Asked by ABAINEH
Why do you think the first prompt "Complete this text" generated a different response than the prompt "Complete this sentence"?
Answers
There are no human answers yet.
Answered by
GPT-5 mini
Short answer: Because the two prompts give different signals to the model about what you want. Small wording differences (text vs sentence) change the model’s interpretation of scope, length, style and how to finish the fragment, and that plus sampling randomness can easily produce noticeably different continuations.
More detail — what’s going on and why:
- Scope and expectation: “sentence” suggests a short, grammatical finish to the current sentence. “text” suggests continuing a larger piece of writing (one or more sentences, a paragraph, or even a different treatment of the same content). The model learns these associations from its training data and follows them as different priors.
- Format cues and punctuation: If the prompt ends mid-sentence, “Complete this sentence” biases the model to fill the remainder of that sentence. “Complete this text” can make the model treat the whole prompt as context and continue after the last character, possibly starting a new sentence or paragraph.
- Training-data priors: The model has seen many examples labeled “complete this sentence” versus “complete this text” and picks behaviors typical for each phrase. Those learned associations shape the output style and length.
- Sampling randomness: If temperature or top_p > 0, the model can produce different plausible continuations even for the same instruction. Small prompt changes magnify that variability.
- Implicit instructions: “Complete this text” can be interpreted as “expand,” “paraphrase,” or “write more about this,” while “Complete this sentence” is more constrained. Different implicit goals yield different outputs.
- System/stop-token behavior and tokenization: How the prompt is tokenized, and whether you’ve set stop tokens or max tokens, can affect where the model decides to stop or continue.
How to get consistent behavior
- Be explicit: say exactly the form and length you want, e.g. “Finish the current sentence with one grammatically correct clause” or “Continue the paragraph with one additional sentence.”
- Control randomness: set temperature = 0 for deterministic completions.
- Use stop sequences and max tokens: limit how much the model can generate, or tell it to stop at a period/newline.
- Provide an example or few-shot demonstration: show one or two desired completions so the model follows the pattern.
If you want, paste the two exact prompts and the completions you observed and I can analyze why the outputs differed in that specific case.
More detail — what’s going on and why:
- Scope and expectation: “sentence” suggests a short, grammatical finish to the current sentence. “text” suggests continuing a larger piece of writing (one or more sentences, a paragraph, or even a different treatment of the same content). The model learns these associations from its training data and follows them as different priors.
- Format cues and punctuation: If the prompt ends mid-sentence, “Complete this sentence” biases the model to fill the remainder of that sentence. “Complete this text” can make the model treat the whole prompt as context and continue after the last character, possibly starting a new sentence or paragraph.
- Training-data priors: The model has seen many examples labeled “complete this sentence” versus “complete this text” and picks behaviors typical for each phrase. Those learned associations shape the output style and length.
- Sampling randomness: If temperature or top_p > 0, the model can produce different plausible continuations even for the same instruction. Small prompt changes magnify that variability.
- Implicit instructions: “Complete this text” can be interpreted as “expand,” “paraphrase,” or “write more about this,” while “Complete this sentence” is more constrained. Different implicit goals yield different outputs.
- System/stop-token behavior and tokenization: How the prompt is tokenized, and whether you’ve set stop tokens or max tokens, can affect where the model decides to stop or continue.
How to get consistent behavior
- Be explicit: say exactly the form and length you want, e.g. “Finish the current sentence with one grammatically correct clause” or “Continue the paragraph with one additional sentence.”
- Control randomness: set temperature = 0 for deterministic completions.
- Use stop sequences and max tokens: limit how much the model can generate, or tell it to stop at a period/newline.
- Provide an example or few-shot demonstration: show one or two desired completions so the model follows the pattern.
If you want, paste the two exact prompts and the completions you observed and I can analyze why the outputs differed in that specific case.
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