Recent advances in the field of artificial intelligence (ai) and Natural Language Processing (NLP) have led to the introduction of Large Language Models (LLM). The increasing popularity of LLMs indicates that human talents may eventually be reflected in robots. In recent research, a team of researchers from Kuaishou Inc. and Harbin Institute of technology presented KwaiAgents, an LLM-based information search agent system.
KwaiAgents is made up of three main parts, which are: a standalone agent loop called KAgentSys, an open source LLM suite called KAgentLMs, and a benchmark called KAgentBench that evaluates how well LLMs perform in response to different signals from the agent system. . With its planning and completion procedure, KAgentSys integrates a hybrid search and navigation toolset to manage data from many sources efficiently.
KAgentLMs include a number of important language models with agent features such as tool usage, planning, and reflection. Included in KAgentBench are over 3,000 human-edited and automatically graded assessment files created to evaluate agent skills. Planning, use of tools, reflection, conclusion and profiling are included in the evaluation dimensions.
KwaiAgents uses LLM as the central processing unit within this architecture. The system is capable of understanding user queries, following behavioral rules, referencing external documents, updating and retrieving data from internal memory, organizing and performing activities with the help of a set of urgent search tools, and finally , offer exhaustive answers. .
The team has shared that the study looks at how well the system works with LLMs that are not as sophisticated as GPT-4. To overcome this, the Meta-Agent Tuning (MAT) architecture has also been introduced, which ensures that the open source 7B or 13B models can work well on a variety of agent systems.
The team has carefully validated these capabilities using human evaluations and benchmarks. To evaluate the performance of the LLM, humans have collected and annotated around 200 objective or time-based queries. Testing has shown that KwaiAgents performs better than several open source agent systems when following MAT. Even smaller models, such as 7B or 13B, have demonstrated generalized agent capabilities for tasks involving information retrieval from many systems.
The team has summarized its main contributions as follows.
- KAgentSys has been introduced which includes a special hybrid search navigation and timing toolset along with a planning and completion approach.
- The proposed system has shown improved performance compared to current open source agent systems.
- With the introduction of KAgentLM, the possibility of obtaining generalized agent capabilities for information search tasks has been explored through smaller, open source LLMs.
- The Meta-Agent Tuning framework has been introduced to ensure efficient 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 comprehensive performance evaluation of agent systems has been performed using both automated and human-centered methods.
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Tanya Malhotra is a final year student of University of Petroleum and Energy Studies, Dehradun, pursuing BTech in Computer Engineering with specialization in artificial intelligence and Machine Learning.
She is a Data Science enthusiast with good analytical and critical thinking, along with a burning interest in acquiring new skills, leading groups and managing work in an organized manner.
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