Structural Priming in Language Models: Insights from Lexical and Frequency-Based Factors

Concetti Chiave

Language models exhibit structural priming effects that can be explained by inverse frequency effects, such as prime surprisal and verb preference, as well as lexical dependence between prime and target.

Sintesi

The paper explores structural priming in language models, investigating which linguistic factors at the sentence and token level play an important role in influencing language model predictions. The authors make use of the structural priming paradigm, where recent exposure to a structure facilitates processing of the same structure, to examine whether and where priming effects occur in language models, and what factors predict them.
The key findings are:

Language models exhibit asymmetrical priming effects, where the strength and direction of the priming effect is often inverse to what is observed in humans. This asymmetry can be explained by inverse frequency effects, such as prime surprisal and verb preference.

Lexical overlap between prime and target, especially for verbs and function words, plays a strong role in balancing the priming effects and making them more consistent across structures.

Token-level analysis reveals that priming effects in language models are highly influenced by specific lexical items, and that they incorporate systematic properties of human production preferences learned from the training data.

Regression analysis shows that factors known to predict priming in humans, such as semantic similarity, surprisal, and verb preference, also predict priming effects in language models. This suggests that language models are able to pick up on abstract patterns influencing language predictions in humans.

Overall, the results provide insights into the mechanisms underlying structural prediction in language models and how they relate to human language processing.

Statistiche

The girl gave the ball to the boy.
The girl gave the boy the ball.
The baker gave the lady the cake.

Citazioni

"Structural priming is the phenomenon where speakers are more likely to repeat a certain structure after being recently exposed to a sentence containing a congruent structure."
"Priming effects in humans are typically stronger when there are shared words between prime and target, and when the prime is more unusual, or less frequent."
"Structural preference—which expresses within which structure a verb is most likely to occur—is another important factor when predicting priming behaviour."

Approfondimenti chiave tratti da

Do Language Models Exhibit Human-like Structural Priming Effects?

by Jaap Jumelet... alle arxiv.org 09-18-2024

https://arxiv.org/pdf/2406.04847.pdf

Domande più approfondite

How do the priming effects observed in language models compare to priming effects in other modalities, such as language comprehension?

The priming effects observed in language models (LLMs) exhibit notable similarities and differences when compared to priming effects in human language comprehension. In humans, structural priming is a well-documented phenomenon where exposure to a specific syntactic structure increases the likelihood of producing the same structure in subsequent sentences. This effect is influenced by various factors, including lexical overlap, semantic similarity, and the inverse frequency effect, where rarer structures lead to stronger priming effects.
In LLMs, similar structural priming effects have been identified, as demonstrated by studies such as Sinclair et al. (2022) and the current research. LLMs show asymmetrical priming effects, where the probability of generating a target sentence is significantly influenced by the preceding prime structure. However, a key difference lies in the nature of these effects: while humans typically exhibit positive priming for both structures (e.g., Prepositional Object (PO) and Double Object (DO) constructions), LLMs often display a skewed preference towards one structure, particularly the DO construction. This asymmetry is less pronounced in human data, where both structures tend to show positive priming effects.
Moreover, the mechanisms underlying these priming effects differ. In humans, structural priming is thought to arise from cognitive processes such as implicit learning and error-based adjustments, where speakers adapt their predictions based on recent experiences. In contrast, LLMs rely on statistical patterns learned from vast corpora of human-generated text, which may not fully capture the cognitive nuances present in human language processing. Thus, while LLMs can replicate certain aspects of structural priming, the underlying cognitive mechanisms and the resulting patterns of priming may diverge from those observed in human language comprehension.

What other linguistic factors, beyond those considered in this study, might influence structural priming in language models?

Beyond the factors considered in the study, such as lexical dependence, semantic similarity, and inverse frequency effects, several other linguistic factors could influence structural priming in language models.

Contextual Factors: The broader discourse context in which sentences are situated can significantly impact structural priming. For instance, the thematic coherence and relevance of preceding sentences may affect the likelihood of a particular structure being primed. LLMs could benefit from incorporating discourse-level features to enhance their understanding of structural preferences.

Syntactic Complexity: The complexity of the syntactic structures involved may also play a role. More complex structures might exhibit different priming effects compared to simpler ones. Investigating how varying levels of syntactic complexity influence priming could provide deeper insights into the capabilities of LLMs.

Pragmatic Factors: Pragmatic considerations, such as speaker intent and the communicative context, can influence structural choices in human language. LLMs might not fully account for these pragmatic nuances, which could lead to differences in priming behavior compared to human speakers.

Morphological Features: The morphological properties of words, such as tense, aspect, and agreement, could also impact structural priming. For example, the presence of specific morphological markers might enhance or inhibit the likelihood of certain structures being produced.

Cross-linguistic Variability: Structural priming may vary across different languages due to inherent syntactic and morphological differences. Exploring how LLMs trained on multilingual data respond to structural priming could reveal the influence of language-specific factors.

By considering these additional linguistic factors, future research could further elucidate the mechanisms of structural priming in language models and enhance their alignment with human language processing.

How do the representations and learning mechanisms in language models relate to the cognitive processes underlying structural priming in humans?

The representations and learning mechanisms in language models (LLMs) share some parallels with the cognitive processes underlying structural priming in humans, yet they also exhibit significant differences.

Statistical Learning vs. Implicit Learning: LLMs are primarily based on statistical learning, where they analyze vast amounts of text data to identify patterns and relationships between words and structures. This process is akin to implicit learning in humans, where speakers unconsciously acquire language rules and structures through exposure. However, while humans adapt their predictions based on contextual cues and recent experiences (as suggested by error-based learning theories), LLMs rely on fixed statistical patterns derived from their training data, which may not fully capture the dynamic nature of human language processing.

Representation of Syntax and Semantics: LLMs encode syntactic and semantic information in high-dimensional vector spaces, allowing them to generate contextually appropriate responses. This representation is somewhat analogous to how humans mentally represent syntactic structures and semantic meanings. However, human cognitive processes involve more complex interactions between syntax, semantics, and pragmatics, which may not be fully replicated in LLMs. For instance, humans can leverage their understanding of discourse and speaker intent to guide structural choices, while LLMs may lack this nuanced understanding.

Inverse Frequency Effects: Both LLMs and humans exhibit inverse frequency effects, where rarer structures lead to stronger priming effects. This similarity suggests that LLMs can capture some abstract patterns of human language processing. However, the mechanisms driving these effects may differ; in humans, they arise from cognitive adaptations to recent linguistic experiences, while in LLMs, they stem from the statistical properties of the training data.

Token-Level Predictions: The study highlights the importance of token-level predictions in understanding structural priming in LLMs. This focus on individual tokens aligns with human language processing, where specific words and their roles within a structure can significantly influence priming effects. However, the timing and nature of these predictions may differ, as humans often exhibit priming effects earlier in the sentence than LLMs.

In summary, while there are notable similarities between the representations and learning mechanisms in LLMs and the cognitive processes underlying structural priming in humans, the differences in adaptability, contextual understanding, and the nature of learning highlight the complexity of human language processing that LLMs may not fully replicate. Further research into these relationships could enhance our understanding of both human cognition and the development of more sophisticated language models.