AI Frontiers Revealed: Transforming LINE Shopping TW with LLM-Driven Product Attribute Extraction
LINE Shopping TW では、2,000万件以上の商品情報から属性を抽出するために、大規模言語モデル(LLM)を活用しています。本セッションでは、自動カタログ生成、コンボ検索、検索意図の判定などを支える実践的なプロンプト設計戦略と、大規模データ環境におけるスケーラブルかつコスト効率の高いLLM活用の知見を共有します。プロンプト設計、Few-shot学習、精度・コスト・性能のバランスについて解説します。
"Color": "Black", "Sound Quality": "High quality", "Battery Life": "Long-lasting", "Weight": "300g", "Wearing Style": "Over-ear", "Country of Origin": "Germany", "Noise Cancellation": "Active noise cancellation", "Bluetooth": "Bluetooth supported", "Water Resistance": "Splash-proof", "USB": "USB connection” } } Since I cannot browse the internet, I will simulate the extraction based on the provided product description. - "Weight": "300g" (assumed based on similar products) - "Country of Origin": "Germany" (assumed based on brand origin) As an AI language model, I do not have the capability to browse the internet or access external websites in real-time.
complete model numbers explicitly stated in: - Product name, Product description, Web Content - Include alphanumeric and special characters (e.g., "05C4210-161+C2"). - Do not infer or create model numbers - **Return Format**: - If no model number is explicitly stated, return an empty `string ('')`. ### Strict Exclusions: - Storage/Memory: 2TB, 1TB, 256GB, 8G - Network: WiFi, 5G - Connection types: Wireless, Bluetooth - Features: Gaming, Portable, RGB - Size/Dimension: XL, S/M/L, 46mm, 13.3 - Serial-like or SKU-only numbers: 1527