Скачать или смотреть WebShaper: Better Training Data for LLM Agents

In this AI Research Roundup episode, Alex discusses the paper:
'WebShaper: Agentically Data Synthesizing via Information-Seeking Formalization'
This paper introduces WebShaper, a new framework to solve the problem of poor-quality training data for Information-Seeking (IS) LLM agents. Current methods often create inconsistencies by gathering web data first and then generating questions. WebShaper flips this process by first systematically defining the task's reasoning structure using a concept called Knowledge Projections (KP). This allows an agentic system to synthesize high-quality, complex, and diverse data from the ground up. The result is better training data, leading to more capable and reliable information-seeking agents.
Paper URL: https://huggingface.co/papers/2507.15061

#AI #MachineLearning #DeepLearning #LLM #DataSynthesis #LLMAgents #InformationSeeking