A large language model (LLM) is a neural network trained on a huge pile of text to handle natural language processing tasks, with language generation being the headline act [Source 1]. If you've used a modern chatbot, you've used an LLM under the hood [Source 1].
What it does
Give an LLM text, get text back. The common jobs:
Generating new text
Summarizing existing text
Translating between languages
Parsing text into something more structured
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An LLM is only as good as what it read. Biased or inaccurate training data makes the output less reliable [Source 1]. That's the short version of why these models confidently say wrong things: they learned from text that was wrong, skewed, or incomplete.
Fine-tuning
A base LLM is trained for general language generation. To make it useful for a specific job, you fine-tune it. Instruction tuning is the common approach, and it noticeably improves performance across tasks [Source 4].
The catch: the recipe matters. Instructions roughly split into three buckets:
NLP downstream tasks
Coding
General chat
Mixing these isn't free. Some instruction types help one application and actively hurt another, so the dataset blend is a real design decision, not an afterthought [Source 4].
LLMs as agents
Point an LLM at tools like a search engine and it can act as an agent that interacts with an environment [Source 3]. The awkward part is that LLMs are optimized for generating language, not for using tools, which limits how well they perform in agent roles [Source 3].
The usual fix is to collect interaction trajectories (the model trying things in an environment) and fine-tune on the successful ones. That throws away every failed attempt, which makes training data scarce, expensive to collect, and wastes signal that could have helped [Source 3]. Newer work argues those negative examples are worth keeping [Source 3].
Benchmarks
How do you know if one LLM is better than another? You run a language model benchmark: a standardized test for NLP tasks like understanding, generation, and reasoning, designed so different models can be compared on the same footing [Source 2].
Quick glossary
LLM: neural network trained on lots of text, used mostly for generation [Source 1].
Instruction tuning: fine-tuning on instruction-style data to improve task performance [Source 4].
Agent: an LLM that interacts with an environment through tools [Source 3].
Trajectory: a recorded sequence of an agent's interactions with its environment, used as fine-tuning data [Source 3].
Benchmark: standardized evaluation for comparing models [Source 2].
