The Mutual Reinforcement Effect (MRE) investigates the synergistic
relationship between word-level and text-level classifications in text
classification tasks. It posits that the performance of both classification
levels can be mutually enhanced. However, this mechanism has not been
adequately demonstrated or explained in prior research. To address this gap, we
employ information flow analysis to observe and substantiate the MRE theory.
Our experiments on six MRE hybrid datasets revealed the presence of MRE in the
model and its impact. Additionally, we conducted fine-tuning experiments, whose
results were consistent with those of the information flow experiments. The
convergence of findings from both experiments corroborates the existence of
MRE. Furthermore, we extended the application of MRE to prompt learning,
utilizing word-level information as a verbalizer to bolster the model's
prediction of text-level classification labels. In our final experiment, the
F1-score significantly surpassed the baseline in five out of six datasets,
further validating the notion that word-level information enhances the language
model's comprehension of the text as a whole.

互相增强效应 (Mutual Reinforcement Effect) 研究了文本分类任务中词级别和文本级别分类之间的协同关系，探讨了两个分类级别的互相促进性能的机制。使用信息流分析来观察和验证了互相增强效应理论，并将其应用于提示学习，以加强模型对文本级别分类标签的预测，实验证实了词级别信息增强整体文本理解的观点。

通过信息流展示相互增强效应

Demonstrating Mutual Reinforcement Effect through Information Flow

Information Extraction (IE) stands as a cornerstone in natural language
processing, traditionally segmented into distinct sub-tasks. The advent of
Large Language Models (LLMs) heralds a paradigm shift, suggesting the
feasibility of a singular model addressing multiple IE subtasks. In this vein,
we introduce the General Information Extraction Large Language Model (GIELLM),
which integrates text Classification, Sentiment Analysis, Named Entity
Recognition, Relation Extraction, and Event Extraction using a uniform
input-output schema. This innovation marks the first instance of a model
simultaneously handling such a diverse array of IE subtasks. Notably, the
GIELLM leverages the Mutual Reinforcement Effect (MRE), enhancing performance
in integrated tasks compared to their isolated counterparts. Our experiments
demonstrate State-of-the-Art (SOTA) results in five out of six Japanese mixed
datasets, significantly surpassing GPT-3.5-Turbo. Further, an independent
evaluation using the novel Text Classification Relation and Event
Extraction(TCREE) dataset corroborates the synergistic advantages of MRE in
text and word classification. This breakthrough paves the way for most IE
subtasks to be subsumed under a singular LLM framework. Specialized fine-tune
task-specific models are no longer needed.

信息抽取是自然语言处理中的基石，传统上被细分为不同的子任务。大型语言模型的出现预示着一种新的范式转变，即单一模型能够解决多个信息抽取子任务。本文引入通用信息抽取大型语言模型（GIELLM），它通过统一的输入输出架构整合了文本分类、情感分析、命名实体识别、关系抽取和事件抽取。这次创新标志着一个模型首次同时处理如此多样的信息抽取子任务。值得注意的是，GIELLM 利用了互相增强效应（MRE），在综合任务中提高了性能，与各自孤立的任务相比。我们的实验证明，在六个日语混合数据集中，我们在五个数据集中取得了最先进的结果，明显超过了 GPT-3.5-Turbo。此外，使用新颖的文本分类关系和事件抽取（TCREE）数据集进行的独立评估证实了 MRE 在文本和词分类中的协同优势。这一突破为大多数信息抽取子任务在一个统一的 LLM 框架下合并提供了可能，不再需要专门的微调任务特定模型。

GIELLM：利用相互增强效应的日语通用信息抽取大型语言模型

GIELLM: Japanese General Information Extraction Large Language Model  Utilizing Mutual Reinforcement Effect

Sentiment analysis is a pivotal task in the domain of natural language
processing. It encompasses both text-level sentiment polarity classification
and word-level Part of Speech(POS) sentiment polarity determination. Such
analysis challenges models to understand text holistically while also
extracting nuanced information. With the rise of Large Language Models(LLMs),
new avenues for sentiment analysis have opened. This paper proposes enhancing
performance by leveraging the Mutual Reinforcement Effect(MRE) between
individual words and the overall text. It delves into how word polarity
influences the overarching sentiment of a passage. To support our research, we
annotated four novel Sentiment Text Classification and Part of Speech(SCPOS)
datasets, building upon existing sentiment classification datasets.
Furthermore, we developed a Universal Sentiment Analysis(USA) model, with a
7-billion parameter size. Experimental results revealed that our model
surpassed the performance of gpt-3.5-turbo across all four datasets,
underscoring the significance of MRE in sentiment analysis.

利用互动增强效应以提高普通情感分析模型性能