@inproceedings{zhao-etal-2024-probe,
    title = "Probe Then Retrieve and Reason: Distilling Probing and Reasoning Capabilities into Smaller Language Models",
    author = "Zhao, Yichun  and
      Zhou, Shuheng  and
      Zhu, Huijia",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1140",
    pages = "13026--13032",
    abstract = "Step-by-step reasoning methods, such as the Chain-of-Thought (CoT), have been demonstrated to be highly effective in harnessing the reasoning capabilities of Large Language Models (LLMs). Recent research efforts have sought to distill LLMs into Small Language Models (SLMs), with a significant focus on transferring the reasoning capabilities of LLMs to SLMs via CoT. However, the outcomes of CoT distillation are inadequate for knowledge-intensive reasoning tasks. This is because generating accurate rationales requires crucial factual knowledge, which SLMs struggle to retain due to their parameter constraints. We propose a retrieval-based CoT distillation framework, named Probe then Retrieve and Reason (PRR), which distills the question probing and reasoning capabilities from LLMs into SLMs. We train two complementary distilled SLMs, a probing model and a reasoning model, in tandem. When presented with a new question, the probing model first identifies the necessary knowledge to answer it, generating queries for retrieval. Subsequently, the reasoning model uses the retrieved knowledge to construct a step-by-step rationale for the answer. In knowledge-intensive reasoning tasks, such as StrategyQA and OpenbookQA, our distillation framework yields superior performance for SLMs compared to conventional methods, including simple CoT distillation and knowledge-augmented distillation using raw questions.",
}

Step-by-step reasoning methods, such as the Chain-of-Thought (CoT), have been demonstrated to be highly effective in harnessing the reasoning capabilities of Large Language Models (LLMs). Recent research efforts have sought to distill LLMs into Small Language Models (SLMs), with a significant focus on transferring the reasoning capabilities of LLMs to SLMs via CoT. However, the outcomes of CoT distillation are inadequate for knowledge-intensive reasoning tasks. This is because generating accurate rationales requires crucial factual knowledge, which SLMs struggle to retain due to their parameter constraints. We propose a retrieval-based CoT distillation framework, named Probe then Retrieve and Reason (PRR), which distills the question probing and reasoning capabilities from LLMs into SLMs. We train two complementary distilled SLMs, a probing model and a reasoning model, in tandem. When presented with a new question, the probing model first identifies the necessary knowledge to answer it, generating queries for retrieval. Subsequently, the reasoning model uses the retrieved knowledge to construct a step-by-step rationale for the answer. In knowledge-intensive reasoning tasks, such as StrategyQA and OpenbookQA, our distillation framework yields superior performance for SLMs compared to conventional methods, including simple CoT distillation and knowledge-augmented distillation using raw questions.
Zhao, Yichun Zhou, Shuheng Zhu, Huijia
Probe Then Retrieve and Reason: Distilling Probing and Reasoning Capabilities into Smaller Language Models