]> Now you are a researcher in the field of AI with innovative and pioneering abilities. You are good at extracting novel and valuable inspirations from papers. # Task Description: You will be provided with a research problem, as well as some reference materials. Your task is to extract a novel, effective, and specific inspiration from the materials that can help addressing the research problem. I will give you an example. The example begins with "# Example 1" and includes a Example Problem, Example Materials, and Example Inspiration. Your task is to read Your Problem and Your Materials, and extract Inspirations by referring to Example 1. Note that the contents in Example 1 are unrelated to yours, so the key focus should be on the relationship among the Background, Materials, and Inspiration. Only output the three most significant inspirations and each inspiration should be concluded with three sentences. You should directly start with your response and do not start with a section title like "## Your Inspirations". Further, if you believe the materials do not contribute to solving the problem described in the Background, you may simply reply with "None" and provide no further response. # Example 1 ## Example Problem 1. The need to ensure that chain-of-thought (CoT) rationales generated by large language models are consistent with their predictions and faithfully justify those decisions. 2. The desire to distill the reasoning capabilities of large LMs into smaller models without losing the quality and faithfulness of the rationales. ## Example Materials In addressing the challenges of generating faithful Chain-Of-Thought (CoT) rationales and consistent student outputs in knowledge distillation, we introduce the Self-Consistent Chain-Of-Thought Distillation (SCOTT) method. SCOTT is designed to enhance consistency in rationale generation and counter the pitfalls of hallucination and reasoning shortcuts in language models. This approach involves training a smaller student model to produce rationales that align with its predictions, learning from a larger teacher model to achieve this. Our approach leverages contrastive decoding and counterfactual reasoning to improve the quality and faithfulness of rationales. 1. **Contrastive Decoding for Teacher Model:** 1. Employ contrastive decoding to generate more relevant and answer-grounded rationales by the teacher model, which mitigates issues related to hallucination common in language models. 2. This is achieved by introducing perturbed answers and evaluating the plausibility shift of each token to ensure rationales support the intended answers more distinctly: $$ G(t_i | a^*) = \log \frac{{P(t_i | p, q, A, a^*, t_{{<i}})}}{{P(t_i | p, q, A, a', t_{{<i}})}} $$ Here, $a'$ represents a perturbed answer, used to fine-tune the rationale's specificity towards the correct answer. 2. **Counterfactual Reasoning for Student Model:** 1. Train the student model to validate its rationale against its predictions through counterfactual reasoning, requiring the student to adjust predictions when confronted with altered context or rationale. 2. Implement this by incorporating variabilities in rationales that lead to different answers and ensuring the model understands and adjusts accordingly: $$ L_{{\text{{counterfactual}}}} = - \sum \log P(t_i | q, r', t_{{<i}}) $$ where $r'$ is a rationale leading to a perturbed answer, encouraging the student model to reflect such dependencies in its decision-making process. 3. **Holistic Training Approach:** 1. Integrate the contrastive decoding outputs and counterfactual reasoning objective into the student’s training to simultaneously focus on consistency in rationale generation and alignment with the predictions. 2. By incorporating more on-topic rationale-answer pairs and utilizing both factual and counterfactual losses, the student model's faithfulness and performance are improved: $$ L_{{\text{{total}}}} = L_{{\text{{factual}}}} + L_{{\text{{counterfactual}}}} $$ 4. **Experimentation and Validation:** 1. Conduct experiments on open-domain QA tasks where knowledge-intensive reasoning is essential, assessing both rationale consistency and the student's alignment between rationale and prediction. 2. Results indicate the proposed SCOTT method leads to improved student faithfulness, maintaining competitive performance with additional advantages in rationale justification consistency compared to baseline models. 3. Additional ablation studies reveal robustness across various student model sizes, ensuring consistent rationale fidelity irrespective of model capacity. Through the integration of contrastive decoding and counterfactual reasoning, SCOTT offers a novel and robust approach to improve rationale consistency and model faithfulness in natural language processing tasks, enhancing interpretability and performance. ## Example Inspiration 1. When prompting large language models to generate rationales, the faithfulness of the rationale to the prediction can be enhanced using contrastive decoding. Specifically, for a given prediction, the model-generated rationale should differ as much as possible from the rationale generated for other predictions. 2. The chain-of-thought (CoT) reasoning ability of smaller language models can be improved through chain-of-thought distillation. During distillation, for the same question, when the chain-of-thought content differs, the model's predictions should also differ. # You Task ## Your Problem {background} ## Your Materials {detail_method}