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This paper introduces Parrot, a system that enhances large language models' (LLMs) multi-turn instruction following capabilities through context-aware preference optimization (CaPO) and synthetic data generation, achieving significant performance improvements with limited training data.
The article reviews Didi’s research on a Multi‑turn Response Triggering Model (MRTM) that uses self‑supervised learning and asymmetric self‑attention to decide when a customer‑service chatbot should reply, achieving higher accuracy and recall than rule‑based and supervised baselines while remaining efficient enough for production deployment.
The article outlines the design and implementation of a multi‑turn conversational chatbot, detailing how natural‑language understanding converts user utterances into structured representations, a CNN‑LSTM language model classifies topics, intents, and sentiments, and an XML‑based answer engine orchestrates tasks and services for real‑world deployment.
The article presents a technical overview of Chatopera’s multi‑turn dialogue platform, covering language model fundamentals, Chinese segmentation, word embeddings, information retrieval, and open‑source tools, while illustrating how these AI techniques enable low‑cost, scalable enterprise chatbot solutions.