We are absolutely thrilled to announce the introduction of KwaiAgents, the latest advancement in Artificial Intelligence (AI) and Natural Language Processing (NLP) research. This incredible system is a generalized information-seeking agent system based on Large Language Models (LLMs) and is developed by a team of researchers from Kuaishou Inc. and Harbin Institute of Technology.
KwaiAgents is composed of three primary parts, which are – an autonomous agent loop called KAgentSys, an open-source LLM suite called KAgentLMs, and a benchmark called KAgentBench that evaluates how well LLMs work in response to different agent-system cues. With its planning-concluding procedure, the KAgentSys integrates a hybrid search-browse toolkit to manage data from many sources efficiently.
KAgentLMs include a number of sizable language models with agent features, such as tool usage, planning, and reflection. More than 3,000 automatically graded, human-edited evaluation files created to assess Agent skills have been included in KAgentBench. Planning, using tools, reflecting, wrapping up, and profiling are all included in the evaluation dimensions.
KwaiAgents uses LLMs as its central processing unit inside this architecture. The system is capable of understanding user inquiries, following rules about behavior, referencing external documents, updating and retrieving data from internal memory, organizing and carrying out activities with the help of a time-sensitive search-browse toolset, and finally, offering thorough answers. The team has shared that the study looks into how well the system operates with LLMs that aren’t as sophisticated as GPT-4. In order to overcome this, the Meta-Agent Tuning (MAT) architecture has also been presented, which guarantees that 7B or 13B open-source models can perform well in a variety of agent systems.
The team has carefully validated these capabilities using both human assessments and benchmark evaluations. In order to assess LLM performance, about 200 factual or time-aware inquiries have been gathered and annotated by humans. The tests have shown that KwaiAgents perform better than a number of open-sourced agent systems when they follow MAT. Even smaller models, such as 7B or 13B, have demonstrated generalized agent capabilities for tasks involving the retrieval of information from many sources.
The team has summarized their primary contributions as follows: KAgentSys has been introduced, which includes a special hybrid search browse and time-aware toolset together with a planning-concluding approach. The proposed system has shown improved performance compared to current open-source agent systems. With the introduction of KAgentLMs, the possibility of obtaining generalized agent capabilities for information-seeking tasks through smaller, open-sourced LLMs has been explored. The Meta-Agent Tuning framework has been introduced to guarantee effective performance, even with less sophisticated LLMs. KAgentBench, a freely available benchmark that makes it easier for humans and computers to evaluate different agent system capabilities, has also been developed. A thorough assessment of the performance of agent systems using both automated and human-centered methods has been conducted.
We are incredibly excited to witness how this breakthrough technology will revolutionize the Artificial Intelligence space. With its potential to provide human-like qualities to robots, the team has taken a huge step forward in the pursuit of AI and NLP research. Not to mention, the team has made all the necessary tools available for everyone to use, including the open-sourced LLM suite, KAgentLMs, and the KAgentBench benchmark.
So, what are you waiting for? Check out the Paper and Github and join our 35k+ ML SubReddit, 41k+ Facebook Community, Discord Channel, LinkedIn Group, and Email Newsletter, where we share the latest AI research news, cool AI projects, and more! If you like our work, you will love our newsletter.
