Paraphrases represent a human's intuitive ability to understand expressions
presented in various different ways. Current paraphrase evaluations of language
models primarily use binary approaches, offering limited interpretability of
specific text changes. Atomic paraphrase types (APT) decompose paraphrases into
different linguistic changes and offer a granular view of the flexibility in
linguistic expression (e.g., a shift in syntax or vocabulary used). In this
study, we assess the human preferences towards ChatGPT in generating English
paraphrases with ten APTs and five prompting techniques. We introduce APTY
(Atomic Paraphrase TYpes), a dataset of 500 sentence-level and word-level
annotations by 15 annotators. The dataset also provides a human preference
ranking of paraphrases with different types that can be used to fine-tune
models with RLHF and DPO methods. Our results reveal that ChatGPT can generate
simple APTs, such as additions and deletions, but struggle with complex
structures (e.g., subordination changes). This study contributes to
understanding which aspects of paraphrasing language models have already
succeeded at understanding and what remains elusive. In addition, our curated
datasets can be used to develop language models with specific linguistic
capabilities.

ChatGPT 在生成英文释义时可以处理简单的文本更改，如添加和删除，但在复杂结构（例如从属关系变化）方面表现困难。本研究旨在了解释义语言模型在理解方面取得了哪些成功，并探讨了仍有待解决的问题。此外，我们的数据集可用于开发具有特定语言能力的语言模型。

ChatGPT 中关于释义类型的人类理解

Towards Human Understanding of Paraphrase Types in ChatGPT

Much of the success of modern language models depends on finding a suitable
prompt to instruct the model. Until now, it has been largely unknown how
variations in the linguistic expression of prompts affect these models. This
study systematically and empirically evaluates which linguistic features
influence models through paraphrase types, i.e., different linguistic changes
at particular positions. We measure behavioral changes for five models across
120 tasks and six families of paraphrases (i.e., morphology, syntax, lexicon,
lexico-syntax, discourse, and others). We also control for other prompt
engineering factors (e.g., prompt length, lexical diversity, and proximity to
training data). Our results show a potential for language models to improve
tasks when their prompts are adapted in specific paraphrase types (e.g., 6.7%
median gain in Mixtral 8x7B; 5.5% in LLaMA 3 8B). In particular, changes in
morphology and lexicon, i.e., the vocabulary used, showed promise in improving
prompts. These findings contribute to developing more robust language models
capable of handling variability in linguistic expression.

这项研究通过对一系列不同的语言表达的变化进行系统和实证评估，发现语言模型在适应特定的语义转换类型（如形态、词汇）的提示时具有潜力，这为开发能够处理语言表达的变异性的更强大的语言模型做出了贡献。

改述类型间引发提示工程能力

Paraphrase Types Elicit Prompt Engineering Capabilities

This paper presents VisLingInstruct, a novel approach to advancing
Multi-Modal Language Models (MMLMs) in zero-shot learning. Current MMLMs show
impressive zero-shot abilities in multi-modal tasks, but their performance
depends heavily on the quality of instructions. VisLingInstruct tackles this by
autonomously evaluating and optimizing instructional texts through In-Context
Learning, improving the synergy between visual perception and linguistic
expression in MMLMs. Alongside this instructional advancement, we have also
optimized the visual feature extraction modules in MMLMs, further augmenting
their responsiveness to textual cues. Our comprehensive experiments on MMLMs,
based on FlanT5 and Vicuna, show that VisLingInstruct significantly improves
zero-shot performance in visual multi-modal tasks. Notably, it achieves a 13.1%
and 9% increase in accuracy over the prior state-of-the-art on the TextVQA and
HatefulMemes datasets.

VisLingInstruct 通过自主评估和优化教学文本的过程，改善了多模态语言模型中视觉感知和语言表达的协同作用，这在零样本学习中显著提高了多模态任务的性能。

VisLingInstruct: 用自主指令优化提升多模式语言模型中的零样本学习

VisLingInstruct: Elevating Zero-Shot Learning in Multi-Modal Language  Models with Autonomous Instruction Optimization

Sarcasm detection is an essential task that can help identify the actual
sentiment in user-generated data, such as discussion forums or tweets. Sarcasm
is a sophisticated form of linguistic expression because its surface meaning
usually contradicts its inner, deeper meaning. Such incongruity is the
essential component of sarcasm, however, it makes sarcasm detection quite a
challenging task. In this paper, we propose a model, that incorporates
different features to capture the incongruity intrinsic to sarcasm. We use a
pre-trained transformer and CNN to capture context features, and we use
transformers pre-trained on emotions detection and sentiment analysis tasks.
Our approach outperformed previous state-of-the-art results on four datasets
from social networking platforms and online media.

本文提出一种模型，使用预训练的转换器和 CNN 捕捉上下文特征，用于捕捉蕴含在讽刺中的不一致性，并在社交网络平台和在线媒体的四个数据集上优于以前的最新成果。