The Rational Speech Act (RSA) model provides a flexible framework to model
pragmatic reasoning in computational terms. However, state-of-the-art RSA
models are still fairly distant from modern machine learning techniques and
present a number of limitations related to their interpretability and
scalability. Here, we introduce a new RSA framework for metaphor understanding
that addresses these limitations by providing an explicit formula - based on
the mutually shared information between the speaker and the listener - for the
estimation of the communicative goal and by learning the rationality parameter
using gradient-based methods. The model was tested against 24 metaphors, not
limited to the conventional $\textit{John-is-a-shark}$ type. Results suggest an
overall strong positive correlation between the distributions generated by the
model and the interpretations obtained from the human behavioral data, which
increased when the intended meaning capitalized on properties that were
inherent to the vehicle concept. Overall, findings suggest that metaphor
processing is well captured by a typicality-based Bayesian model, even when
more scalable and interpretable, opening up possible applications to other
pragmatic phenomena and novel uses for increasing Large Language Models
interpretability. Yet, results highlight that the more creative nuances of
metaphorical meaning, not strictly encoded in the lexical concepts, are a
challenging aspect for machines.

我们引入了一个新的基于合作信息的 RSA 框架用于隐喻理解，该框架通过提供一个明确的公式来估计交际目标，并使用基于梯度的方法学习合理性参数，从而解决了现有 RSA 模型在解释性和可扩展性方面的局限性。实验结果表明，模型生成的分布与人类行为数据的解释之间存在强正相关性，特别是当意图的含义涉及到目标概念固有属性时，相关性更加明显。研究结果表明，隐喻处理可以很好地由基于典型性的贝叶斯模型捕捉到，即使其具有更大的可扩展性和解释性，也可以应用于其他语用现象和提高大型语言模型的可解释性，然而，结果还突显出，与词汇概念中不严格编码的隐喻含义相比，更具创造性的细微差别是机器难以捕捉到的挑战性方面。

朝着一个完全可解释且更可扩展的基于 RSA 模型的隐喻理解模型

Towards a Fully Interpretable and More Scalable RSA Model for Metaphor  Understanding

Metaphors in natural language are a reflection of fundamental cognitive
processes such as analogical reasoning and categorisation, and are deeply
rooted in everyday communication. Metaphor understanding is therefore an
essential task for large language models (LLMs). We release the Metaphor
Understanding Challenge Dataset (MUNCH), designed to evaluate the metaphor
understanding capabilities of LLMs. The dataset provides over 10k paraphrases
for sentences containing metaphor use, as well as 1.5k instances containing
inapt paraphrases. The inapt paraphrases were carefully selected to serve as
control to determine whether the model indeed performs full metaphor
interpretation or rather resorts to lexical similarity. All apt and inapt
paraphrases were manually annotated. The metaphorical sentences cover natural
metaphor uses across 4 genres (academic, news, fiction, and conversation), and
they exhibit different levels of novelty. Experiments with LLaMA and GPT-3.5
demonstrate that MUNCH presents a challenging task for LLMs. The dataset is
freely accessible at
this https URL

为了评估大型语言模型（LLMs）对隐喻理解的能力，我们发布了隐喻理解挑战数据集（MUNCH），该数据集提供了超过 10k 个含隐喻用法的句子的释义和 1.5k 个含不恰当释义的实例，以确定模型是否真正进行了完整的隐喻解释或者仅依赖词汇相似性。实验表明，MUNCH 对 LLMs 来说是一个具有挑战性的任务。

LLM 大模型隐喻理解挑战数据集

Metaphor Understanding Challenge Dataset for LLMs

Probabilistic models of language understanding are interpretable and
structured, for instance models of metaphor understanding describe inference
about latent topics and features. However, these models are manually designed
for a specific task. Large language models (LLMs) can perform many tasks
through in-context learning, but they lack the clear structure of probabilistic
models. In this paper, we use chain-of-thought prompts to introduce structures
from probabilistic models into LLMs. These prompts lead the model to infer
latent variables and reason about their relationships to choose appropriate
paraphrases for metaphors. The latent variables and relationships chosen are
informed by theories of metaphor understanding from cognitive psychology. We
apply these prompts to the two largest versions of GPT-3 and show that they can
improve paraphrase selection.

本文使用思维导向提示将概率模型结构引入了大型语言模型的上下文学习中，以推断潜在变量并选择合适的隐喻释义，从而改进了模型的模仿能力。