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Large Language Model can reasonably understand and generate human expressionsbut may lack of thorough thinking and reasoning mechanisms. Recently there havebeen several studies which enhance the thinking ability of language models butmost of them are not data-driven or training-based. In this paper, we aremotivated by the cognitive mechanism in the natural world, and design a novelmodel architecture called TaS which allows it to first consider the thoughtsand then express the response based upon the query. We design several pipelinesto annotate or generate the thought contents from prompt-response samples, thenadd language heads in a middle layer which behaves as the thinking layer. Wetrain the language model by the thoughts-augmented data and successfully letthe thinking layer automatically generate reasonable thoughts and finallyoutput more reasonable responses. Both qualitative examples and quantitativeresults validate the effectiveness and performance of TaS. Our code isavailable at https://anonymous.4open.science/r/TadE.

MeTHanol: Modularized Thinking Language Models with Intermediate Layer Thinking, Decoding and Bootstrapping Reasoning