Spartan Daily, September 26, 1974

Volume 63, Issue 10https://scholarworks.sjsu.edu/spartandaily/5895/thumbnail.jp

Clover Quiz: a trivia game powered by DBpedia

Producción CientíficaDBpedia is a large-scale and multilingual knowledge base generated by extracting structured data from Wikipedia. There have been several attempts to use DBpedia to generate questions for trivia games, but these initiatives have not succeeded to produce large, varied, and entertaining question sets. Moreover, latency is too high for an interactive game if questions are created by submitting live queries to the public DBpedia endpoint. These limitations are addressed in Clover Quiz, a turn-based multiplayer trivia game for Android devices with more than 200K multiple choice questions (in English and Spanish) about different domains generated out of DBpedia. Questions are created off-line through a data extraction pipeline and a versatile template-based mechanism. A back-end server manages the question set and the associated images, while a mobile app has been developed and released in Google Play. The game is available free of charge and has been downloaded by more than 5K users since the game was released in March 2017. Players have answered more than 614K questions and the overall rating of the game is 4.3 out of 5.0. Therefore, Clover Quiz demonstrates the advantages of semantic technologies for collecting data and automating the generation of multiple choice questions in a scalable way.Ministerio de Economía, Industria y Competitividad (Projects TIN2017-85179-C3-2-R and RESET TIN2014-53199-C3-2

AutoAgents: A Framework for Automatic Agent Generation

Large language models (LLMs) have enabled remarkable advances in automated task-solving with multi-agent systems. However, most existing LLM-based multi-agent approaches rely on predefined agents to handle simple tasks, limiting the adaptability of multi-agent collaboration to different scenarios. Therefore, we introduce AutoAgents, an innovative framework that adaptively generates and coordinates multiple specialized agents to build an AI team according to different tasks. Specifically, AutoAgents couples the relationship between tasks and roles by dynamically generating multiple required agents based on task content and planning solutions for the current task based on the generated expert agents. Multiple specialized agents collaborate with each other to efficiently accomplish tasks. Concurrently, an observer role is incorporated into the framework to reflect on the designated plans and agents' responses and improve upon them. Our experiments on various benchmarks demonstrate that AutoAgents generates more coherent and accurate solutions than the existing multi-agent methods. This underscores the significance of assigning different roles to different tasks and of team cooperation, offering new perspectives for tackling complex tasks. The repository of this project is available at https://github.com/Link-AGI/AutoAgents

NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned

We review the EfficientQA competition from NeurIPS 2020. The competition focused on open-domain question answering (QA), where systems take natural language questions as input and return natural language answers. The aim of the competition was to build systems that can predict correct answers while also satisfying strict on-disk memory budgets. These memory budgets were designed to encourage contestants to explore the trade-off between storing retrieval corpora or the parameters of learned models. In this report, we describe the motivation and organization of the competition, review the best submissions, and analyze system predictions to inform a discussion of evaluation for open-domain QA