OPT-R: Exploring the Role of Explanations in Finetuning and Prompting
  for Reasoning Skills of Large Language Models
In this paper, 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 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.
646c212ce96a751c5255c91d
Badr AlKhamissi
claimed_verified
2023-05-23T08:11:14.560Z
63ca94752ffd350b486354e1
/avatars/2e41b2651d30b855f0b46cddaa02aa43.svg
Badr AlKhamissi
user
bkhmsi
646c212ce96a751c5255c91e
Siddharth Verma
claimed_verified
2023-08-29T06:59:39.535Z
64ed03c921ab42dfda186620
/avatars/308c1c23c76a254386d163bdcade0ca2.svg
Siddharth Verma
user
siddharthverma
646c212ce96a751c5255c91f
Ping Yu
646c212ce96a751c5255c920
Zhijing Jin
claimed_verified
2023-05-23T12:22:35.214Z
646a524d3eb2bab0419b0653
/avatars/56c468d4083e16a0660a51965d2cd842.svg
Causal NLP
user
causal-nlp
646c212ce96a751c5255c921
Asli Celikyilmaz
646c212ce96a751c5255c922
Mona Diab
OPT-R: Exploring the Role of Explanations in Finetuning and Prompting
  for Reasoning Skills of Large Language Models
In this paper, 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 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.
Fine-tuned OPT models with and without explanations were evaluated on a wide range of reasoning tasks, revealing that explanations enhance performance in non-finetuned models and specific reasoning skills.
Large Language Models Open Pretrained Transformers OPT-R OPT-RE finetuning reasoning corpus SUPER-NATURALINSTRUCTIONS prompting classifications accuracy reasoning skills Numerical reasoning Analogical reasoning