Black Big Boxes: Do Language Models Hide a Theory of Adjective Order? (2407.02136v1)

Abstract: In English and other languages, multiple adjectives in a complex noun phrase show intricate ordering patterns that have been a target of much linguistic theory. These patterns offer an opportunity to assess the ability of LMs to learn subtle rules of language involving factors that cross the traditional divisions of syntax, semantics, and pragmatics. We review existing hypotheses designed to explain Adjective Order Preferences (AOPs) in humans and develop a setup to study AOPs in LMs: we present a reusable corpus of adjective pairs and define AOP measures for LMs. With these tools, we study a series of LMs across intermediate checkpoints during training. We find that all models' predictions are much closer to human AOPs than predictions generated by factors identified in theoretical linguistics. At the same time, we demonstrate that the observed AOPs in LMs are strongly correlated with the frequency of the adjective pairs in the training data and report limited generalization to unseen combinations. This highlights the difficulty in establishing the link between LM performance and linguistic theory. We therefore conclude with a road map for future studies our results set the stage for, and a discussion of key questions about the nature of knowledge in LMs and their ability to generalize beyond the training sets.

• The paper introduces a novel dataset (CAP) and specific metrics (AOP-∆) to measure how language models capture human-like adjective order preferences.
• The study demonstrates that LMs achieve high accuracy in predicting adjective orders by leveraging both training data frequency and contextual information.
• Findings reveal that LMs primarily memorize frequency patterns, resulting in limited generalization to unseen adjective pairs.

LLMs and Adjective Order Preferences: An Analysis

The paper detailed in "Black Big Boxes: Do LLMs Hide a Theory of Adjective Order?" by Jumelet et al. presents a meticulous examination of how LMs learn and process adjective order preferences (AOPs) in complex noun phrases. This research focuses on evaluating the alignment of LMs' predictions with human AOPs and how these predictions are influenced by the models' training data and linguistic context. The authors introduce innovative methodologies and data resources to scrutinize the underlying mechanisms of adjective order in LMs, culminating in important insights about linguistic generalization and memorization.

Key Contributions and Methodologies

1. Introduction of the Corpus of Adjective Pairs (CAP):
   • The authors developed a novel dataset, CAP, which comprises double adjectives extracted from a diverse set of English sources. This dataset serves as a benchmark for evaluating AOPs in various LMs.
2. AOP Metrics for LMs:
   • The paper introduces specific metrics to quantify AOPs in LMs. These metrics include AOP-∆, which measures the difference in log probabilities for natural vs. swapped adjective orders, both in isolation and within context.
3. Experimental Analysis on LMs:
   • The researchers evaluated several pretrained LLMs from the Pythia suite, focusing on their AOP prediction capabilities. Through this, they identified distinct phases of AOP acquisition during training, highlighting that adjective order preferences are learned early and stabilized quickly.

Results and Findings

1. Comparable Performance to Human AOPs:
   • All evaluated models showed predictions closely aligned with human AOPs, surpassing traditional linguistic factors in predictive capability. For instance, the Pythia-12b model achieved an AOP prediction accuracy of up to 94.1%.
2. Impact of Training Data and Frequency:
   • The paper found a strong correlation between LMs' AOPs and the frequency of adjective pairs within the training corpus. Simple bigram statistics from the training data could independently predict naturally occurring adjective orders with an accuracy of 90.3%.
3. Role of Context:
   • Contextual information significantly improves LMs' AOP predictions. The presence of context increased AOP accuracy, suggesting that the models leverage more complex linguistic signals beyond mere co-occurrence statistics.
4. Limited Generalization:
   • While LMs exhibited some capacity to generalize AOPs to unseen adjective combinations, this generalization was relatively limited. The paper shows that LMs primarily rely on memorized frequencies rather than general abstract principles for unseen combinations.

Implications for Future Research

Practical Implications

• Improvement in NLP Applications:
  • The findings can enhance the performance of NLP applications that rely on nuanced linguistic patterns, such as machine translation and text generation, by fine-tuning LMs to better capture adjective order preferences.

Theoretical Implications

• Insights into Cognitive Linguistics:
  • The alignment between LM predictions and human AOPs, as well as the models' reliance on frequency and context, provides valuable insights into the cognitive processes involved in language learning and usage.
• Potential for Linguistic Theory Development:
  • The observed performance gaps underline the potential for advancing linguistic theories that account for the graded and context-sensitive nature of adjective order preferences.

Future Directions

• Cross-Linguistic Analysis:
  • Extending the analysis to cover multiple languages could unravel universal vs. language-specific aspects of adjective order preferences, beneficial for developing multilingual LMs.
• Corpus Interventions:
  • Implementing controlled interventions in the training data, such as filtering out specific constructions, could provide deeper insights into the abstraction level at which LMs learn linguistic rules.
• Contextual Dynamics:
  • Further exploration of how different types of context influence AOPs could refine the understanding of context dependency in LMs, offering broader implications for context-aware language modeling.

Conclusion

This paper provides a comprehensive exploration of how LLMs process and predict adjective order preferences, demonstrating significant alignment with human linguistic behavior. Through detailed experiments and innovative methodological contributions, the paper paves the way for further research into the subtle linguistic capabilities of LMs and their potential applications in both theoretical and practical realms of AI and linguistics.

The findings emphasize the complex interplay between memorization and abstraction in LLMs, suggesting that while current models are adept at leveraging frequency-based patterns, there remains a scope for improving their generalization capabilities in a human-like fashion. As the field continues to evolve, such research is crucial for advancing the understanding and capabilities of NLP systems.