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A Survey on Large Language Model based Autonomous Agents

A Survey on Large Language Model based Autonomous Agents

Autonomous agents have long been a prominent research topic in the academic community. Previous research in this field often focuses on training agents with limited knowledge within isolated environments, which diverges significantly from the human learning processes, and thus makes the agents hard to achieve human-like decisions. Recently, through the acquisition of vast amounts of web knowledge, large language models (LLMs) have demonstrated remarkable potential in achieving human-level intelligence. This has sparked an upsurge in studies investigating autonomous agents based on LLMs. To harness the full potential of LLMs, researchers have devised diverse agent architectures tailored to different applications. In this paper, we present a comprehensive survey of these studies, delivering a systematic review of the field of autonomous agents from a holistic perspective. More specifically, our focus lies in the construction of LLM-based agents, for which we propose a unified framework that encompasses a majority of the previous work. Additionally, we provide a summary of the various applications of LLM-based AI agents in the domains of social science, natural science, and engineering. Lastly, we discuss the commonly employed evaluation strategies for LLM-based AI agents. Based on the previous studies, we also present several challenges and future directions in this field.

User Behavior Simulation with Large Language Model based Agents

User Behavior Simulation with Large Language Model based Agents

Simulating high quality user behavior data has always been a fundamental problem in human-centered applications, where the major difficulty originates from the intricate mechanism of human decision process. Recently, substantial evidences have suggested that by learning huge amounts of web knowledge, large language models (LLMs) can achieve human-like intelligence. We believe these models can provide significant opportunities to more believable user behavior simulation. To inspire such direction, we propose an LLM-based agent framework and design a sandbox environment to simulate real user behaviors. Based on extensive experiments, we find that the simulated behaviors of our method are very close to the ones of real humans. Concerning potential applications, we simulate and study two social phenomenons including (1) information cocoons and (2) user conformity behaviors. This research provides novel simulation paradigms for human-centered applications.

REASONER: An Explainable Recommendation Dataset with Multi-aspect Real User Labeled Ground Truths: Towards more Measurable Explainable Recommendation

REASONER: An Explainable Recommendation Dataset with Multi-aspect Real User Labeled Ground Truths: Towards more Measurable Explainable Recommendation

REASONER is an explainable recommendation dataset. It contains the ground truths for multiple explanation purposes, for example, enhancing the recommendation persuasiveness, informativeness and so on. In this dataset, the ground truth annotators are exactly the people who produce the user-item interactions, and they can make selections from the explanation candidates with multi-modalities. This dataset can be widely used for explainable recsys, unbiased recommendation and psychology-informed recommendation.

RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms

RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms

In recent years, there are a large number of recommendation algorithms proposed in the literature, from traditional collaborative filtering to deep learning algorithms. However, the concerns about how to standardize open source implementation of recommendation algorithms continually increase in the research community. In the light of this challenge, we propose a unified, comprehensive and efficient recommender system library called RecBole, which provides a unified framework to develop and reproduce recommendation algorithms for research purpose. In this library, we implement 73 recommendation models on 28 benchmark datasets, covering the categories of general recommendation, sequential recommendation, context-aware recommendation and knowledge-based recommendation. We implement the RecBole library based on PyTorch, which is one of the most popular deep learning frameworks. Our library is featured in many aspects, including general and extensible data structures, comprehensive benchmark models and datasets, efficient GPU-accelerated execution, and extensive and standard evaluation protocols. We provide a series of auxiliary functions, tools, and scripts to facilitate the use of this library, such as automatic parameter tuning and break-point resume. Such a framework is useful to standardize the implementation and evaluation of recommender systems.

Measuring the "Why" in Recommender Systems: a Comprehensive Survey on the Evaluation of Explainable Recommendation

Measuring the "Why" in Recommender Systems: a Comprehensive Survey on the Evaluation of Explainable Recommendation

In the field of explainable recommendation, how to evaluate the explanations has long been a fundamental yet not clearly discussed problem. In this survey, we aim to provide a systematic and comprehensive summarization on existing evaluation strategies. The contents of this survey are concluded from more than 100 papers from top-tier conferences like IJCAI, AAAI, TheWebConf, SIGIR, KDD, Recsys, UMAP and IUI, and the complete comparisons are presented at there.

Explainable Recommendation: A Survey and New Perspectives

Explainable Recommendation: A Survey and New Perspectives

In this survey, we (1) provide a chronological research timeline of explainable recommendation, (2) present a two-dimensional taxonomy to classify existing explainable recommendation research, and (3) summarize how explainable recommendation applies to different recommendation tasks.