使用 Llama 2 與 Grammar 進行結構化資訊萃取 (Jet-setting with Llama 2 + Grammars)
Original: Jet-setting with Llama 2 + Grammars
In practical natural language processing (NLP) applications, converting unstructured text (such as emails or conversation logs) into…
Replicate 介紹了如何將 Llama 2 模型與 Grammar（語法約束）結合，用於高精度的資訊萃取任務。透過定義 GBNF 語法，開發者可以強制 LLM 輸出完全符合特定格式（如 JSON）的內容，解決傳統 LLM 輸出格式不穩定、容易幻覺的問題。本文以旅遊規劃（Jet-setting）為例，展示如何從日常對話中精準提取出發地、目的地與日期等結構化數據。
In practical natural language processing (NLP) applications, converting unstructured text (such as emails or conversation logs) into structured data (such as JSON) is a common yet highly challenging task. Although large language models (LLMs) like Llama 2 are powerful, they can still make mistakes when generating specific formats — for example, missing quotation marks, mismatched brackets, or producing unwanted explanatory text.
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