We motivate and formally define a new task for fine-tuning rule-like
generalization in large language models. It is conjectured that the
shortcomings of current LLMs are due to a lack of ability to generalize. It has
been argued that, instead, humans are better at generalization because they
have a tendency at extracting rules from complex data. We try to recreate this
tendency to rule-based generalization. When exposed to tests of analytic
intelligence, for example, the visual RAVEN IQ test, human problem-solvers
identify the relevant objects in the picture and their relevant attributes and
reason based on rules applied to these objects and attributes. Based on the
induced rules, they are able to provide a solution to the test. We propose a
task that translates this IQ task into language. In this paper, we provide the
formal specification for the task and the generative process of its datasets.

本文提出了一项新任务，即通过模仿人类从复杂数据中提取规则进行类似规则推广的微调任务，以解决当前大型语言模型泛化不足的问题，并提供了相关任务的正式规范和数据生成过程。

黑雀语言矩阵 (BLM)，神经网络规则化通用性的新任务：动机和形式规格

Blackbird language matrices (BLM), a new task for rule-like  generalization in neural networks: Motivations and Formal Specifications

Transformer models can use two fundamentally different kinds of information:
information stored in weights during training, and information provided
``in-context'' at inference time. In this work, we show that transformers
exhibit different inductive biases in how they represent and generalize from
the information in these two sources. In particular, we characterize whether
they generalize via parsimonious rules (rule-based generalization) or via
direct comparison with observed examples (exemplar-based generalization). This
is of important practical consequence, as it informs whether to encode
information in weights or in context, depending on how we want models to use
that information. In transformers trained on controlled stimuli, we find that
generalization from weights is more rule-based whereas generalization from
context is largely exemplar-based. In contrast, we find that in transformers
pre-trained on natural language, in-context learning is significantly
rule-based, with larger models showing more rule-basedness. We hypothesise that
rule-based generalization from in-context information might be an emergent
consequence of large-scale training on language, which has sparse rule-like
structure. Using controlled stimuli, we verify that transformers pretrained on
data containing sparse rule-like structure exhibit more rule-based
generalization.

本文研究 Transformer 模型的归纳偏差，发现预训练模型在处理稀少类似规则的数据时更倾向于基于规则的归纳，而在无监督学习上表现出基于例子的归纳偏差。