7/2/2023 0 Comments Lidar annotator![]() ![]() The client used the dataset for to fine-tune their LLM for product catalog inquiries and were able to achieve significant improvements in performance across stages of the shopping journey as well as alignment of the model’s responses with their brand voice.Frames folder - This folder contains subfolders, each with JPEG images per frame, and a JSON file that gives the context. RESULTS: The client noted that they were impressed with our linguists’ edits and additions to the guidelines, which resulted in high quality prompts and responses. Leveraging Appen RLHF and a diverse team of US-based AI Training Specialists, we delivered 112k product catalog prompts and responses along with requested metadata. SOLUTION: The client required a dataset of realistic questions and answers about products in their catalog, along with key metadata including product category, shopping stage, and reference URLs. Our client, a global ecommerce leader, seeks to launch an expert shopping assistant that can efficiently and continuously digest their rapidly evolving catalog, answer their customers’ questions effectively, and appropriately represent their brand. Transforming online shopping: fine-tuning LLMs for personalized product assistanceĬHALLENGE: Frequently, online shoppers stall or abandon a purchase because they cannot find a product or determine whether it will meet their needs. The client was able to use the data to develop high performing dialog summarization models, which they were able to offer to their client organizations to improve operational efficiency. ![]() RESULT: The client was impressed with the diversity and quality of the dataset.We delivered over 200 hours of audio with transcriptions and summaries and more than 6,000 naturalistic SMS conversations and summaries to the client. Accordingly, we collected spoken and chat-based conversations from an AI Training Specialist pool based in locales across the US, UK, India and the Philippines. ![]()
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