# From Human Explanations to AI Reasoning: Exploring New Paths of Knowledge Distillation in Natural Language Inference

> Compare the effects of human explanations and LLM chain-of-thought on NLI tasks, and study how to distill reasoning capabilities into encoder models

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-04-25T15:02:00.000Z
- 最近活动: 2026-04-25T15:28:28.676Z
- 热度: 148.6
- 关键词: 自然语言推理, NLI, 知识蒸馏, 思维链, 可解释AI, DeBERTa, 大语言模型
- 页面链接: https://www.zingnex.cn/en/forum/thread/ai-895b64d7
- Canonical: https://www.zingnex.cn/forum/thread/ai-895b64d7
- Markdown 来源: floors_fallback

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## Introduction: Exploring New Paths of Knowledge Distillation in NLI

This article addresses the "black box" problem and shallow reasoning limitations of Natural Language Inference (NLI) models. It studies how to transfer the reasoning capabilities of human explanations and LLM chain-of-thought to efficient encoder models via knowledge distillation, compares their effects, and explores hybrid strategies, providing a new direction for interpretable NLI.

## Background: Reasoning Dilemmas of NLI Models

The NLI task requires judging the logical relationship between sentences. However, existing pre-trained models (such as BERT, DeBERTa) lack interpretability in their decisions, often relying on shallow lexical heuristics (e.g., judging entailment based on identical vocabulary), and exhibit "pseudo-understanding" in complex reasoning samples.

## Methodology: Four-Way Comparative Experiment Design

Based on the DeBERTa architecture, four training settings are designed: 1. Baseline model (using only premise-hypothesis pairs); 2. Human explanation distillation (multi-task learning to generate human explanations); 3. LLM-CoT distillation (training with chain-of-thought generated by GPT-4); 4. Hybrid distillation (combining both).

## Evidence: Key Experimental Findings

Experiments show: 1. Reasoning supervision significantly improves performance on complex samples; 2. LLM chain-of-thought outperforms human explanations in systematization and scalability; 3. Distillation strategies (e.g., multi-task learning) affect the results.

## Conclusion: Value of Reasoning Capability Distillation

Reasoning capabilities can be transferred to small models via distillation, and LLM chain-of-thought can complement human explanations, providing a feasible path for interpretable NLI in resource-constrained scenarios.

## Implications: Impact on NLI Research

1. Re-examine the relationship between human annotations and synthetic data; 2. Need stricter evaluation metrics to distinguish real reasoning from surface imitation; 3. Transfer of reasoning capabilities supports trustworthy AI.

## Future Directions: Limitations and Follow-up Exploration

Limitations: Validated only on English SNLI, and the quality of LLM chain-of-thought depends on prompts; Future: Explore advanced distillation techniques, expand to other reasoning tasks, and optimize evaluation metrics.