Incorporating natural language rationales in the prompt and In-Context
Learning (ICL) has led to a significant improvement of Large Language Models
(LLMs) performance. However, rationales currently require human-annotation or
the use of auxiliary proxy models to target promising samples or generate
high-quality rationales. In this work, we propose Self-AMPLIFY to generate
automatically rationales from post hoc explanation methods applied to Small
Language Models (SLMs) to improve their own performance. Self-AMPLIFY is a
3-step method that targets samples, generates rationales and builds a final
prompt to leverage ICL. Self-AMPLIFY performance is evaluated on two SLMs and
two datasets requiring reasoning abilities: these experiments show that
Self-AMPLIFY achieves good results against competitors. Self-AMPLIFY is the
first method to apply post hoc explanation methods to SLM to generate
rationales to improve their own performance in a fully automated manner.

自动化方法 Self-AMPLIFY 将事后解释方法应用于小型语言模型，生成有理性解释并改善其性能。

Self-AMPLIFY: 提高小型语言模型性能的自解释方法

Self-AMPLIFY: Improving Small Language Models with Self Post Hoc  Explanations

Natural language rationales could provide intuitive, higher-level
explanations that are easily understandable by humans, complementing the more
broadly studied lower-level explanations based on gradients or attention
weights. We present the first study focused on generating natural language
rationales across several complex visual reasoning tasks: visual commonsense
reasoning, visual-textual entailment, and visual question answering. The key
challenge of accurate rationalization is comprehensive image understanding at
all levels: not just their explicit content at the pixel level, but their
contextual contents at the semantic and pragmatic levels. We present
Rationale^VT Transformer, an integrated model that learns to generate free-text
rationales by combining pretrained language models with object recognition,
grounded visual semantic frames, and visual commonsense graphs. Our experiments
show that the base pretrained language model benefits from visual adaptation
and that free-text rationalization is a promising research direction to
complement model interpretability for complex visual-textual reasoning tasks.

本文介绍了首个以生成自然语言解释为中心的研究，该研究关注复杂的视觉推理任务，包括视觉常识推理、视觉文本蕴含和视觉问答。文章提出了 Rationale^VT Transformer，该模型通过结合预训练的语言模型、对象识别、基于视觉的语义框架和视觉常识图生成自由文本解释，实现了全面的图像理解，并且实验证明，自然语言解释是用于复杂视觉 - 文本推理任务的一种具有前景的研究方向。