title: OPT-R: Exploring the Role of Explanations in Finetuning and Prompting
    for Reasoning Skills of Large Language Models
summary: 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.
paper_authors._id: 646c212ce96a751c5255c91d
paper_authors.name: Badr AlKhamissi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-05-23T08:11:14.560Z
paper_authors._id: 63ca94752ffd350b486354e1
paper_authors.avatarUrl: /avatars/2e41b2651d30b855f0b46cddaa02aa43.svg
paper_authors.fullname: Badr AlKhamissi
paper_authors.type: user
paper_authors.user: bkhmsi
paper_authors._id: 646c212ce96a751c5255c91e
paper_authors.name: Siddharth Verma
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-08-29T06:59:39.535Z
paper_authors._id: 64ed03c921ab42dfda186620
paper_authors.avatarUrl: /avatars/308c1c23c76a254386d163bdcade0ca2.svg
paper_authors.fullname: Siddharth Verma
paper_authors.type: user
paper_authors.user: siddharthverma
paper_authors._id: 646c212ce96a751c5255c91f
paper_authors.name: Ping Yu
paper_authors._id: 646c212ce96a751c5255c920
paper_authors.name: Zhijing Jin
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-05-23T12:22:35.214Z
paper_authors._id: 646a524d3eb2bab0419b0653
paper_authors.avatarUrl: /avatars/56c468d4083e16a0660a51965d2cd842.svg
paper_authors.fullname: Causal NLP
paper_authors.type: user
paper_authors.user: causal-nlp
paper_authors._id: 646c212ce96a751c5255c921
paper_authors.name: Asli Celikyilmaz
paper_authors._id: 646c212ce96a751c5255c922
paper_authors.name: Mona Diab
paper_title: OPT-R: Exploring the Role of Explanations in Finetuning and Prompting
    for Reasoning Skills of Large Language Models
paper_summary: 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.
paper_ai_keywords: Large Language Models (LLMs) Pretrained Transformers (OPT) finetuning reasoning corpus SPECIAL-R Discrimin OPT-RE SUPER-NATURALINSTRUCTIONS benchmarks reasoning skills prompting techniques grid of configurations classification accuracy explanations Numerical reasoning Analogical reasoning