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[R] Small Language Models Fine-tuned to Coordinate Larger Language Models improve Complex Reasoning (alien.top)

submitted 10 months ago by Gaussian_Kernel@alien.top to c/machinelearning@academy.garden

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Paper link http://arxiv.org/abs/2310.18338

Description We introduce DaSLaM, which uses a decomposition generator to decompose complex problems into subproblems that require fewer reasoning steps. These subproblems are answered by a solver. We use a relatively small (13B parameters) LM as the decomposition generator, which we train using policy gradient optimization to interact with a solver LM (regarded as black-box) and guide it through subproblems, thereby rendering our method solver-agnostic. Evaluation on multiple different reasoning datasets reveal that with our method, a 175 billion parameter LM (text-davinci-003) can produce competitive or even better performance, compared to its orders-of-magnitude larger successor, GPT-4. Additionally, we show that DaSLaM is not limited by the solver's capabilities as a function of scale; e.g., solver LMs with diverse sizes give significant performance improvement with our solver-agnostic decomposition technique.

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[–] Single_Ring4886@alien.top 1 points 10 months ago

This is thing I was talking about some months ago after I first saw GPT4. Glad someone actually worked in this direction as ideas are somehow cheap these days :)