Automatic Paper Analysis and Categorisation for Systematic Reviews with Combined Reasoning-Augmented SFT and DAPO RL
Abstract
Systematic reviews are a cornerstone of modern science, synthesising evidence from published research to provide the highest level of research evidence in a field.The process includes categorising studies on a number of different dimensions which is laborious and time consuming.Automatic approaches are beginning to be explored but the complexity of the task means we are currently far from a satisfactory solution.In this paper, we test different annotation scheme-agnostic methods for automatic NLP paper categorisation for systematic reviews, and test them on two tasks: (i) annotating NLP papers for categories of reported controlled-text generation methods, and (ii) annotating NLP papers for categories of reported human evaluations.We find that reasoning-enhanced finetuning combined with DAPO reinforcement learning rewarding both correctness and output format substantially improves the performance of LLMs (by up to +53.8 points), even when they have been pre-trained to perform reasoning, and cuts time required for annotation by around 80% in a human-in-the-loop setting.To extract the control type, control attributes, control implementation, and control integration from the given paper, let's follow the steps outlined in the instructions.
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Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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