United States

A question for computational linguistics has been to what degree do language models encode case information. However, the majority of the work has focused on structural cases (cases which change when the syntactic configuration changes). On the other hand, inherent cases (which are assigned by specific lexical items and do not change if the syntactic configuration changes) have not been investigated. This paper sets out to investigate if German language models distinctly encode inherent dative from structural accusative and nominative. We conducted a linguistic probing investigation where probes are trained on contextual word embeddings of active nominative, accusative, and dative arguments to predict if passivised datives are analysed as a structural nominative. We provide a cased and caseless version of the experiment. Our results suggest that when case information is removed language models can distinguish between inherent dative and structural accusative, regardless of argument position, due to verb information. However, language models cannot distinguish between structural nominative and inherent dative when the dative appears in a position where there is an expected nominative, due to over-relying on surface patterns.

ACL 2026

An LLM Investigation into Inherent and Structural Case Representation: a German Case Study

structural case

inherent case

case

linguistic probing

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Context-dependent morphophonological rules -- consonant gradation, vowel harmony, possessive suffix assimilation -- lack a compositional formalization: cascades of finite-state transducers (FSTs) require multiplicative state explosion to encode rule interactions, and neural models provide no formal account of the underlying processes. We formalize these rules as coKleisli arrows of the Writer comonad (DeletionSet x Zipper), where extend lifts a local rule to a global transformation and coKleisli composition chains rules with strict associativity. This resolves length-changing operations -- notably consonant deletion -- algebraically, restoring compositionality that FST cascades sacrifice. Thirteen coKleisli arrows replace 874 FST continuation classes at the morphophonological rule level (67:1 reduction via orthogonal composition), and the same abstraction unifies character-level, morpheme-level, and sentence-level processing. Evaluated on Universal Dependencies (UD) Finnish-TDT, the system achieves 83.92% universal part-of-speech (UPOS) accuracy with rule-only disambiguation (94.66% with a lightweight suffix tagger), and the coKleisli pipeline enables bidirectional morphology -- a MorphGenerator reuses the same arrows for generation.

Comonadic Morphophonology: A Compositional Framework for Context-Dependent Morphological Rules in Finnish

Models of morphophonological learning have focused primarily on concatenative processes, leaving the challenges of non-concatenative morphology largely unaddressed. Reduplication, the systematic copying operation (e.g., Ilokano pluralization [kal-kald\'IN}] `goats'), is particularly revealing because successful learning requires the joint inference of prosodic templates that govern copying, underlying representations (URs) of stems and other affixes, and the phonological grammar. In this paper, we present a learner that tackles this challenge by allowing reduplication to be learned alongside general morphophonemic alternations, a combination that, to our knowledge, has not been directly modeled in prior computational work. We show that the learner successfully captures the attested typology of reduplication–phonology interaction.

Learning reduplicative templates as hidden structures: the case of reduplication-phonology interactions

This paper investigates the learnability of interacting phonological processes by restricting the hypothesis space to a subregular class of functions. Interacting processes can be modeled as function composition, where the output of one function serves as the input to another. We focus specifically on interactions between two simplex Input Strictly Local (ISL$_2$) functions, a proper subclass of the ISL function class. We propose a decomposition algorithm that reconstructs both the individual component processes and their relative ordering by exploiting structural properties of simplex ISL$_2$ transducers and their compositions. This work provides an initial step toward understanding how learners can infer not only single phonological processes, but structured interactions between processes.

Learning Process Interaction Through Simplex ISL Transducers

Few studies have examined how large language models (LLMs) handle negative polarity items (NPIs) in languages other than English. This study investigates NPI licensing in Korean, an under-explored language that is subject to a well established syntactic constraint. Compared to human judgments, Korean-trained models exhibit clear difficulties in detecting this requirement, underscoring a persistent gap between human and model performance on syntax-sensitive phenomena. The consistently poor performance of all three GPT language models examined suggests that the syntactic properties of NPI licensing are not adequately captured. This study thus contributes to the growing literature on linguistic dependencies in language models, while extending evaluation to underrepresented languages. (We will be able to present our work in person.)

Beyond linearity and beyond English: Language model performance on Korean NPI licensing

Zipf's law of abbreviation, a negative correlation between word length and frequency, has been shown to apply in many languages, and is widely regarded as a linguistic universal. However, it has also been shown that word length correlates strongly with surprisal, or average information content. The relative strengths of these correlations have been used as an argument that lexicons are somehow optimized for efficient communication: Words are short because they are frequent or highly informative, implying a causal relationship between word length and frequency or average surprisal. While previous work implies the existence of these causal relationships, they have not explicitly been tested for. In this paper, we apply causal discovery algorithms to lexical data from 12 languages, and find that there is no universal causal relationship between word length, frequency, and average surprisal. Instead, languages vary in the causal structure of their lexicons, suggesting that while Zipf's law describes a universal correlation across languages, that correlation is not the result of a universal causal process.

Word lengths are not optimized for efficient communication

Autoregressive language models process text token-by-token, yet the human language system operates over structured multi-word units. We propose that phrase-level representations provide a key granularity at which LLM internal states align with human language processing, and that phrase-segmentation fine-tuning can amplify this alignment. Using Meta-Llama-3.1-8B (base and fine-tuned) and the ReadBrain naturalistic reading dataset (eye tracking and fMRI), we provide three converging lines of evidence. First, phrase-level attention features predict eye movements significantly better than word-level features---a phrase superiority effect confirmed by partial correlation analysis to reflect genuine linguistic boundaries rather than aggregation artifacts. Second, fMRI encoding analyses show that fine-tuning selectively amplifies phrase encoding in left Superior Temporal Gyrus (STG) and pars opercularis at mid-to-high transformer layers, with no improvement for word representations. Third, representational similarity analysis confirms that fine-tuning improves model--brain geometric alignment specifically for phrases. The persistent null for phrase-minus-word residuals suggests that phrases function as holistic, chunk-like units rather than products of separable compositional operations. Together, these results identify phrase-level representation as a linguistic granularity at which LLM attention shows particularly strong correspondence with human language cortex, and demonstrate that targeted training objectives can selectively strengthen model--brain alignment.

Do Large Language Models Acquire Phrase-Based Processing? Evidence from Eye Movements, fMRI Encoding, and Model-Brain Alignment After Fine-Tuning

Children’s acquisition of filler-gap dependencies has been argued by some to depend on innate grammatical knowledge, while others suggest that the distributional evidence available in child-directed speech suffices. Unfortunately, the relevant input is difficult to quantify at scale with fine granularity, making this question difficult to resolve. We present a system that identifies three core filler-gap constructions in spoken English corpora -- matrix wh-questions, embedded wh-questions, and relative clauses -- and further identifies the extraction site (i.e., subject vs. object vs. adjunct). Our approach combines constituency and dependency parsing, leveraging their complementary strengths for construction classification and extraction site identification. We validate the system on human-annotated data and find that it scores well across most categories. Applying the system to 57 English CHILDES corpora, we are able to characterize children’s filler-gap input and their filler-gap production trajectories over the course of development, including construction-specific frequencies and extraction-site asymmetries. The resulting fine-grained labels enable future work in both acquisition and computational studies, which we demonstrate with a case study using filtered corpus training with language models.

What Exactly do Children Receive in Language Acquisition? A Case Study on CHILDES with Automated Detection of Filler-Gap Dependencies

Human discourse comprehension includes graded expectations about whether a speaker is likely to lie. If language models capture human-like discourse expectations, they should be sensitive not only to factual consistency but also to lie expectancy as a contextual probability from complex pragmatic cues. We test this idea using discourse scenarios with varying incentives to deceive. Human lie probability is estimated from free continuations, and model lie expectancy is derived from the probability mass assigned to human-produced lie versus truth continuations. Across Qwen3 models, likelihood-derived lie mass aligns strongly with human lie expectancy. The best performance comes from the base checkpoints. By contrast, post-trained and mode-specialized variants show weaker alignment. 
Qualitative analysis suggests a structured error pattern: models tend to overpredict lies when a response directly conflicts with known facts, but underpredict them when lie expectancy depends more on contextual pressures such as politeness, self-protection, or strategic gain. These results suggest that graded lie expectancy is recoverable from model continuation probabilities and can be learned, at least in part, through the ordinary next-token prediction objective.

Graded Expectations: Do Large Language Models Show Human-like Sensitivity to the Likelihood of Deceptive Speech Acts?

Research in sound discrimination demonstrates that listeners exhibit reduced sensitivity to acoustic differences between allophones, as opposed to phonemes. Previous studies indicates that highly predictable, complementary distribution of allophones contributes to this limited sensitivity by providing strong contextual cues. Building on these insights, this study investigates the role of predictability in sound discrimination within a supervised contrastive learning framework. Specifically, we examine how varying levels of predictability affect the ability to distinguish sounds and whether this influence is categorical or gradual. Additionally, we explore the interaction between acoustic distance and predictability, as well as how the presence of other contrasts within a language modulates this process. Our findings indicate that only full predictability leads to a significant decline in discrimination performance, demonstrating a categorical effect. This impairment can be alleviated as acoustic distance increases. Moreover, the presence of additional contrasts sharing the relevant acoustic dimension enhances discriminability, showing the importance of contextual contrasts in speech perception.

Roles of Predictability and Acoustic Distance in Sound Discrimination via Contrastive Learning

Given a listener's native language, some non-native contrasts may be harder to discriminate than others. The computation required to mimic this variable difficulty is not yet known. The present work approaches this question by training small supervised feedforward neural networks to perform Spanish vowel classification and then evaluating model classification of Catalan vowels, thereby approximating Spanish-listeners' cross-linguistic perception of Catalan. Vowels were extracted from Spanish and Catalan audio corpora, respectively. Ultimately, Spanish models exhibited expected misperception of Catalan's /e/-/ɛ/, /o/-/ɔ/, and /ɛ/-/a/ contrasts; Spanish-dominant listeners have difficulty perceiving these contrasts, and Spanish models classified Catalan /ɛ/ as /e/ or /a/, and Catalan /ɔ/ as /o/. This demonstrates that small supervised neural models are capable of making specific, cross-linguistic perceptual predictions given realistic input.

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