In-context learning (ICL) achieves remarkable performance in various domains
such as knowledge acquisition, commonsense reasoning, and semantic
understanding. However, its performance significantly deteriorates for emotion
detection tasks, especially fine-grained emotion recognition. The underlying
reasons for this remain unclear. In this paper, we identify the reasons behind
ICL's poor performance from the perspective of prototype theory and propose a
method to address this issue. Specifically, we conduct extensive pilot
experiments and find that ICL conforms to the prototype theory on fine-grained
emotion recognition. Based on this theory, we uncover the following
deficiencies in ICL: (1) It relies on prototypes (example-label pairs) that are
semantically similar but emotionally inaccurate to predict emotions. (2) It is
prone to interference from irrelevant categories, affecting the accuracy and
robustness of the predictions. To address these issues, we propose an Emotion
Context Learning method (E-ICL) on fine-grained emotion recognition. E-ICL
relies on more emotionally accurate prototypes to predict categories by
referring to emotionally similar examples with dynamic labels. Simultaneously,
E-ICL employs an exclusionary emotion prediction strategy to avoid interference
from irrelevant categories, thereby increasing its accuracy and robustness.
Note that the entire process is accomplished with the assistance of a
plug-and-play emotion auxiliary model, without additional training. Experiments
on the fine-grained emotion datasets EDOS, Empathetic-Dialogues,
EmpatheticIntent, and GoEmotions show that E-ICL achieves superior emotion
prediction performance. Furthermore, even when the emotion auxiliary model used
is lower than 10% of the LLMs, E-ICL can still boost the performance of LLMs by
over 4% on multiple datasets.

通过原型理论的视角，本文识别了上下文学习（ICL）在细粒度情绪识别上表现不佳的原因，并提出了一种解决此问题的方法 ——Emotion Context Learning（E-ICL）。该方法通过使用更准确的原型并参考情感相似的示例来预测情绪类别，并采用排除性情感预测策略以避免干扰，从而提高了准确性和稳健性。在多个数据集上的实验证明，E-ICL 达到了优秀的情绪预测性能，并且即使所使用的情感辅助模型低于 LLMs 的 10%，E-ICL 仍然能够提高 LLMs 的性能超过 4%。

E-ICL：运用原型理论加强细粒度情绪识别

E-ICL: Enhancing Fine-Grained Emotion Recognition through the Lens of  Prototype Theory

We introduce Natural Learning (NL), a novel algorithm that elevates the
explainability and interpretability of machine learning to an extreme level. NL
simplifies decisions into intuitive rules, like "We rejected your loan because
your income, employment status, and age collectively resemble a rejected
prototype more than an accepted prototype." When applied to real-life datasets,
NL produces impressive results. For example, in a colon cancer dataset with
1545 patients and 10935 genes, NL achieves 98.1% accuracy, comparable to DNNs
and RF, by analyzing just 3 genes of test samples against 2 discovered
prototypes. Similarly, in the UCI's WDBC dataset, NL achieves 98.3% accuracy
using only 7 features and 2 prototypes. Even on the MNIST dataset (0 vs. 1), NL
achieves 99.5% accuracy with only 3 pixels from 2 prototype images. NL is
inspired by prototype theory, an old concept in cognitive psychology suggesting
that people learn single sparse prototypes to categorize objects. Leveraging
this relaxed assumption, we redesign Support Vector Machines (SVM), replacing
its mathematical formulation with a fully nearest-neighbor-based solution, and
to address the curse of dimensionality, we utilize locality-sensitive hashing.
Following theory's generalizability principle, we propose a recursive method to
prune non-core features. As a result, NL efficiently discovers the sparsest
prototypes in O(n^2pL) with high parallelization capacity in terms of n.
Evaluation of NL with 17 benchmark datasets shows its significant
outperformance compared to decision trees and logistic regression, two methods
widely favored in healthcare for their interpretability. Moreover, NL achieves
performance comparable to finetuned black-box models such as deep neural
networks and random forests in 40% of cases, with only a 1-2% lower average
accuracy. The code is available via this http URL

自然学习（NL）算法通过解析性和可解释性的机器学习带到了极限水平。通过提取很少的特征和语义原型，NL 在多个领域表现出了令人印象深刻的结果，包括对癌症和分类数据集的高准确率和高性能与其他方法相媲美。

自然学习

Natural Learning

For two decades, the formalism of quantum mechanics has been successfully
used to describe human decision processes, situations of heuristic reasoning,
and the contextuality of concepts and their combinations. The phenomenon of
'categorical perception' has put us on track to find a possible deeper cause of
the presence of this quantum structure in human cognition. Thus, we show that
in an archetype of human perception consisting of the reconciliation of a
bottom up stimulus with a top down cognitive expectation pattern, there arises
the typical warping of categorical perception, where groups of stimuli clump
together to form quanta, which move away from each other and lead to a
discretization of a dimension. The individual concepts, which are these quanta,
can be modeled by a quantum prototype theory with the square of the absolute
value of a corresponding Schrödinger wave function as the fuzzy prototype
structure, and the superposition of two such wave functions accounts for the
interference pattern that occurs when these concepts are combined. Using a
simple quantum measurement model, we analyze this archetype of human
perception, provide an overview of the experimental evidence base for
categorical perception with the phenomenon of warping leading to quantization,
and illustrate our analyses with two examples worked out in detail.

研究表明，人类感知的量子结构的根源是对自下而上刺激与自上而下认知期望模式的调和，导致了感知的类别化扭曲现象并形成 “quanta”，可用量子原型理论进行建模，而这些 “quanta” 之间存在离散化的距离，导致它们的组合呈现出干涉图案。