Exploring Analogical Reasoning in Language Models: Evaluating Training Objectives and Comparing to Human Performance

Core Concepts

Language models can learn to solve complex analogies through targeted training objectives, approaching human-level performance on unseen analogy datasets.

Abstract

The paper investigates whether analogical reasoning can be learned by language models, focusing on more complex analogies that are closer to what is used to test human analogical reasoning, rather than the semantic/morphological analogies commonly used in NLP benchmarks.
The authors propose a novel training objective that allows language models to learn analogies by maximizing the cosine similarity between the vector differences of the word pairs in an analogy (a-b and c-d). They test this approach using a BERT-based model and compare the results to several baselines, including a non-fine-tuned BERT model and FastText.
The experiments show that the fine-tuned BERT model with the proposed training objective can learn analogical reasoning, achieving an accuracy of 0.69 on an unseen test set designed to measure human analogical reasoning, which is 0.15 below the human performance. The model performs better on "near" analogies (where the a-b and c-d pairs are semantically similar) compared to "far" analogies.
The authors also find that fine-tuning the model on the analogy task does not deteriorate its performance on external semantic similarity tasks, and in some cases, even improves it. The paper discusses the limitations of the study, such as the small dataset size, and suggests future research directions, including exploring alternative ways to represent the relations between entities in an analogy.

Stats

Entities in false analogies were observed in the pre-training data more frequently than those in true analogies.
Analogies predicted as true contained entities seen 60% more on average than those in analogies predicted as false before training.
Analogies with no out-of-vocabulary (OOV) entities were almost always predicted as true before training.

Quotes

"Language models can learn analogical reasoning, even with a small amount of data."
"After training, the model approaches human performance on an unseen test set constructed for testing human analogical reasoning."
"Fine-tuning the model on the analogy task does not deteriorate its performance on external semantic similarity tasks."

Key Insights Distilled From

Can language models learn analogical reasoning? Investigating training objectives and comparisons to human performance

by Molly R. Pet... at arxiv.org 05-06-2024

https://arxiv.org/pdf/2310.05597.pdf

Deeper Inquiries

How could the training objective be further improved to better capture the nuances of analogical reasoning?

In order to enhance the training objective to better capture the nuances of analogical reasoning, several strategies can be implemented:

Incorporating Contextual Information: Introducing contextual information into the training objective can help models understand the relationships between entities in a more nuanced way. By considering the context in which analogies occur, models can better grasp the underlying logic behind analogical reasoning.

Utilizing Multiple Measures of Similarity: Instead of relying solely on cosine similarity, incorporating multiple measures of similarity such as Euclidean distance or correlation coefficients can provide a more comprehensive understanding of relational similarity in analogies.

Accounting for Permutations: Considering all possible permutations of analogies, as opposed to a single fixed format, can help models generalize better and capture the diverse ways in which analogical reasoning can manifest.

Fine-tuning on Diverse Analogies: Training models on a wide range of analogies that vary in complexity, relation types, and semantic distance can help them learn to handle diverse analogical reasoning tasks more effectively.

Integrating Human Feedback: Incorporating human feedback during the training process can provide valuable insights into how well the model is capturing the nuances of analogical reasoning and can guide adjustments to the training objective.

What are the potential limitations of using cosine similarity as the primary measure of relational similarity in analogies?

Using cosine similarity as the primary measure of relational similarity in analogies has some limitations:

Sensitivity to Vector Length: Cosine similarity is sensitive to the length of the vectors being compared, which can lead to inaccuracies when comparing vectors of different magnitudes.

Limited Semantic Understanding: Cosine similarity measures the angle between vectors but does not capture the semantic nuances of the relationships between entities. It may overlook subtle semantic differences that are crucial in analogical reasoning.

Lack of Directionality: Cosine similarity is symmetric and does not account for the directionality of relationships in analogies. This can result in a loss of information regarding the specific nature of the relationship between entities.

Difficulty in Handling Multi-entity Relations: Cosine similarity may struggle to capture complex analogies with multiple entities and intricate relational structures, as it simplifies the comparison to a single similarity score.

Vulnerability to Noise: Cosine similarity can be sensitive to noise in the data, leading to inaccuracies in measuring relational similarity, especially in noisy or ambiguous analogies.

How might the findings of this study apply to other types of reasoning tasks beyond analogies, such as causal or relational reasoning?

The findings of this study can be extrapolated to other types of reasoning tasks beyond analogies, such as causal or relational reasoning, in the following ways:

Transferability of Training Objectives: The training objectives developed for analogical reasoning can be adapted to train models for causal or relational reasoning tasks. By modifying the training data and objectives, models can learn to identify causal relationships or relational structures effectively.

Enhanced Generalization: Models trained on analogical reasoning tasks may exhibit improved generalization capabilities, allowing them to apply learned relational patterns to new tasks like causal reasoning.

Improved Semantic Understanding: Training models on analogical reasoning can enhance their semantic understanding and ability to infer relationships between entities, which is crucial for tasks involving causal or relational reasoning.

Fine-tuning for Specific Tasks: Fine-tuning models that have learned analogical reasoning on causal or relational reasoning datasets can lead to better performance on these tasks, as the models would have developed a strong foundation in understanding complex relationships.

Integration of Contextual Information: Incorporating contextual information into the training process can help models grasp the context-dependent nature of causal and relational reasoning, enabling them to make more accurate predictions based on the given context.