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.
PLATO-2: Towards Building an Open-Domain Chatbot via Curriculum Learning
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abstract
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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2024 1verdicts
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Improving Linguistic Diversity of Large Language Models with Possibility Exploration Fine-Tuning
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.