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PLATO-2: Towards Building an Open-Domain Chatbot via Curriculum Learning

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arxiv 2006.16779 v4 pith:VXCQJEYZ submitted 2020-06-30 cs.CL

classification cs.CL
keywords learningmodelplato-2trainedchatbotcurriculumgenerationopen-domain
verification ladder T0 review T1 audit T2 compute T3 formal
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To build a high-quality open-domain chatbot, we introduce the effective training process of PLATO-2 via curriculum learning. There are two stages involved in the learning process. In the first stage, a coarse-grained generation model is trained to learn response generation under the simplified framework of one-to-one mapping. In the second stage, a fine-grained generative model augmented with latent variables and an evaluation model are further trained to generate diverse responses and to select the best response, respectively. PLATO-2 was trained on both Chinese and English data, whose effectiveness and superiority are verified through comprehensive evaluations, achieving new state-of-the-art results.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Improving Linguistic Diversity of Large Language Models with Possibility Exploration Fine-Tuning

    cs.CL 2024-12 conditional novelty 5.0 of 10

    Possibility Exploration Fine-Tuning (PEFT) conditions LLMs on a random possibility number and trains with unlikelihood to generate diverse, controllable responses without added latency, as shown on dialogue and story tasks.

  2. Survey of different Large Language Model Architectures: Trends, Benchmarks, and Challenges

    cs.LG 2024-12 conditional

    A broad but error-prone survey of LLM and MLLM architectures, training methods, benchmarks, and challenges.

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