5 citations · 7 across the 12 of their papers we have counts for
12 papers
Where to Look and What to Use: Retrieve-Localize-Generate for Long-Term Conversational Memory Question Answering
Yifan Wang, Xinkui Lin, Yongxiu Xu +9
Retrieval-augmented generation (RAG) enables large language models (LLMs) to answer questions by accessing external knowledge and has been widely adopted for long-term conversation…
AppDeltaWorld: Transition-Grounded Delta Code World Model for Mobile GUI Agents
Weikai Xu, Yunren Feng, Haoxiang Lei +6
Mobile GUI agents can operate apps through pixel perception and touch actions, making them a promising interface for collecting and improving long-horizon mobile interaction polici…
CoME: Empowering Channel-of-Mobile-Experts with Informative Hybrid-Capabilities Reasoning
Yuxuan Liu, Weikai Xu, Kun Huang +9
Mobile Agents can autonomously execute user instructions, which requires hybrid-capabilities reasoning, including screen summary, subtask planning, action decision and action funct…
More is not always better? Enhancing Many-Shot In-Context Learning with Differentiated and Reweighting Objectives
Xiaoqing Zhang, Ang Lv, Yuhan Liu +6
Large language models (LLMs) excel at few-shot in-context learning (ICL) without requiring parameter updates. However, as ICL demonstrations increase from a few to many, performanc…
CausalStock: Deep End-to-end Causal Discovery for News-driven Stock Movement Prediction
Shuqi Li, Yuebo Sun, Yuxin Lin +3
There are two issues in news-driven multi-stock movement prediction tasks that are not well solved in the existing works. On the one hand, "relation discovery" is a pivotal part wh…
MobileVLM: A Vision-Language Model for Better Intra- and Inter-UI Understanding
Qinzhuo Wu, Weikai Xu, Wei Liu +6
Recently, mobile AI agents based on VLMs have been gaining increasing attention. These works typically utilize VLM as a foundation, fine-tuning it with instruction-based mobile dat…
Mobile-Bench: An Evaluation Benchmark for LLM-based Mobile Agents
Shihan Deng, Weikai Xu, Hongda Sun +8
With the remarkable advancements of large language models (LLMs), LLM-based agents have become a research hotspot in human-computer interaction. However, there is a scarcity of ben…
Harnessing Multi-Role Capabilities of Large Language Models for Open-Domain Question Answering
Hongda Sun, Yuxuan Liu, Chengwei Wu +5
Open-domain question answering (ODQA) has emerged as a pivotal research spotlight in information systems. Existing methods follow two main paradigms to collect evidence: (1) The \t…
"In Dialogues We Learn": Towards Personalized Dialogue Without Pre-defined Profiles through In-Dialogue Learning
Chuanqi Cheng, Quan Tu, Shuo Shang +4
Personalized dialogue systems have gained significant attention in recent years for their ability to generate responses in alignment with different personas. However, most existing…
CharacterEval: A Chinese Benchmark for Role-Playing Conversational Agent Evaluation
Quan Tu, Shilong Fan, Zihang Tian +1
Recently, the advent of large language models (LLMs) has revolutionized generative agents. Among them, Role-Playing Conversational Agents (RPCAs) attract considerable attention due…
DetermLR: Augmenting LLM-based Logical Reasoning from Indeterminacy to Determinacy
Hongda Sun, Weikai Xu, Wei Liu +5
Recent advances in large language models (LLMs) have revolutionized the landscape of reasoning tasks. To enhance the capabilities of LLMs to emulate human reasoning, prior studies…
CharacterChat: Learning towards Conversational AI with Personalized Social Support
Quan Tu, Chuanqi Chen, Jinpeng Li +5
In our modern, fast-paced, and interconnected world, the importance of mental well-being has grown into a matter of great urgency. However, traditional methods such as Emotional Su…