{"id":10380,"date":"2024-02-28T10:18:11","date_gmt":"2024-02-28T10:18:11","guid":{"rendered":"https:\/\/dailyai.com\/?p=10380"},"modified":"2024-02-28T10:18:11","modified_gmt":"2024-02-28T10:18:11","slug":"meta-ucsd-introduce-toolverifier-to-improve-llm-tool-calls","status":"publish","type":"post","link":"https:\/\/dailyai.com\/pt\/2024\/02\/meta-ucsd-introduce-toolverifier-to-improve-llm-tool-calls\/","title":{"rendered":"Meta e UCSD apresentam o ToolVerifier para melhorar as chamadas de ferramentas LLM"},"content":{"rendered":"

Os investigadores da Meta e da Universidade da Calif\u00f3rnia em San Diego (UCSD) desenvolveram o ToolVerifier, um m\u00e9todo que melhora a forma como os LLMs chamam e interagem com as ferramentas de software.<\/strong><\/p>\n

Para que os LLM se tornem \u00fateis como assistentes gerais ou agentes<\/a>Para isso, \u00e9 necess\u00e1rio ensin\u00e1-los a usar v\u00e1rias ferramentas ou APIs. O ajuste fino de um LLM para usar uma ferramenta espec\u00edfica funciona, mas o verdadeiro desafio \u00e9 que um LLM interaja com novas ferramentas sem a necessidade de ajustes finos ou de demonstra\u00e7\u00f5es pontuais.<\/p>\n

Quando duas ferramentas s\u00e3o muito semelhantes, pode ser especialmente dif\u00edcil para o LLM escolher a ferramenta correcta para atingir o seu objetivo. O m\u00e9todo atual de fornecer v\u00e1rios exemplos de poucos disparos para cada ferramenta pode consumir muito da janela de contexto dispon\u00edvel para um LLM tamb\u00e9m.<\/p>\n

O ToolVerifier \u00e9 um m\u00e9todo de auto-verifica\u00e7\u00e3o que permite ao LLM fazer perguntas a si pr\u00f3prio, de modo a determinar qual a ferramenta a utilizar e quais os par\u00e2metros a passar \u00e0 ferramenta.<\/p>\n

Para ajudar o LLM, o ToolVerifier come\u00e7a por selecionar a ferramenta mais adequada a partir de uma biblioteca de op\u00e7\u00f5es e depois gera os par\u00e2metros apropriados. Em cada uma dessas etapas, ele gera perguntas para ajudar a avaliar suas escolhas e discriminar entre ferramentas candidatas semelhantes.<\/p>\n

Eis um exemplo do documento de investiga\u00e7\u00e3o que mostra o processo de sele\u00e7\u00e3o de ferramentas e de clarifica\u00e7\u00e3o de par\u00e2metros.<\/p>\n

\"\"
O ToolVerifier primeiro identifica as duas principais ferramentas e gera uma pergunta de verifica\u00e7\u00e3o. A resposta \u00e0 pergunta leva \u00e0 escolha final da ferramenta. \u00c9 utilizado um m\u00e9todo semelhante para gerar par\u00e2metros. Fonte: arXiv<\/figcaption><\/figure>\n

O ToolVerifier foi treinado com dados que consistiam numa lista de ferramentas sint\u00e9ticas, incluindo ferramentas de viagem, banc\u00e1rias e de calend\u00e1rio e as respectivas descri\u00e7\u00f5es. Foi treinado para selecionar a ferramenta adequada com base apenas no t\u00edtulo e na descri\u00e7\u00e3o.<\/p>\n

Uma vez treinado na sele\u00e7\u00e3o de ferramentas e na verifica\u00e7\u00e3o de par\u00e2metros, os investigadores testaram o ToolVerifier com 4 tarefas do benchmark ToolBench que exigiam que a Llama 2-70B interagisse com 17 ferramentas nunca antes vistas.<\/p>\n

Os resultados publicado no jornal<\/a> afirmam que a utiliza\u00e7\u00e3o do m\u00e9todo ToolVerifier resultou numa \"melhoria m\u00e9dia de 22% em rela\u00e7\u00e3o \u00e0s linhas de base de poucos disparos, mesmo em cen\u00e1rios em que as distin\u00e7\u00f5es entre as ferramentas candidatas s\u00e3o finamente matizadas\".<\/p>\n

\"\"
Percentagem (%) da taxa de sucesso das tarefas Weather, Booking, Home e Cat do benchmark Toolbench comparando modelos com e sem ToolVerifier. Fonte: arXiv<\/figcaption><\/figure>\n

Os resultados mostram que o ToolVerifier proporciona uma melhoria substancial na sele\u00e7\u00e3o de ferramentas de um LLM e na gera\u00e7\u00e3o de par\u00e2metros precisos. O m\u00e9todo s\u00f3 foi treinado e testado para interac\u00e7\u00f5es com uma \u00fanica ferramenta e n\u00e3o com v\u00e1rias ferramentas, mas \u00e9 promissor.<\/p>\n

Os LLMs com ferramentas aumentadas s\u00e3o um desenvolvimento interessante na utiliza\u00e7\u00e3o da IA como agente generalizado. Quando os LLMs aprenderem a utilizar v\u00e1rias ferramentas para atingir um objetivo, ser\u00e3o ainda mais \u00fateis do que j\u00e1 s\u00e3o.<\/p>\n

O futuro em que um assistente de IA reserva um voo, coordena uma reuni\u00e3o ou faz as compras de mercearia por si, n\u00e3o parece muito distante.<\/p>","protected":false},"excerpt":{"rendered":"

Os investigadores da Meta e da Universidade da Calif\u00f3rnia em San Diego (UCSD) desenvolveram o ToolVerifier, um m\u00e9todo que melhora a forma como os LLMs chamam e interagem com ferramentas de software. Para que os LLMs se tornem \u00fateis como assistentes ou agentes gerais, \u00e9 necess\u00e1rio ensin\u00e1-los a usar v\u00e1rias ferramentas ou APIs. O ajuste fino de uma LLM para usar uma ferramenta espec\u00edfica funciona, mas o verdadeiro desafio \u00e9 que uma LLM interaja com novas ferramentas sem a necessidade de ajuste fino ou de poucas demonstra\u00e7\u00f5es. Quando duas ferramentas s\u00e3o muito semelhantes, pode ser especialmente dif\u00edcil para o LLM escolher a ferramenta correcta para realizar o seu objetivo.<\/p>","protected":false},"author":6,"featured_media":10390,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[118,131],"class_list":["post-10380","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-llms","tag-meta"],"yoast_head":"\nMeta, UCSD introduce ToolVerifier to improve LLM tool calls | DailyAI<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dailyai.com\/pt\/2024\/02\/meta-ucsd-introduce-toolverifier-to-improve-llm-tool-calls\/\" \/>\n<meta property=\"og:locale\" content=\"pt_PT\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Meta, UCSD introduce ToolVerifier to improve LLM tool calls | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Researchers from Meta and the University of California San Diego (UCSD) developed ToolVerifier, a method that improves how LLMs call and interact with software tools. 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