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Motivated by human cognitive processes, attention mechanism within transformer architecture has been developed to assist neural networks in allocating focus to specific aspects within input data. Despite claims regarding the interpretability achieved by attention mechanisms, the extent of correlation and similarity between machine and human attention remains a subject requiring further investigation. In this paper, we conduct a quantitative analysis of human attention compared to neural attention mechanisms in the context of the anaphora resolution task. We collect an eye-tracking dataset based on the Winograd schema challenge task for the Russian language. Leveraging this dataset, we conduct an extensive analysis of the correlations between human and machine attention maps across various transformer architectures, network layers of pre-trained and fine-tuned models. Our aim is to investigate whether insights from human attention mechanisms can be used to enhance the performance of neural networks in tasks such as anaphora resolution. The results reveal distinctions in anaphora resolution processing, offering promising prospects for improving the performance of neural networks and understanding the cognitive nuances of human perception.

ACL 2024

Transformer Attention vs Human Attention in Anaphora Resolution

winograd schema challenge

anaphora

attention mechanism

eye-tracking

workshop paper

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The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

In this study, we explore the proficiency of large language models (LLMs) in understanding two key lexical aspects: duration (durative/stative) and telicity (telic/atelic). Through experiments on datasets featuring sentences, verbs, and verb positions, we prompt the LLMs to identify aspectual features of verbs in sentences. Our findings reveal that certain LLMs, particularly those closed-source ones, are able to capture information on duration and telicity, albeit with some performance variations and weaker results compared to the baseline. By employing prompts at three levels (sentence-only, sentence with verb, and sentence with verb and its position), we demonstrate that integrating verb information generally enhances performance in aspectual feature recognition, though it introduces instability. We call for future research to look deeper into methods aimed at optimizing LLMs for aspectual feature comprehension.

Evaluating Lexical Aspect with Large Language Models

Babies’ daily auditory environment plays a crucial role in language development. Most previous research estimating the quantitative and qualitative aspects of early speech inputs has predominantly focused on English- and Spanish-speaking families. In addition, validation studies for daylong recordings' analysis tools are scarce on French data sets. In this paper, we present a French corpus of daylong audio recordings longitudinally collected with the LENA (Language ENvironment Analysis) system from infants aged 3 to 24 months. We conduct a thorough exploration of this data set, which serves as a quality check for both the data and the analysis tools. We evaluate the reliability of LENA metrics by systematically comparing them with those obtained from the ChildProject set of tools and by checking the known dynamics of the metrics with age. These metrics are also used to replicate, on our data set, findings from (Warlaumont et al, 2014) about the increase of infants' speech vocalizations and temporal contingencies between infants and caregivers with age

Daily auditory environments in French-speaking infants: A longitudinal dataset

Verbs describe the dynamics of interactions between people, objects, and their environments. They play a crucial role in language formation and understanding. Nonetheless, recent vision-language models like CLIP predominantly rely on nouns and have a limited account of verbs. This limitation affects their performance in tasks requiring action recognition and scene understanding. In this work, we introduce VerbCLIP, a verb-centric vision-language model which learns meanings of verbs based on a compositional approach to statistical machine learning. Our methods significantly outperform CLIP in zero-shot performance on the VALSE, VL-Checklist, and SVO-Probes datasets, with improvements of +2.38\%, +3.14\%, and +1.47\%, without fine-tuning. Fine-tuning resulted in further improvements, with gains of +2.85\% and +9.2\% on the VALSE and VL-Checklist datasets.

VerbCLIP: Improving Verb Understanding in Vision-Language Models

Language complexity is an emerging concept critical for NLP and for quantitative and cognitive approaches to linguistics. In this work, we evaluate the behavior of a set of compression-based language complexity metrics when applied to a large set of native South American languages. Our goal is to validate the desirable properties of such metrics against a more diverse set of languages, guaranteeing the universality of the techniques developed on the basis of this type of theoretical artifact. Our analysis confirmed with statistical confidence most propositions about the metrics studied, affirming their robustness, despite showing less stability than when the same metrics were applied to Indo-European languages. We also observed that the trade-off between morphological and syntactic complexities is strongly related to language phylogeny.

Analysing and Validating Language Complexity Metrics Across South American Indigenous Languages

Psycholinguistic experiments reveal that efficiency of human language use is founded on predictions at both syntactic and lexical levels. Previous models of human prediction exploiting LLMs have used an information theoretic measure called \emph{surprisal}, with success on naturalistic text in a wide variety of languages, but under-performance on challenging text such as garden path sentences. This paper introduces a novel framework that combines the lexical predictions of an LLM with the syntactic structures provided by a dependency parser. The framework gives rise to an \emph{Incompatibility Fraction}. When tested on two garden path datasets, it correlated well with human reading times, distinguished between easy and hard garden path, and outperformed surprisal.

How can large language models become more human?

Emergent language games are experimental protocols designed to model how communication may arise among a group of agents. In this paper, we focus on how to improve performances of neural agents playing a signaling game: a sender is exposed to an image and generates a sequence of symbols that is transmitted to a receiver, which uses it to distinguish between two images, one that is semantically related to the original image, and one that is not. We consider multiple design choices, such as pretraining the visual components of the agents, introducing regularization terms, how to sample training items from the dataset, and we study how these different choices impact the behavior and performances of the agents. To that end, we introduce a number of automated metrics to measure the properties of the emergent language. We find that some implementation choices are always beneficial, and that the information that is conveyed by the agents’ messages is shaped not only by the game, but also by the overall design of the agents as well as seemingly unrelated implementation choices.

So many design choices: Improving and interpreting neural agent communication in signaling games

The symbol grounding problem---how to connect a symbolic system to the outer world---is a longstanding question in AI that has recently gained prominence with the progress made in NLP in general and surrounding large language models in particular. In this article, we study the emergence of semantic categories in the communication protocol developed by neural agents involved in a well-established type of signaling game. In its basic form, the game requires one agent to retrieve an image based on a message produced by a second agent. We first show that the agents are able to, and do, learn to communicate high-level semantic concepts rather than low-level features of the images even from very indirect training signal to that end. Second, we demonstrate that the introduction of an adversarial agent in the game fosters the emergence of semantics by producing an appropriate training signal when no other method is available.

The Emergence of High-Level Semantics in a Signaling Game

We develop a multilingual version of the Wug Test, an artificial word completion experiment that is typically used to test the morphological knowledge of children, and apply it to the GPT family of large language models (LLMs). LLMs' performance on this test was evaluated by native speakers of six different languages, who judged whether the inflected and derived forms generated by the models conform to the morphological rules of their language. Our results show that LLMs can generalize their morphological knowledge to new, unfamiliar words, but that their success in generating the "correct" generalization (as judged by native human speakers) is predicted by a language's morphological complexity (specifically, integrative complexity). We further find that the amount of training data has surprisingly little on LLMs' morphological generalization abilities within the scope of the analyzed languages. These findings highlight that ``morphology matters'', and have important implications for improving low-resource language modeling.

Morphology Matters: Probing the Cross-linguistic Morphological Generalization Abilities of Large Language Models through a Wug Test

Research in artificial intelligence has witnessed the surge of large language models (LLMs) demonstrating improved performance in various natural language processing tasks. This has sparked significant discussions about the extent to which large language models emulate human linguistic cognition and usage. This study delves into the representation of grammatical well-formedness in LLMs, which is a critical aspect of linguistic knowledge. In three preregistered experiments, we collected grammaticality judgment data for over 2400 English sentences with varying structures from ChatGPT and Vicuna, comparing them with human judgment data. The results reveal substantial alignment in the assessment of grammatical correctness between LLMs and human judgments, albeit with LLMs often showing more conservative judgments for grammatical correctness or incorrectness.

Evaluating Grammatical Well-Formedness in Large Language Models: A Comparative Study with Human Judgments

Humans have clear cross-modal preferences when matching certain novel words to visual shapes. Evidence suggests that these preferences play a prominent role in our linguistic processing, language learning, and the origins of signal-meaning mappings. With the rise of multimodal models in AI, such as vision-and-language (VLM) models, it becomes increasingly important to uncover the kinds of visio-linguistic associations these models encode and whether they align with human representations. Informed by experiments with humans, we probe and compare four VLMs for a well-known human cross-modal preference, the bouba-kiki effect. We do not find conclusive evidence for this effect but suggest that results may depend on features of the models, such as architecture design, model size, and training details. Our findings inform discussions on the origins of the bouba-kiki effect in human cognition and future developments of VLMs that align well with human cross-modal associations.

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Transformer Attention vs Human Attention in Anaphora Resolution

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