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This eye-tracking study examined how heritage speakers of Spanish process gender agreement morphology at a distance, focusing on the activation of the gender feature during sentence processing. Previous work is conceptually replicated and further extended by assessing (1) whether reduced sensitivity to gender agreement mismatches when another word intervenes between the head noun and its modifying adjective stems from weakened gender feature activation, (2) whether a gender-marked determiner enhances this activation, and (3) whether Age of Onset of Bilingualism (AOB) plays a role in this activation. Fifty-three English-dominant heritage speakers of Spanish and a comparison group of 32 Spanish-dominant monolingually raised speakers read sentences with and without gender agreement mismatches while their eye movements were monitored. Sentences contained mismatches in adjectives modified by the intensifier “muy” under two conditions: a No Cue condition (e.g., árboles muy altos/*altas) and a Cue condition with a gender-marked determiner (e.g., unos árboles muy altos/*altas). Statistical modeling of the eye-tracking data suggests similar effects for both groups in the No Cue condition, but AOB and proficiency modulated sensitivity for heritage speakers with a later AOB (4–6). Gender cues on the determiner (Cue condition) impacted the time course of agreement processing for all groups, the total time spent reading mismatches for all heritage speakers as a function of proficiency, and the rereading time for heritage speakers with a later AOB (4–9). We consider the role of Age of Onset of Bilingualism (AOB) and proficiency in morphosyntactic processing, feature retrieval, and cue facilitation in heritage language processing.

This study focuses on the acquisition of grammatical gender agreement at different levels of language proficiency in English, paying attention to syntactic constraints and those arising from the influence of the mother tongue. Thus, the objectives are, first, to categorise mismatches in English grammatical gender acquisition when learnt by Spaniards at A2, B1, B2 and C1 levels of proficiency established by the Common European Framework of Reference for Languages (Council of Europe, 2001; 2018). Second, the study aims to determine whether these mismatches are produced by syntactic failure or by mother tongue transfer. Additionally, it explores whether communicative competence may be affected by grammatical gender acquisition mismatches. In this paper, the failures in grammatical gender agreement were identified and classified into syntactic and mother tongue transfer. The methodology followed was based on a corpus-driven approach. First, a corpus of essays written by Spanish learners of English with A2, B1, B2 and C1 language proficiency levels was compiled. The grammatical gender agreement mismatches were then identified to ascertain the students’ ability to assign gender in English. Finally, the frequencies of failure of grammatical gender agreement at the different levels of language proficiency were classified into syntactic and language transfer categories. The results showed not only the grammatical gender agreement mechanisms when acquiring English at the different levels of language proficiency but also which kind of components in communicative competence may be affected by gender agreement processes.

We introduce The Benchmark of Linguistic Minimal Pairs (BLiMP), a challenge set for evaluating the linguistic knowledge of language models (LMs) on major grammatical phenomena in English. BLiMP consists of 67 individual datasets, each containing 1,000 minimal pairs—that is, pairs of minimally different sentences that contrast in grammatical acceptability and isolate specific phenomenon in syntax, morphology, or semantics. We generate the data according to linguist-crafted grammar templates, and human aggregate agreement with the labels is 96.4%. We evaluate -gram, LSTM, and Transformer (GPT-2 and Transformer-XL) LMs by observing whether they assign a higher probability to the acceptable sentence in each minimal pair. We find that state-of-the-art models identify morphological contrasts related to agreement reliably, but they struggle with some subtle semantic and syntactic phenomena, such as negative polarity items and extraction islands.

Know your limits. This familiar adage is not an inspirational rallying cry or a recipe for bold action. It serves better as the motto for the tortoise than the hare. But, after many false starts over the past twenty years, states were well advised to heed it when negotiating the Paris Agreement. While it is still far too early to say whether the Agreement will be a success, its comparatively modest approach provides a firmer foundation on which to build than its more ambitious predecessor, the Kyoto Protocol.

We argue for a uniformly upward-probing implementation of Agree (Upward Agree, UA), showing that it can account for a wide range of long-distance agreement phenomena, including cases that have been cited as evidence against earlier UA models of ϕ-agreement. Our core revision to earlier UA approaches is a distinction between checking and valuation: while we maintain that checking is strictly regulated by UA, we propose that valuation depends on a secondary relation of accessibility, which allows valuation of a higher probe by a lower, accessible goal, in cases where the checker of the probe cannot (fully) value it. This model provides a better account of asymmetries between Spec-head agreement and long-distance agreement patterns, and also accounts for movement-agreement interactions without a need for EPP features.

This work investigates the morphosyntax of nominal expressions in Standard Italian that have multiple adjectives in “split coordination,” which permits a plural noun to be modified by singular adjectives, for example le mani destra e sinistra (the hand.pl left.sg and right.sg). The proposal is (i) that these expressions are built from multidominant structures, with a constituent shared by the conjuncts, and (ii) that plural marking on the noun reflects “summative” feature resolution on the nP comparable to coordination resolution. This proposal captures various properties of split-coordinated expressions, including the availability of adjective stacking and of feature-mismatched conjuncts, as well as agreement with a class of nouns that “switch” gender in the plural. Taking agreement with resolving features to be a form of semantic agreement, which has been argued to be possible only in certain syntactic configurations (Smith 2015, 2017, 2021), the account captures prenominal-postnominal adjective asymmetries in split coordination. The work offers a coherent account of coordination and semantic agreement in the nominal domain, connects split coordination to related phenomena such as nominal right node raising and adjectival hydras, and, more broadly, evinces the unity of nominal and verbal agreement, pace analyses of nominal concord (Norris 2014).

Large language models (LLMs) have demonstrated impressive performances in various medical domains, prompting an exploration of their potential utility within the high-demand setting of emergency department (ED) triage. This study evaluated the triage proficiency of different LLMs and ChatGPT, an LLM-based chatbot, compared to professionally trained ED staff and untrained personnel. We further explored whether LLM responses could guide untrained staff in effective triage. This study aimed to assess the efficacy of LLMs and the associated product ChatGPT in ED triage compared to personnel of varying training status and to investigate if the models' responses can enhance the triage proficiency of untrained personnel. A total of 124 anonymized case vignettes were triaged by untrained doctors; different versions of currently available LLMs; ChatGPT; and professionally trained raters, who subsequently agreed on a consensus set according to the Manchester Triage System (MTS). The prototypical vignettes were adapted from cases at a tertiary ED in Germany. The main outcome was the level of agreement between raters' MTS level assignments, measured via quadratic-weighted Cohen κ. The extent of over- and undertriage was also determined. Notably, instances of ChatGPT were prompted using zero-shot approaches without extensive background information on the MTS. The tested LLMs included raw GPT-4, Llama 3 70B, Gemini 1.5, and Mixtral 8x7b. GPT-4-based ChatGPT and untrained doctors showed substantial agreement with the consensus triage of professional raters (κ=mean 0.67, SD 0.037 and κ=mean 0.68, SD 0.056, respectively), significantly exceeding the performance of GPT-3.5-based ChatGPT (κ=mean 0.54, SD 0.024; P<.001). When untrained doctors used this LLM for second-opinion triage, there was a slight but statistically insignificant performance increase (κ=mean 0.70, SD 0.047; P=.97). Other tested LLMs performed similar to or worse than GPT-4-based ChatGPT or showed odd triaging behavior with the used parameters. LLMs and ChatGPT models tended toward overtriage, whereas untrained doctors undertriaged. While LLMs and the LLM-based product ChatGPT do not yet match professionally trained raters, their best models' triage proficiency equals that of untrained ED doctors. In its current form, LLMs or ChatGPT thus did not demonstrate gold-standard performance in ED triage and, in the setting of this study, failed to significantly improve untrained doctors' triage when used as decision support. Notable performance enhancements in newer LLM versions over older ones hint at future improvements with further technological development and specific training.

Research on human language converges on a view in which a grammatical “subject” is the most saliently encoded entity in mental representation. However, subjecthood is not a syntactically uniform phenomenon. Notably, many languages encode morphological distinctions between subjects of transitive verbs (i.e., verbs that require an object) and subjects of intransitive verbs. We ask how this typological pattern manifests in a language like English (which does not morphologically signal it) by examining the “distinctiveness” of transitive versus intransitive subjects in memory during online sentence processing. We conducted a self-paced reading experiment that tested for “attraction” effects (Dillon et al., Journal of Memory and Language , 69 (2), 85–103, 2013 ; Wagers et al., Journal of Memory and Language , 61 , 206–237, 2009 ) in the processing of subject-verb number agreement. We find that transitive subjects trigger attraction effects, but that these effects are mitigated for in transitive subject attractors (independently of the number of other noun phrases present in the intervening clause). We interpret this as indicating that transitive subjects are less distinctive and therefore less representationally salient than intransitive subjects: This is because a transitive subject must compete with another clause-mate core argument (i.e., a direct object), which draws on resources from the same pool of memory resources. On the other hand, an intransitive subject minimally only competes with a non - core argument (i.e., an oblique noun phrase); this consumes fewer memory resources, leaving the subject to enjoy greater spoils.

Based on the cross- and intra-linguistic distribution of Person Case Constraint (PCC) effects, this paper shows that there can be no agreement in ϕ-features (PERSON, NUMBER, GENDER/NOUN-CLASS) which systematically lacks a morpho-phonological footprint. That is, there is no such thing as “abstract” ϕ-agreement, null across the entire paradigm. Applying the same diagnostic to instances of clitic doubling, we see that these do involve syntactic agreement. This cannot be because clitic doubling is agreement; it behaves like movement (and unlike agreement) in a variety of respects. Nor can this be because clitic doubling, qua movement, is contingent on prior agreement—since the claim that all movement depends on prior agreement is demonstrably false. Clitic doubling requires prior agreement because it is an instance of non-local head movement, and movement of X0 to Y0 always requires a prior syntactic relationship between Y0 and XP. In local head movement (the kind that is already permitted under the Head Movement Constraint), this requirement is trivially satisfied by (c-)selection. But in non-local cases, agreement must fill this role.