5 citations · 17 across the 15 of their papers we have counts for
7 papers · 1 filter
Explainable Behavior Cloning: Teaching Large Language Model Agents through Learning by Demonstration
Yanchu Guan, Dong Wang, Yan Wang +5
Autonomous mobile app interaction has become increasingly important with growing complexity of mobile applications. Developing intelligent agents that can effectively navigate and…
FAMMA: A Benchmark for Financial Domain Multilingual Multimodal Question Answering
Siqiao Xue, Xiaojing Li, Fan Zhou +3
In this paper, we introduce FAMMA, an open-source benchmark for \underline{f}in\underline{a}ncial \underline{m}ultilingual \underline{m}ultimodal question \underline{a}nswering (QA…
DB-GPT-Hub: Towards Open Benchmarking Text-to-SQL Empowered by Large Language Models
Fan Zhou, Siqiao Xue, Danrui Qi +8
Large language models (LLMs) becomes the dominant paradigm for the challenging task of text-to-SQL. LLM-empowered text-to-SQL methods are typically categorized into prompting-based…
Sora Detector: A Unified Hallucination Detection for Large Text-to-Video Models
Zhixuan Chu, Lei Zhang, Yichen Sun +4
The rapid advancement in text-to-video (T2V) generative models has enabled the synthesis of high-fidelity video content guided by textual descriptions. Despite this significant pro…
A Causal Explainable Guardrails for Large Language Models
Zhixuan Chu, Yan Wang, Longfei Li +3
Large Language Models (LLMs) have shown impressive performance in natural language tasks, but their outputs can exhibit undesirable attributes or biases. Existing methods for steer…
Professional Agents -- Evolving Large Language Models into Autonomous Experts with Human-Level Competencies
Zhixuan Chu, Yan Wang, Feng Zhu +3
The advent of large language models (LLMs) such as ChatGPT, PaLM, and GPT-4 has catalyzed remarkable advances in natural language processing, demonstrating human-like language flue…
LLM-Guided Multi-View Hypergraph Learning for Human-Centric Explainable Recommendation
Zhixuan Chu, Yan Wang, Qing Cui +4
As personalized recommendation systems become vital in the age of information overload, traditional methods relying solely on historical user interactions often fail to fully captu…