Knowledge Tree
Pre-training
Also known as: pretraining, Pre-training methods, pre-training framework, pre-training tasks
Facts (36)
Sources
A Survey on the Theory and Mechanism of Large Language Models arxiv.org Mar 12, 2026 13 facts
claimThe training stage of an LLM pipeline consists of two processes: pre-training, which forges foundational capabilities, and fine-tuning, which adapts the model.
formulaSaunshi et al. (2020) proved that a language model achieving epsilon-optimal cross-entropy loss during pre-training enables a simple linear classifier to achieve an error rate of epsilon on downstream natural classification tasks.
perspectiveThe 'Representation Camp' perspective posits that Large Language Models (LLMs) store memories about various topics during pretraining, and in-context learning retrieves contextually relevant topics during inference based on demonstrations.
claimWei et al. (2023) observed that smaller large language models primarily rely on semantic priors from pretraining during in-context learning (ICL) and often disregard label flips in the context, whereas larger models demonstrate the capability to override these priors when faced with label flips.
referenceQian et al. (2024) found that concepts related to trustworthiness become linearly separable early in the pre-training phase of Large Language Models, as revealed by applying linear probing techniques to intermediate checkpoints.
claimZhai et al. (2025) theorize that pre-training learns the “contexture,” defined as the top-singular functions of the association between inputs and their contexts, and that a representation learning this contexture is optimal for compatible downstream tasks.
claimLarge language models do not learn new tasks during in-context learning (ICL); instead, they use demonstration information to locate tasks or topics, while the ability to perform tasks is learned during pretraining.
claimLi and Flanigan (2024) found that a model's superior performance in zero- or few-shot settings may stem from exposure to task-related samples during pre-training rather than genuine generalization.
claimWei et al. (2021) posit that pre-training enables models to capture underlying latent variable information within text data.
referenceThe paper 'Towards tracing trustworthiness dynamics: revisiting pre-training period of large language models' (arXiv:2402.19465) investigates the trustworthiness dynamics of large language models during their pre-training phase.
perspectiveThe 'Algorithmic Camp' perspective posits that Large Language Models learn to execute algorithms during pre-training and subsequently execute those algorithms for different tasks during in-context learning inference, as argued by Li et al. (2023a), Zhang et al. (2023), and Bai et al. (2023b).
referenceThe paper 'Pre-training under infinite compute' is an arXiv preprint (arXiv:2509.14786) cited in section 4.2.1 of 'A Survey on the Theory and Mechanism of Large Language Models'.
procedureThe LLM training process consists of two primary stages: (1) Pre-Training, a massive-scale, self-supervised process where the model optimizes a next-token prediction objective to acquire linguistic knowledge and reasoning abilities; and (2) Supervised Fine-Tuning (SFT), where the pre-trained model is trained on a smaller, high-quality dataset of labeled instruction-response pairs to adapt to human intent.
Unlocking the Potential of Generative AI through Neuro-Symbolic ... arxiv.org Feb 16, 2025 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 of Incorporating Psychological Theories in LLMs - arXiv arxiv.org 3 facts
claimSchulze Buschoff et al. (2023) utilize gradually expanding pre-training tasks, while Chen et al. (2024d) use contradictory historical tasks for conceptual restructuring, both applying the principle of incremental cognitive development.
claimPost-training in Large Language Models (LLMs) refines models from general proficiency to task-specific, goal-oriented behavior after the foundational knowledge is acquired during pre-training.
claimDevelopmental psychology is often referenced in the early stages of LLM development, specifically during data selection and pretraining.
Practices, opportunities and challenges in the fusion of knowledge ... frontiersin.org 3 facts
referenceKG-T5 (Moiseev et al., 2022) achieves a 3x performance gain by directly pre-training on knowledge graph triples.
referenceCoLAKE (Sun et al., 2020) uses a unified pre-training framework that jointly learns contextualized representations of language and knowledge by integrating them into a shared structure called the word-knowledge graph.
referenceThe integration of Knowledge Graphs into Large Language Models can be categorized into three types based on the effect of the enhancement: pre-training, reasoning methods (including supervised fine-tuning and alignment fine-tuning), and model interpretability.
The Synergy of Symbolic and Connectionist AI in LLM-Empowered ... arxiv.org Jul 11, 2024 2 facts
procedurePre-training involves adjusting model parameters based on the statistical properties of a large text corpus, which enables the model to understand syntax, semantics, and linguistic nuances.
procedureThe training process for Large Language Models (LLMs) generally consists of two stages: pre-training and fine-tuning.
Medical Hallucination in Foundation Models and Their Impact on ... medrxiv.org Nov 2, 2025 2 facts
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.
claimThe authors of the study hypothesize that general-purpose models outperform medical-specialized models because they possess superior abstraction capabilities developed during diverse pretraining, whereas medical-specialized models may overfit to domain-specific surface patterns without developing the flexible reasoning required for novel clinical scenarios.
A survey on augmenting knowledge graphs (KGs) with large ... link.springer.com Nov 4, 2024 2 facts
Do LLMs Build World Representations? Probing Through ... neurips.cc Dec 9, 2024 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 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).
What Really Causes Hallucinations in LLMs? - AI Exploration Journey aiexpjourney.substack.com Sep 12, 2025 1 fact
claimPre-training contributes to LLM hallucinations because the objective of density estimation forces the model to make confident guesses even when it encounters information it has not learned.
The construction and refined extraction techniques of knowledge ... nature.com Feb 10, 2026 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.
Medical Hallucination in Foundation Models and Their ... medrxiv.org Mar 3, 2025 1 fact
claimTargeted knowledge integration during pretraining can reduce blind spots in Large Language Models (LLMs), though maintaining up-to-date domain coverage remains a challenge (Feng et al., 2024).
Combining large language models with enterprise knowledge graphs frontiersin.org Aug 26, 2024 1 fact
referenceThe paper 'Deepstruct: pretraining of language models for structure prediction' by Wang et al. (2022) describes a pretraining method for language models focused on structure prediction tasks.
The Synergy of Symbolic and Connectionist AI in LLM ... arxiv.org 1 fact
claimLAAs use extensive pre-training on vast textual corpora to acquire broad knowledge and perform human reasoning tasks by generating contextually appropriate text.
Hallucination Causes: Why Language Models Fabricate Facts mbrenndoerfer.com Mar 15, 2026 1 fact
claimFinetuning large language models modifies the model's response style regarding expressed confidence, but the underlying knowledge gaps and exposure bias patterns remain encoded in the base model from pretraining.