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LLMs are trained; the useful question behind the provocative phrase “There Is No Such Thing as a Trained LLM” is whether their training objectives prepare them for the work people expect them to do. Training changes a model’s weights, but the objective and examples used to do that do not necessarily match a user’s real task.
What does it mean to train an LLM?
Training is a process that adjusts a model’s weights using examples and a learning objective. In broad pretraining, an LLM is exposed to large datasets to develop general language capabilities. Developers may then continue training it with more curated, instruction-focused, or domain-specific examples.
These stages are often called pretraining and fine-tuning, but they are both training: each can update the model’s weights. The labels describe common roles in a development pipeline, not fundamentally different kinds of learning. The GenLaw workshop report explains this practical distinction in its account of LLM training (GenLaw workshop report).
Are LLMs trained on the wrong tasks?
That is the core challenge raised by Vincent Granville’s November 24, 2024 Hugging Face post. Granville argues that conventional LLM training can focus on objectives that are not the same as the tasks users ask a model to perform (Vincent Granville’s post). This is a critique of objective fit, not proof that pretraining is useless or that models are literally untrained.
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A model may learn to predict likely continuations of text and still need additional shaping and evaluation to answer questions, follow instructions, or perform a specialized task reliably. The important comparison is between what the training objective rewards and what users actually need from the model.
Granville’s accessible post also makes numerical claims about a trillion-token dataset being 99% noise and humans having about 30,000 keywords. The post does not provide a study or method for those figures, so they should not be treated as verified statistics.
Is fine-tuning really training?
Yes. Fine-tuning is additional training when it updates model weights using another set of examples. It can steer a broadly pretrained model toward instruction following, a particular domain, or another intended behavior. It does not guarantee that the model will perform well on every real-world task; that depends on how well the fine-tuning examples and objective represent the task.
For a technical account of language modeling and inference, the Georgetown Law Journal article cites Speech and Language Processing by Daniel Jurafsky and James H. Martin as a reference (Georgetown Law Journal article).
Why isn’t a trained model the whole chatbot?
A deployed chatbot is more than its trained weights. At inference—the stage when a model generates a response—the system combines the model with a prompt and a sampling strategy for choosing the next token. The surrounding product may also supply system instructions, conversation history, and filters. Each element can affect the response without necessarily training the model or changing its weights.
As linguist and AI researcher Christopher Potts puts it in the Georgetown Law Journal article, “Once you choose [a prompt and a sampling strategy], you have a system.” That distinction matters: if a chatbot behaves differently after a prompt change, the change may come from the system’s context or generation settings rather than from retraining.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to judge whether an LLM was trained for your task
Rather than asking only whether a model is “trained,” ask what its development and deployment are suited to:
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- Examples: Do the training or fine-tuning examples represent the inputs, domain, and outputs expected in use?
- Weight changes: Was behavior shaped through additional training, or through inference-time context, sampling, and platform controls?
- Evaluation: Has performance been assessed on representative examples of the intended real-world task?
These questions separate capability-building from task fit. Pretraining can provide a broad foundation, fine-tuning can steer behavior, and deployment choices can shape each interaction; none alone establishes how well a model will handle a particular use.
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