This paper explores the emerging knowledge-driven autonomous driving technologies. Our
investigation highlights the limitations of current autonomous driving systems, in particular …

In this paper, we introduce InfiAgent-DABench, the first benchmark specifically designed to
evaluate LLM-based agents on data analysis tasks. These tasks require agents to end-to …

Large Language Models (LLMs) excel at tackling various natural language tasks. However,
due to the significant costs involved in re-training or fine-tuning them, they remain largely …

With large language models (LLMs) achieving remarkable breakthroughs in NLP domains,
LLM-enhanced recommender systems have received much attention and have been …

Conversational Agents (CAs) acting as peer supporters have been widely studied and
demonstrated beneficial for people's mental health. However, previous peer support CAs …

Large Language Models (LLMs) have shown impressive performance in various domains,
prompting researchers to explore their potential application in recommendation systems …

With the emergence of large language models (LLMs), LLM-powered multi-agent systems
(LLM-MA systems) have been proposed to tackle real-world tasks. However, their agents …

Large Language Models (LLMs) have shown versatility in various Natural Language
Processing (NLP) tasks, including their potential as effective question-answering systems …

Large language models (LLMs) offer unprecedented flexibility in procedural generation,
enabling the creation of dynamic video game storylines that evolve with user input. A critical …

In this paper, we address the lifelong sequential behavior incomprehension problem in large
language models (LLMs) for recommendation, where LLMs struggle to extract useful …