Evaluating the Sensitivity of Language Models to Argument Roles: A Psycholinguistic Approach

Alapfogalmak

Large language models demonstrate some sensitivity to argument roles in sentence processing, but their performance differs significantly from human behavior, suggesting a reliance on lexical cues rather than a deep understanding of syntactic structure and argument role relationships.

Kivonat

Lee, E.R., Nair, S., & Feldman, N.H. (2024). A Psycholinguistic Evaluation of Language Models’ Sensitivity to Argument Roles. arXiv preprint arXiv:2410.16139v1.

This research paper investigates the ability of large language models (LLMs) to process and utilize argument role information during sentence comprehension, comparing their performance to human behavior observed in psycholinguistic studies.

Főbb Kivonatok

A Psycholinguistic Evaluation of Language Models' Sensitivity to Argument Roles

by Eun-Kyoung R... : arxiv.org 10-22-2024

https://arxiv.org/pdf/2410.16139.pdf

Mélyebb kérdések

How might the training data of LLMs be modified to improve their understanding and utilization of argument roles, moving beyond simple lexical associations?

Several strategies could be employed to modify LLM training data and enhance their grasp of argument roles beyond mere lexical associations:

Incorporating Explicit Syntactic Information:

Treebanks: Supplementing training data with parsed sentences from treebanks, which explicitly label syntactic relationships like subject, object, and indirect object, can provide LLMs with a stronger structural foundation.
Dependency Parsing Signals: Integrating dependency parsing information during training can help LLMs learn the directed relationships between words, clarifying argument roles even in complex sentences.

Enriching Semantic Representations:

Event Schemas: Training on data enriched with event schemas, which define typical participants and roles within specific events (e.g., "eating" involves an "eater" and "food"), can guide LLMs to infer roles from semantic context.
Knowledge Graphs: Leveraging knowledge graphs to provide background information about entities and their typical relationships can help LLMs disambiguate roles based on world knowledge.

Promoting Role-Sensitive Learning Objectives:

Role Prediction Tasks: Introducing auxiliary training tasks that require explicit prediction of argument roles, such as masked role prediction or role labeling, can encourage LLMs to focus on these relationships.
Adversarial Training: Employing adversarial examples, where argument roles are subtly manipulated, can force LLMs to develop more robust and less lexically-biased representations.

Leveraging Diverse Linguistic Phenomena:

Passivization: Training on data with varied syntactic constructions, such as active and passive voice, can help LLMs generalize their understanding of argument roles beyond canonical word order.
Pronoun Resolution: Incorporating tasks that require accurate pronoun resolution can encourage LLMs to track argument roles across sentences, enhancing their understanding of discourse coherence.

By combining these approaches, we can move beyond simply feeding LLMs vast amounts of text and instead provide them with richer, more structured input that facilitates a deeper understanding of argument roles.

Could the observed differences in argument role processing between LLMs and humans be attributed to the inherent limitations of current computational models, or do they point to fundamental distinctions in cognitive architecture?

The observed discrepancies in argument role processing between LLMs and humans likely stem from a combination of both inherent limitations in current computational models and potential fundamental distinctions in cognitive architecture.
Limitations of Current Models:

Statistical Learning Bias: LLMs heavily rely on statistical regularities in training data. Their difficulty with argument roles, particularly in cases like the "swap-arguments" condition, might reflect a bias towards frequent lexical co-occurrences rather than a deep understanding of syntactic structure.
Lack of World Knowledge: LLMs often struggle to integrate real-world knowledge and common sense reasoning, which humans readily employ to disambiguate argument roles. For instance, understanding that "The customer served the waitress" is implausible requires knowledge about typical restaurant scenarios.
Limited Compositionality: While LLMs exhibit some degree of compositionality, their ability to combine meanings of individual words into complex sentence-level representations remains limited. This can hinder their ability to fully grasp the nuanced interplay between verbs and their arguments.
Potential Cognitive Distinctions:

Innate Linguistic Biases: Humans possess innate linguistic biases that guide language acquisition and processing. These biases might predispose us to acquire certain syntactic structures and argument role assignments more readily than others.
Embodied Cognition: Human cognition is deeply intertwined with our physical bodies and experiences in the world. This embodiment might shape our understanding of events, actions, and the roles of participants in a way that is difficult to replicate in disembodied computational models.
Consciousness and Attention: Human consciousness and attentional mechanisms play a crucial role in language processing, allowing us to selectively focus on relevant information and suppress irrelevant details. LLMs lack these mechanisms, potentially leading to different processing strategies.
Further research is needed to disentangle the contributions of model limitations and cognitive distinctions. Investigating how these differences manifest across languages and developmental stages could provide valuable insights into the nature of both human and artificial language processing.

What are the implications of these findings for the development of more human-like artificial intelligence, particularly in tasks requiring nuanced language understanding and reasoning about events and relationships?

The findings highlight significant challenges in developing more human-like AI, particularly for tasks demanding nuanced language understanding and reasoning about events and relationships.
Here are some key implications:

Rethinking Evaluation Metrics: Current benchmarks often focus on superficial measures of language proficiency. We need more sophisticated evaluation metrics that probe an AI's ability to understand and reason about argument roles, event structures, and complex relationships within text.

Beyond Surface Form: Moving beyond simple word prediction and focusing on deeper semantic understanding is crucial. This involves equipping AI systems with the ability to extract meaning, infer intent, and reason about the implications of events and actions described in language.

Incorporating World Knowledge: Integrating common sense reasoning and real-world knowledge is essential for AI systems to interpret language in a human-like manner. This might involve leveraging knowledge graphs, reasoning engines, or simulating experiences within interactive environments.

Modeling Cognitive Processes: Drawing inspiration from human cognitive processes, such as attention, memory, and learning biases, could guide the development of more robust and flexible AI systems. This might involve exploring novel architectures or training paradigms that better reflect human cognition.

Ethical Considerations: As AI systems become more adept at understanding and generating human-like language, ethical considerations become paramount. We must ensure that these systems are developed and deployed responsibly, addressing potential biases, promoting fairness, and fostering transparency.

By addressing these implications, we can strive towards AI systems that not only excel at language-based tasks but also exhibit a deeper, more human-like understanding of the world and our place within it.