instance Count Equally? Order Effects in LLM Multi-Instance Processing
Jingxuan Chen, Mohammad Taher Pilehvar, Jose Camacho-Collados
Users increasingly rely on large language models (LLMs) to analyse collections of independent inputs, such as product reviews or social media posts, and return a single aggregated…
Understanding LLM Performance Degradation in Multi-Instance Processing: The Roles of Instance Count and Context Length
Jingxuan Chen, Mohammad Taher Pilehvar, Jose Camacho-Collados
Users often rely on Large Language Models (LLMs) for processing multiple documents or performing analysis over a number of instances. For example, analysing the overall sentiment o…
Beyond Syntax: Action Semantics Learning for App Agents
Bohan Tang, Dezhao Luo, Jianheng Liu +5
The recent development of Large Language Models (LLMs) enables the rise of App agents that interpret user intent and operate smartphone Apps through actions such as clicking and sc…
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…
Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning
Filippos Christianos, Georgios Papoudakis, Matthieu Zimmer +13
A key method for creating Artificial Intelligence (AI) agents is Reinforcement Learning (RL). However, constructing a standalone RL policy that maps perception to action directly e…