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Large Language Model (LLM)

Definition
AI-generated

A large language model is a neural network trained on vast text to predict the next token (or span), typically using a transformer architecture; at scale it can generate fluent prose, summarize documents, translate, and follow instructions, with capabilities that depend on architecture, data, and alignment training.

Topics
LLM and Agents
Synonyms
LLM
Related
AI Agent Attention Mechanism BERT (Bidirectional Encoder Representations from Transformers)

Why it matters in GWAS

LLMs are increasingly used to draft methods text, parse nomenclature, suggest code, and triage literature—but outputs must be verified against primary data, statistics, and curated databases because models can be outdated or confidently wrong.

Example usage

"We used an LLM to draft a first-pass plain-language summary of the GWAS loci, then edited against the supplementary tables."

AI Agent Attention Mechanism BERT (Bidirectional Encoder Representations from Transformers) Chain-of-Thought (CoT) Context Window Embedding Fine-Tuning Foundation Model Machine Learning Memory Neural Network Transformer Genome-Wide Association Study (GWAS) In-Context Learning LLM Hallucination Prompt Pretraining Prompt Engineering Reinforcement Learning from Human Feedback (RLHF) Retrieval-Augmented Generation (RAG) Temperature Token Tokenization Tool Calling
Related-term network

Click any node to open that term page.

Node order. The current term is first. Next come every page linked from Related terms, in the same top-to-bottom order as that list. Then new terms appear in two-hop layers: for each of those direct neighbors (in that same order), their related terms are appended if not already listed. A third pass does the same for two-hop nodes. The walk stops when a fixed maximum number of nodes is reached.

References

  • Brown T, et al. (2020). Language models are few-shot learners. NeurIPS.
  • Bommasani R, et al. (2021). On the opportunities and risks of foundation models. arXiv:2108.07258.
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