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This study examines how sentence structure shapes contrast predictions in both humans and large language models (LLMs). Using Mandarin ditransitive constructions — double object (DO, “She gave the girl the candy, but not…”) vs. prepositional object (PO, “She gave the candy to the girl, but not…”) as a testbed, we employed a sentence continuation task involving three human groups (written, spoken, and prosodically normalized spoken stimuli) and three LLMs (GPT-4o, LLaMA-3, and Qwen-2.5). Two principal findings emerged: (1) Although human participants predominantly focused on the theme (e.g., “the candy”), contrast predictions were significantly modulated by sentence structure—particularly in spoken contexts, where the sentence-final element drew more attention. (2) While LLMs showed a similar reliance on structure, they displayed a larger effect size and more closely resembled human spoken data than written data, indicating a stronger emphasis on linear order in generating contrast predictions. By adopting a unified psycholinguistic paradigm, this study advances our understanding of predictive language processing for both humans and LLMs and informs research on human–model alignment in linguistic tasks.

NAACL 2025

What to Predict? Exploring How Sentence Structure Influences Contrast Predictions in Humans and Large Language Models

ditransitive constructions

contrast predictions

predictive language processing

sentence structure

large language models

psycholinguistics

workshop paper

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
**[Continue reading...](https://drive.google.com/file/d/1jX-qGhqVSZZCIrAnJaz798pflu5Irrdn/view?usp=sharing)**

*- Colin Cherry, Google, NAACL 2025 General Chair* 

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Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

This paper uses autoregressive large language models to explore at which points in a given input sentence the semantic information is decodable. Using representational similarity analysis and probing, the results show that autoregressive models are capable of extracting the semantic relation information from a dataset of noun-noun compounds. When considering the effect of processing the head and modifier nouns in context, the extracted representations show greater correlation after processing both constituent nouns in the same sentence. The linguistic properties of the head nouns may influence the ability of LLMs to extract relation information when the head and modifier words are processed separately. Probing suggests that Phi-1 and LLaMA-3.2 are exposed to relation information during training, as they are able to predict the relation vectors for compounds from separate word representations to a similar degree as using compositional compound representations. However, the difference in processing condition for GPT-2 and DeepSeek-R1 indicates that these models are actively processing the contextual semantic relation information of the compound.

Investigating noun-noun compound relation representations in autoregressive large language models

Large language models (LLMs) exhibit remarkable capabilities on not just language tasks, but also various tasks that are not linguistic in nature, such as logical reasoning and social inference. In the human brain, neuroscience has identified a core language system that selectively and causally supports language processing. We here ask whether similar specialization for language emerges in LLMs. We identify language-selective units within 18 popular LLMs, using the same localization approach that is used in neuroscience. We then establish the causal role of these units by demonstrating that ablating LLM language-selective units -- but not random units -- leads to drastic deficits in language tasks. Correspondingly, language-selective LLM units are more aligned to brain recordings from the human language system than random units. Finally, we investigate whether our localization method extends to other cognitive domains: while we find specialized networks in some LLMs for reasoning and social capabilities, there are substantial differences among models. These findings provide functional and causal evidence for specialization in large language models, and highlight parallels with the functional organization in the brain.

The LLM Language Network: A Neuroscientific Approach for Identifying Causally Task-Relevant Units

Pragmatic accommodation is a key factor in maintaining smooth real-life communication, yet it is largely overlooked in pragmatic reasoning models such as the Rational Speech Act (RSA) model. The current study explores ways of extending the basic RSA model: revising the listener’s belief of common ground, adjusting the listener’s belief of speaker rationality, and doing both simultaneously. We evaluated model predictions for utterances varying in utility levels (i.e., how useful an utterance is for state updating) and sentence polarities. We find that (i) contra prior findings, low utility does not always trigger the expected extra inferences, but high utility does, (ii) higher utility is associated with higher speaker rationality, and (iii) our combined model predicts that lower speaker rationality and lower wonkiness co-occur. Theoretical implications of these findings are discussed.

Modeling Pragmatic Accommodation: A [Rational, Irrational] Speaker in a [Wonky, Normal] World

The achievements of Large Language Models in Natural Language Processing, especially for high-resource languages, call for a better understanding of their characteristics from a cognitive perspective.
Researchers have attempted to evaluate artificial models by testing their ability to predict behavioral (e.g., eye-tracking fixations) and physiological (e.g., brain responses) variables during language processing (e.g., reading/listening). In this paper, we propose using spontaneous speech corpora to derive production variables (speech reductions, prosodic prominences) and applying them in a similar fashion. More precisely, we extract. We then test models trained with a standard procedure on different pretraining datasets (written, spoken, and mixed genres) for their ability to predict these two variables. Our results show that, after some fine-tuning, the models can predict these production variables well above baselines. We also observe that spoken genre training data provides more accurate predictions than written genres. These results contribute to the broader effort of using high-quality speech corpora as benchmarks for LLMs.

Spontaneous Speech Variables as a Test of Human Cognitive Adequacy in LLMs

Temporal connectives like before and after play an important role in sentence comprehension by linking events in the sentence to real-world event order. Previous psycholinguistic research has shown that sentences where the order of mention aligns with real-world event order (chronological order) are easier to process than those that do not (reverse order). In this study, we investigate whether BERT, a large language model (LLM), reflects this humanlike preference for chronological order when predicting temporal connectives. We conduct experiments using both ambiguous and unambiguous sentences, evaluating the model’s ability to predict the appropriate connective in sentence-initial and sentence-medial positions. Our results confirm prior findings that BERT prefers chronological order only in sentence-initial positions when sentences are ambiguous. However, we extend this research by demonstrating that BERT also shows a chronological preference in sentence-medial positions when sentences are unambiguous. Additionally, we show that the model achieves higher accuracy when the correct connective reflects chronological order, illustrating its sensitivity to event order. These findings indicate that BERT predictions on event order align with human psycholinguistic patterns, with implications for cognitive modelling and event knowledge representations in LLMs.

Event Order Recognition in BERT: Evidence from Temporal Connectives

With the increasing interest in using large language models (LLMs) for planning in natural language, understanding their behaviors becomes an important research question. This work conducts a systematic investigation of LLMs' ability to perform logical reasoning in natural language. We introduce a controlled dataset of hypothetical and disjunctive syllogisms in propositional and modal logic and use it as the testbed for understanding LLM performance. Our results lead to novel insights in predicting LLM behaviors: in addition to the probability of input, logical forms should be considered as important factors. In addition, we show similarities and discrepancies between the logical reasoning performances of humans and LLMs by collecting and comparing behavioral data from both.

Logical forms complement probability in understanding language model (and human) performance

What is a neural model with minimum architectural complexity that exhibits reasonable language learning capability? To explore such a simple but sufficient neural language model, we revisit a basic reservoir computing (RC) model, Echo State Network (ESN), a restricted class of simple Recurrent Neural Networks. Our experiments showed that ESN with a large hidden state is comparable or superior to Transformer in grammaticality judgment tasks when trained with about 100M words, suggesting that architectures as complex as that of Transformer may not always be necessary for syntactic learning.

Syntactic Learnability of Echo State Neural Language Models at Scale

Recent research has increasingly focused on the extent to which large language models (LLMs) exhibit human-like behavior. In this study, we investigate whether the mental lexicon in LLMs resembles that of humans in terms of lexical organization. Using a word association task—a direct and widely used method for probing word meaning and relationships in the human mind—we evaluated the lexical representations of GPT-4 and Llama-3.1. Our findings reveal that LLMs closely emulate human mental lexicons in capturing semantic relatedness but exhibit notable differences in other properties, such as association frequency and dominant lexical patterns (e.g., top associates). Specifically, LLM lexicons demonstrate greater clustering and reduced diversity compared to the human lexicon, with KL divergence analysis confirming significant deviations in word association patterns. Additionally, LLMs fail to capture word association responses patterns in different demographic human groups. Among the models, GPT-4 consistently exhibited a slightly higher degree of human-likeness than Llama-3.1. This study highlights both the potential and limitations of LLMs in replicating human mental lexicons, offering valuable insights for applications in natural language processing and cognitive science research involving LLMs.

Humanlikeness of LLMs in Mental Lexicon

Emotional support conversation (ESC) aims to alleviate the emotional distress of individuals through effective conversations. Although large language models (LLMs) have obtained remarkable progress on ESC, most of these studies might not define the diagram from the state model perspective, therefore providing a suboptimal solution for long-term satisfaction. To address such an issue, we leverage the Finite State Machine (FSM) on LLMs, and propose a framework called FiSMiness. Our framework allows a single LLM to bootstrap the planning during ESC, and self-reason the seeker's emotion, support strategy and the final response upon each conversational turn. Substantial experiments on ESconv suggest that FiSMiness outperforms many baselines, including direct inference, fine-tuning, self-refine, chain of thought, and external-assisted methods, even those with many more parameters.

FiSMiness: A Finite State Machine Based Paradigm for Emotional Support Conversations

There is widespread of misinformation on futuristic technology and society. To accurately detect such news, the algorithms require up-to-date knowledge. The Large Language Models excel in the NLP but cannot retrieve the ongoing events or innovations. For example, GPT and it's variants are restricted till the knowledge of 2021. We introduce a new methodology for the identification of fake news pertaining to futuristic technology and society. Leveraging the power of Google Knowledge, we enhance the capabilities of the GPT-3.5 language model, thereby elevating its performance in the detection of misinformation. The proposed framework exhibits superior efficacy compared to established baselines with the accuracy of 81.04%. Moreover, we propose a novel dataset consisting of fake news in three languages English, Telugu and Tenglish of around 21000 from various sources.

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Investigating noun-noun compound relation representations in autoregressive large language models

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Investigating noun-noun compound relation representations in autoregressive large language models

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