Я уже недавно писал, что в эпоху LLM регулярки снова стали актуальным инструментом так называемого AI. Regex-in-the-loop как промежуточный вариант между "слепо доверимся черному ящику" и относительно дорогим human-in-the-loop.
ReLLM filters non-matching tokens pre-generation. For each token, ReLLM tests every possible completion against a partial regex. For the potential completions that do not match the pattern, ReLLM masks the logits so that the language model does not generate them.
У меня нет бенчмарков, потому голословно выскажу предположение, что для ряда нехитрых продакшен задач такой нехитрый костыль сильно сократит отставание опенсорсных LLM от великого и могучего OpenAI.
Я уже недавно писал, что в эпоху LLM регулярки снова стали актуальным инструментом так называемого AI. Regex-in-the-loop как промежуточный вариант между "слепо доверимся черному ящику" и относительно дорогим human-in-the-loop.
ReLLM filters non-matching tokens pre-generation. For each token, ReLLM tests every possible completion against a partial regex. For the potential completions that do not match the pattern, ReLLM masks the logits so that the language model does not generate them.
У меня нет бенчмарков, потому голословно выскажу предположение, что для ряда нехитрых продакшен задач такой нехитрый костыль сильно сократит отставание опенсорсных LLM от великого и могучего OpenAI.
BY partially unsupervised
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from jp
