OPT - R : Exploring the Role of Explanations in Finetuning and Prompting for Reasoning Skills of Large Language Models (2023)

Abstract: We conduct a thorough investigation into the reasoning capabilities of Large Language Models (LLMs), focusing specifically on the Open Pretrained Transformers (OPT) models as a representative of such models. Our study entails finetuning three different sizes of OPT on a carefully curated reasoning corpus, resulting in two sets of finetuned models: OPT-R, finetuned without explanations, and OPT-RE, finetuned with explanations. We then evaluate all models on 57 out-of-domain tasks drawn from the Super-NaturalInstructions benchmark, covering 26 distinct reasoning skills, utilizing three prompting techniques. Through a comprehensive grid of 27 configurations and 6,156 test evaluations, we investigate the dimensions of finetuning, prompting, and scale to understand the role of explanations on different reasoning skills. Our findings reveal that having explanations in the fewshot exemplar has no significant impact on the model’s performance when the model is finetuned, while positively affecting the non-finetuned counterpart. Moreover, we observe a slight yet consistent increase in classification accuracy as we incorporate explanations during prompting and finetuning, respectively. Finally, we offer insights on which reasoning skills benefit the most from incorporating explanations during finetuning and prompting, such as Numerical (+20.4%) and Analogical (+13.9%) reasoning, as well as skills that exhibit negligible or negative effects.

## introduction recently, there has been a surge in the release of large language models (llms) by both industrial and academic institutions. these models vary from open-source releases such as opt @xcite and llama @xcite to closed-source ones like gpt-3 @xcite and palm @xcite . in addition, researchers have developed models that are finetuned on top of these foundational models to better each dimension is represented as an axis, with three levels for each of finetuning, prompting, and scale plotted on each axis. the resulting grid consists of 27 different combinations evaluated on various reasoning tasks. it should be noted that there is a hidden dimension, the scoring function, comprising four components. this results in a comprehensive total of 6,156 evaluations. recent efforts have attempted to improve the reasoning performance of llms by decomposing answers into step-by-step reasoning chains using incontext learning @xcite or during finetuning @xcite . while these approaches have shown some improvement on benchmarks such as gsm8k @xcite , it is not clear how those explanations affect finetuning, prompting, or {task definition} provide your answer followed by a brief reasoning. ## results & findings in this section, we present the results and findings of our experiments. first, we illustrate in figure 4 the outcome of our evaluation on the effectiveness of finetuned models as compared to the vanilla opt model, across three different scales when using both fewshot prompting with and without explanations. furthermore, we observe a monotonic increase in the performance of each model as we increase the scale under those two prompting condition, which indicates a positive correlation between the model's capacity and its overall performance. however, we note that this trend does not apply to the zeroshot prompting method, since we are testing out-of-distribution tasks and that the finetuned models were trained with fewshot exemplars in their context. this leads us to focus only on the fewshot prompting methods, with and without explanations, for the remaining of our evaluations. specifically, we investigate the impact of finetuning the opt models on reasoning datasets, as compared to the vanilla opt model, and explore the effect of explanations during finetuning and prompting, both in terms of the reasoning skill. ## conclusion in this study, we investigated the impact of incorporating explanations during finetuning and prompting on three different sizes of the opt model. through a systematic and comprehensive evaluation process that considered three key dimensions, we found that while explanations did provide a small improvement in performance, the effect was not significant when incorporated in the in-context demonstrations during inference for the finetuned models. additionally, our results showed that both finetuned models exhibited significant improvements in reasoning skills such as numerical, analogical and reasoning on objects. moreover, we demonstrated that skills such as physical, counting, and textual entailment benefited from incorporating explanations during the finetuning process. overall, our findings provide insights into the impact of incorporating explanations on the reasoning capabilities of llms and offer guidance on which reasoning skills would benefit most from the inclusion and exclusion of explanations during finetuning and prompting.