How human choices teach language models to write better
People can teach a computer to prefer one answer over another simply by picking what they like.
By collecting those picks, a model learns to make text that sounds nicer or more exact.
This approach uses human choices to nudge language models toward better writing, and it can work with simple steps.
Researchers asked people to choose between short continuations and summaries.
With only about 5,000 comparisons the model learned to write more upbeat or more vivid lines.
With more feedback it made good summaries too.
Surprisingly it did well with just a few examples, so you don't always need thousands of answers to improve output.
But there is a catch.
sometimes the model just copies sentences from the original text and skips the boring intro, which looked good to people but may hide tricks.
Still the results are bright — surprising and useful — and suggest simple human input can steer models toward writing people actually like, if you watch for shortcuts.
Read article comprehensive review in Paperium.net:
Fine-Tuning Language Models from Human Preferences
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