Pre-training ↔ fine-tuning

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The training stage of an LLM pipeline consists of two processes: pre-training, which forges foundational capabilities, and fine-tuning, which adapts the model.
Integrating symbolic knowledge into neural network loss functions reinforces the connection between neural learning and symbolic reasoning in the contexts of model distillation, fine-tuning, pre-training, and transfer learning.
Fine-tuning and advanced pre-training strengthen the tendency of large language models to maintain goal-oriented abstractions during decoding, which prioritizes task completion over the recovery of the world's state and dynamics.
Research on integrating Large Language Models with Knowledge Graphs is categorized into several distinct approaches: Pre-training, Fine-Tuning, KG-Augmented Prompting, Retrieval-Augmented Generation (RAG), Graph RAG, KG RAG, Hybrid RAG, Spatial RAG, Offline/Online KG Guidelines, Agent-based KG Guidelines, KG-Driven Filtering and Validation, Visual Question Answering (VQA), Multi-Document QA, Multi-Hop QA, Conversational QA, Temporal QA, Multilingual QA, Index-based Optimization, and Natural Language to Graph Query Language (NL2GQL).
Transfer learning, which includes pre-training, fine-tuning, and few-shot learning, allows AI models to efficiently adapt knowledge from one task to another.
Hallucination resistance in specialized medical contexts emerges from sophisticated reasoning capabilities, internal consistency mechanisms, and broad world knowledge developed during large-scale pretraining, rather than from domain-specific fine-tuning.
The training process for Large Language Models (LLMs) generally consists of two stages: pre-training and fine-tuning.
Large-scale pre-trained Large Language Models (LLMs) such as GPT-4 and LLaMA-3 utilize large-scale pretraining and task-specific fine-tuning to achieve cross-task generalization.
Approaches such as model distillation, fine-tuning, pre-training, and transfer learning align with the neuro-symbolic compiled paradigm by integrating symbolic constraints into the neural network learning process.

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Unlocking the Potential of Generative AI through Neuro-Symbolic ... arxiv.org arXiv 3 facts

claimIntegrating symbolic knowledge into neural network loss functions reinforces the connection between neural learning and symbolic reasoning in the contexts of model distillation, fine-tuning, pre-training, and transfer learning.

claimTransfer learning, which includes pre-training, fine-tuning, and few-shot learning, allows AI models to efficiently adapt knowledge from one task to another.

claimApproaches such as model distillation, fine-tuning, pre-training, and transfer learning align with the neuro-symbolic compiled paradigm by integrating symbolic constraints into the neural network learning process.

A Survey on the Theory and Mechanism of Large Language Models arxiv.org arXiv 1 fact

claimThe training stage of an LLM pipeline consists of two processes: pre-training, which forges foundational capabilities, and fine-tuning, which adapts the model.

Do LLMs Build World Representations? Probing Through ... neurips.cc NeurIPS 1 fact

claimFine-tuning and advanced pre-training strengthen the tendency of large language models to maintain goal-oriented abstractions during decoding, which prioritizes task completion over the recovery of the world's state and dynamics.

LLM-KG4QA: Large Language Models and Knowledge Graphs for ... github.com GitHub 1 fact

referenceResearch on integrating Large Language Models with Knowledge Graphs is categorized into several distinct approaches: Pre-training, Fine-Tuning, KG-Augmented Prompting, Retrieval-Augmented Generation (RAG), Graph RAG, KG RAG, Hybrid RAG, Spatial RAG, Offline/Online KG Guidelines, Agent-based KG Guidelines, KG-Driven Filtering and Validation, Visual Question Answering (VQA), Multi-Document QA, Multi-Hop QA, Conversational QA, Temporal QA, Multilingual QA, Index-based Optimization, and Natural Language to Graph Query Language (NL2GQL).

Medical Hallucination in Foundation Models and Their Impact on ... medrxiv.org medRxiv 1 fact

perspectiveHallucination resistance in specialized medical contexts emerges from sophisticated reasoning capabilities, internal consistency mechanisms, and broad world knowledge developed during large-scale pretraining, rather than from domain-specific fine-tuning.

The Synergy of Symbolic and Connectionist AI in LLM-Empowered ... arxiv.org arXiv 1 fact

procedureThe training process for Large Language Models (LLMs) generally consists of two stages: pre-training and fine-tuning.

The construction and refined extraction techniques of knowledge ... nature.com Nature 1 fact

claimLarge-scale pre-trained Large Language Models (LLMs) such as GPT-4 and LLaMA-3 utilize large-scale pretraining and task-specific fine-tuning to achieve cross-task generalization.