This article investigates a zero-shot approach to hypernymy prediction using
large language models (LLMs). The study employs a method based on text
probability calculation, applying it to various generated prompts. The
experiments demonstrate a strong correlation between the effectiveness of
language model prompts and classic patterns, indicating that preliminary prompt
selection can be carried out using smaller models before moving to larger ones.
We also explore prompts for predicting co-hyponyms and improving hypernymy
predictions by augmenting prompts with additional information through
automatically identified co-hyponyms. An iterative approach is developed for
predicting higher-level concepts, which further improves the quality on the
BLESS dataset (MAP = 0.8).

该研究通过使用大型语言模型（LLMs）对零样本超级类别预测进行研究，基于文本概率计算方法，进行多种生成提示的应用。实验证明，语言模型提示的有效性与经典模式之间存在着强关联，这表明可以在使用较大模型之前，通过较小模型进行初步提示选择。我们还通过自动识别的共同下义词，探究了预测共同下义词和提高超级类别预测的提示增强方法。针对预测更高级概念，我们开发了一个迭代式方法，这进一步提高了 BLESS 数据集的质量（MAP = 0.8）。

利用大型语言模型探索零样本上位词预测的基于提示的方法

Exploring Prompt-Based Methods for Zero-Shot Hypernym Prediction with  Large Language Models

Interpretability methods in NLP aim to provide insights into the semantics
underlying specific system architectures. Focusing on word embeddings, we
present a supervised-learning method that, for a given domain (e.g., sports,
professions), identifies a subset of model features that strongly improve
prediction of human similarity judgments. We show this method keeps only 20-40%
of the original embeddings, for 8 independent semantic domains, and that it
retains different feature sets across domains. We then present two approaches
for interpreting the semantics of the retained features. The first obtains the
scores of the domain words (co-hyponyms) on the first principal component of
the retained embeddings, and extracts terms whose co-occurrence with the
co-hyponyms tracks these scores' profile. This analysis reveals that humans
differentiate e.g. sports based on how gender-inclusive and international they
are. The second approach uses the retained sets as variables in a probing task
that predicts values along 65 semantically annotated dimensions for a dataset
of 535 words. The features retained for professions are best at predicting
cognitive, emotional and social dimensions, whereas features retained for
fruits or vegetables best predict the gustation (taste) dimension. We discuss
implications for alignment between AI systems and human knowledge.

通过一种有监督学习方法，针对特定领域（例如运动、职业），识别出一组强烈改善人类相似度判断预测的模型特征子集，并通过两种方法解释所保留特征的语义，从而揭示了人类如何根据性别包容性和国际性来区分运动类别，以及不同领域中特征对不同语义维度的预测能力。对于职业特征而言，最能预测认知、情感和社会维度，而对于水果或蔬菜特征而言，则最能预测味觉维度。讨论了对于人工智能系统与人类知识之间的对齐的意义。

利用人类相似度评判修剪词向量以提高可解释性

Enhancing Interpretability using Human Similarity Judgements to Prune  Word Embeddings

ROOT9 is a supervised system for the classification of hypernyms, co-hyponyms
and random words that is derived from the already introduced ROOT13 (Santus et
al., 2016). It relies on a Random Forest algorithm and nine unsupervised
corpus-based features. We evaluate it with a 10-fold cross validation on 9,600
pairs, equally distributed among the three classes and involving several
Parts-Of-Speech (i.e. adjectives, nouns and verbs). When all the classes are
present, ROOT9 achieves an F1 score of 90.7%, against a baseline of 57.2%
(vector cosine). When the classification is binary, ROOT9 achieves the
following results against the baseline: hypernyms-co-hyponyms 95.7% vs. 69.8%,
hypernyms-random 91.8% vs. 64.1% and co-hyponyms-random 97.8% vs. 79.4%. In
order to compare the performance with the state-of-the-art, we have also
evaluated ROOT9 in subsets of the Weeds et al. (2014) datasets, proving that it
is in fact competitive. Finally, we investigated whether the system learns the
semantic relation or it simply learns the prototypical hypernyms, as claimed by
Levy et al. (2015). The second possibility seems to be the most likely, even
though ROOT9 can be trained on negative examples (i.e., switched hypernyms) to
drastically reduce this bias.

ROOT9 是一个基于语料库的非监督特征分类系统，可对上位词、共同下位词和随机单词进行分类，分类效果较之前的方法明显提高。