Large language models (LMs) are capable of generating free-text rationales to
aid question answering. However, prior work 1) suggests that useful
self-rationalization is emergent only at significant scales (e.g., 175B
parameter GPT-3); and 2) focuses largely on downstream performance, ignoring
the semantics of the rationales themselves, e.g., are they faithful, true, and
helpful for humans? In this work, we enable small-scale LMs (approx. 200x
smaller than GPT-3) to generate rationales that not only improve downstream
task performance, but are also more plausible, consistent, and diverse,
assessed both by automatic and human evaluation. Our method, MaRio
(Multi-rewArd RatIOnalization), is a multi-reward conditioned
self-rationalization algorithm that optimizes multiple distinct properties like
plausibility, diversity and consistency. Results on five difficult
question-answering datasets StrategyQA, QuaRel, OpenBookQA, NumerSense and QASC
show that not only does MaRio improve task accuracy, but it also improves the
self-rationalization quality of small LMs across the aforementioned axes better
than a supervised fine-tuning (SFT) baseline. Extensive human evaluations
confirm that MaRio rationales are preferred vs. SFT rationales, as well as
qualitative improvements in plausibility and consistency.

该论文介绍了一种名为 MaRio 的算法，该算法可以使规模较小的语言模型（约为 GPT-3 的 1/200）生成合理、多样且一致的自我理解解释，从而提高问题回答的准确性和自我理解质量，并通过人工评估验证了 MaRio 方案的可行性。

多重奖励提炼的个性化自盈利者设计

Tailoring Self-Rationalizers with Multi-Reward Distillation

Language models (LMs) that jointly generate end-task answers as well as
free-text rationales are known as self-rationalization models. Recent works
demonstrate great performance gain for self-rationalization by few-shot
prompting LMs with rationale-augmented exemplars. However, the ability to
benefit from explanations only emerges with large-scale LMs, which have poor
accessibility. In this work, we explore the less-studied setting of leveraging
explanations for small LMs to improve few-shot self-rationalization. We first
revisit the relationship between rationales and answers. Inspired by the
implicit mental process of how human beings assess explanations, we present a
novel approach, Zero-shot Augmentation of Rationale-Answer pairs (ZARA), to
automatically construct pseudo-parallel data for self-training by reducing the
problem of plausibility judgement to natural language inference. Experimental
results show ZARA achieves SOTA performance on the FEB benchmark, for both the
task accuracy and the explanation metric. In addition, we conduct human and
quantitative evaluation validating ZARA's ability to automatically identify
plausible and accurate rationale-answer pairs.

本研究探索了利用解释来改善小型语言模型的 few-shot 自我合理性。我们提出了一种新方法 Zero-shot Augmentation of Rationale-Answer pairs (ZARA)，通过将可能性判断问题转换为自然语言推理，自动构建了伪平行数据来进行自我训练。实验结果表明，ZARA 在 FEB 基准测试中实现了 SOTA 性能，包括任务准确性和解释度量。此外，我们进行了人类和定量评估，验证了 ZARA 自动识别合理和准确的理由 - 答案对的能力。

ZARA: 提升小语言模型 Few-Shot 自我合理化能力

ZARA: Improving Few-Shot Self-Rationalization for Small Language Models

Combining the visual modality with pretrained language models has been
surprisingly effective for simple descriptive tasks such as image captioning.
More general text generation however remains elusive. We take a step back and
ask: How do these models work for more complex generative tasks, i.e.
conditioning on both text and images? Are multimodal models simply visually
adapted language models, or do they combine they reason jointly over
modalities?
We investigate these questions in the context of self-rationalization
(jointly generating task labels/answers and free-text explanations) of three
tasks: (i) visual question answering in VQA-X, (ii) visual commonsense
reasoning in VCR, and (iii) visual-textual entailment in e-SNLI-VE. We show
that recent unimodal advances, CLIP image representations and scaling of
language models, do not consistently improve self-rationalization in multimodal
tasks. We find that no single model type works universally best across tasks,
datasets, and finetuning data sizes. Our findings motivate the need for novel
general backbones approach that move text generation from images and text
beyond image captioning.

探索多模态模型在联合图像和文本生成任务中的表现，并发现单一模型类型不能通用地在所有任务、数据集和微调数据大小上表现最佳，加深了对从图像和文本生成超越图像字幕的新型通用骨干方法的需求。

图像文本生成的进展 —— 以自我合理化为案例研究

On Advances in Text Generation from Images Beyond Captioning: A Case Study in Self-Rationalization

Self-rationalization models that predict task labels and generate free-text
elaborations for their predictions could enable more intuitive interaction with
NLP systems. These models are, however, currently trained with a large amount
of human-written free-text explanations for each task which hinders their
broader usage. We propose to study a more realistic setting of
self-rationalization using few training examples. We present FEB -- a
standardized collection of four existing English-language datasets and
associated metrics. We identify the right prompting approach by extensively
exploring natural language prompts on FEB. Then, by using this prompt and
scaling the model size, we demonstrate that making progress on few-shot
self-rationalization is possible. We show there is still ample room for
improvement in this task: the average plausibility of generated explanations
assessed by human annotators is at most 51% (with GPT-3), while plausibility of
human explanations is 76%. We hope that FEB and our proposed approach will spur
the community to take on the few-shot self-rationalization challenge.

本文介绍了一种适用于 NLP 系统的自我合理化模型，并通过使用 FEB 数据集探索了几种自然语言提示方法和缩放模型大小的方法，以达到在少量示例下进行自我合理化的目的，但该任务的合理性仍有改进的空间。