Language model tuning in conversational artificial intelligence systems and applications
AssigneeNVIDIA Corporation
InventorsYi Dong, Xianchao Wu, Yi Fen Zenodia Charpy
Life of this patent
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Abstract
Disclosed are systems and techniques that may generate prompts for language models. The techniques include training a hierarchical virtual token generator (VTG) model to generate a large language model (LLM) input prompt, wherein training the VTG model includes training, based at least on a first dataset, a first VTG to output a first virtual token embedding, and training, based at least on a second dataset different from the first dataset, a second VTG to output a second virtual token embedding, wherein, during the training of the hierarchical VTG model, the LLM input prompt comprises the first virtual token embedding and the second virtual token embedding.
Claims20
A system comprising:
- one or more processors configured to:
- train a hierarchical virtual token generator (VTG) model to generate a large language model (LLM) input prompt, wherein to train the VTG model, the one or more processors are further configured to:
- train, based at least on a first dataset, a first VTG to output a first virtual token embedding;
- and
- train, based at least on a second dataset different from the first dataset, a second VTG to output a second virtual token embedding;
- wherein, during the training of the hierarchical VTG model, the LLM input prompt comprises the first virtual token embedding and the second virtual token embedding.
A method comprising:
- generating a large language model (LLM) input prompt using a hierarchical virtual token generator (VTG) model, wherein the hierarchical VTG model comprises:
- a first VTG to generate a first virtual token embedding, and
- a second VTG to generate a second virtual token embedding;
- and
- performing a natural language processing (NLP) task based at least on providing the LLM input prompt as input to an LLM, the LLM input prompt including the first virtual token embedding and the second virtual token embedding.
A system comprising:
- one or more processors to:
- generate a large language model (LLM) input prompt using a hierarchical virtual token generator (VTG) model, wherein the hierarchical VTG model comprises:
- a first VTG to generate a first virtual token embedding, and
- a second VTG to generate a second virtual token embedding;
- and
- perform a natural language processing (NLP) task based at least on providing the LLM input prompt as input to an LLM, the LLM input prompt including the first virtual token embedding and the second virtual token embedding.
Dependencies are inferred from the claim text.