This English page is a curated translation layer linked to the Korean source article. Community changes are currently made on the Korean source, where the full revision history and anonymous edit trail are preserved.
Overview
GPT stands for Generative Pre-trained Transformer and names a family developed by OpenAI, rather than one permanent product or capability set. Its architectural background is the Transformer.
Pretraining and adaptation
Early GPT work combined generative pretraining on unlabeled text with adaptation to downstream language tasks.[1] Pretraining supplies reusable language patterns; subsequent task adaptation and interaction training have different objectives. A task can also be specified through instructions without retraining.
Autoregressive generation and in-context learning
GPT models predict the next token from prior context and repeat this process to generate text. GPT-3 studied zero-shot, one-shot and few-shot tasks through textual instructions and examples without task-specific gradient updates.[2] In-context examples influence a particular request; they are not equivalent to permanently storing new knowledge in the model's weights.
Model families and products
ChatGPT is a conversational service. GPT-6 Astra is a particular model choice, while ChatGPT Work combines available models with files and tools in a work environment.[3][4] A service's connected resources and permissions cannot be inferred from the model family alone.
Evaluate a real task
This is an ORBiS Tree comparison framework, not a benchmark result. Use the same source material and questions, then record correctness, missing requirements, source verification, processing time and human review. For tool tasks include execution and retry costs. A longer, more confident first answer is not evidence of a better completed task.
Read specifications and limits
Check disclosed specifications and evaluation conditions instead of guessing parameter counts, prices, context lengths or benchmark scores from a generation number. Record availability at the time of use.[3] Model cards and safety evaluation explain how to read scope and limitations.
Next-token probability is not factual probability
Autoregressive generation selects a next token conditioned on preceding tokens and extends the context with it. In a hypothetical classification output, probabilities of 0.7 for token A and 0.3 for B do not directly mean that an external event has a 70% chance of being A. A continuation distribution and calibrated probability of a real-world answer are different quantities. These numbers are illustrative, not a GPT measurement.
Generation settings can change the wording selected from an input. Fluent explanations can still be wrong, so evaluate factual grounding independently of confidence. The GPT-3 paper ↗ describes evaluation using instructions and examples in context without task-specific weight updates.[2]
Separate tokens, input budget and output budget
Tokens do not always correspond one-to-one to characters or words. Do not assume one Korean character is one token or estimate every model's input capacity from English word counts. Check the actual tokenizer and model conditions. Instructions, conversation, retrieved material and tool results affect the context budget, together with output requirements.
When a longer source causes missed rules, investigate omission, truncation and prioritization before blaming general model ability. Read inference for input-processing and generation time, and memory for passing needed state into later requests.
Choose among prompting, retrieval and fine-tuning
- If criteria or format are unclear, first define instructions and small labeled examples.
- If changing evidence such as an internal policy is needed, examine retrieval and source updates.
- If repeated behavior should be learned from examples, examine fine-tuning objectives, datasets, evaluation and operating cost.
These methods can be combined. None automatically supplies missing provenance or evaluation. Record whether an example was added to context or model weights were actually updated.
Do not invent specifications from a name
The current model guide ↗ identifies available models and environments.[3] A name does not disclose parameter counts, actual costs or private training data. The Work guide ↗ helps separate model selection from completing a deliverable with tools.[4]
Use task-based comparisons, evaluation scope and condition-specific safety evaluation to record inputs, versions, answer criteria, completion time and retries. If sources also differ between two model trials, the model's effect cannot be isolated cleanly.
SOURCES
Sources
KNOWLEDGE LINKS
Continue from here
BACKLINKS