Syntax and Semantics Meet in the “Middle”: Probing the Syntax-Semantics Interface of LM s Through Agentivity (2023)

Abstract: Recent advances in large language models have prompted researchers to examine their abilities across a variety of linguistic tasks, but little has been done to investigate how models handle the interactions in meaning across words and larger syntactic forms—i.e. phenomena at the intersection of syntax and semantics. We present the semantic notion of agentivity as a case study for probing such interactions. We created a novel evaluation dataset by utilitizing the unique linguistic properties of a subset of optionally transitive English verbs. This dataset was used to prompt varying sizes of three model classes to see if they are sensitive to agentivity at the lexical level, and if they can appropriately employ these word-level priors given a specific syntactic context. Overall, GPT-3 text-davinci-003 performs extremely well across all experiments, outperforming all other models tested by far. In fact, the results are even better correlated with human judgements than both syntactic and semantic corpus statistics. This suggests that LMs may potentially serve as more useful tools for linguistic annotation, theory testing, and discovery than select corpora for certain tasks.

## introduction consider the english sentences in (1) below: these sentences display an interesting property of certain optionally transitive verbs in english. although they share an identical surface syntactic structure-a noun phrase in subject position followed by the intransitive form of the verb and an adverb phrase modifying the verb-they entail very different things about the roles of their subjects. the subject of (1a) is someone that does the action of writing; in other words, this author is an agent in the writing event. on the other hand, the subject of (1b), this passage, doesn't do any writing-it is what is created in the event of writing. in contrast to this author, this passage is a patient. the agent and patient roles are not discrete categories, but rather prototypes on opposite ends of a continuum. these "protoroles" have a number of contributing properties such as causing an event for agents and undergoing change of state for patients @xcite . the contrast between the minimal pair in (1) suggests that there are lexical semantic properties of the subjects that give rise to these two distinct readings: one that describes how the subject generally does an action as in (1a), and another that describes how an event generally unfolds when the subject undergoes an action as in (1b). intuitively, a speaker may know from the meaning of author that authors are animate, have some degree of volition, and typically write things, whereas passages (of text) are inanimate, have no volition, and are typically written. the knowledge of these aspects of meaning must somehow interact with the syntactic form of the sentences in (1) to disambiguate between the two possible readings, and an agent or patient role for the subject follows from the meaning of the statement as a whole. now consider the (somewhat unusual) sentences in (2) which use the transitive form of write: at first glance, the above sentences (with the same sense of write as in 1) are infelicitous unless we imagine some obscure context where this author is something like a character in a text and this passage is somehow anthropomorphized and capable of writing; these contexts go against our natural intuitions of the semantics of "passage" and "author". 1 unlike the syntactic form of the sentences in (1), the explicit inclusion of both arguments (subject and direct object) now forces whatever is in subject position to be the agent and whatever is in object position to be more like a patient, regardless of the typical semantic properties of the arguments. taken together, the examples in (1) and (2) illustrate a compelling interaction at the syntaxsemantics interface. more specifically, we see a two-way interaction: first, near-identical surface forms acquire completely different entailments about their subjects solely depending on the choice of subject, while conversely certain syntactic forms can influence the semantic role of an argument regardless of the usual behavior of said argument. we aim to investigate the linguistic capabilities of language models with regards to this interaction. prior work in studying lms as psycholinguistic subjects has largely focused on syntax and grammatical well-formedness @xcite . however, as illustrated in the above examples, there are instances of near-identical syntactic structures that can give rise to different meanings depending on the individual lexical items as well as surrounding context. thus evaluating lms on syntax, while a necessary starting point, does not give us a sufficient measure of lm linguistic capabilities. while other work such as ettinger (2020), @xcite , and @xcite (among others) evaluate lms on a variety of tests involving semantics and pragmatics, they do not investigate the interaction between the meanings associated with syntactic forms and those of individual lexical items. thus, we not only need to evaluate syntax and utilization of semantic knowledge, but we also need to understand how interactions of meaning at different linguistic levels-i.e. morphological, lexical, phrasal-may alter model behavior. exploring phenomena within the syntax-semantics interface is a compelling approach as it gives us access to specific aspects of semantics while allowing precise control over syntactic form between levels. in this work, we probe the syntax-semantics interface of several language models, focusing on the semantic notion of agentivity. we do this by prompting models to label nouns in isolation or in context as either agents or patients from a curated test set of noun-verb-adverb combinations that dis-(to) this author easily). regardless, given that the agent and patient roles as defined by @xcite are prototypes on a scale, this author in the recipient reading is closer to the patient role. probing for lms for their knowledge of agentivity in syntactic constructions as in (1) and ( 2 ) is a particularly insightful case study as it allows us to explore three interconnected questions in a highly controlled syntactic setting: i. do models display sensitivity to aspects of word-level semantics independent of syntactic context, and is such sensitivity aligned with human judgements? ( §3.1) ii. can models employ lexical semantics to determine the appropriate semantics of a sentence where the syntax is ambiguous between readings (as in 1)? ( §3.2) iii. can models determine the semantics of a sentence from syntax, disregarding lexical semantics when necessary (as in 2)? ( §3.3) additionally, the relatively infrequent pairings of semantic function and syntactic form of sentences such as (1b) are also interesting from a learnability and acquisition perspective for both lms and humans. how both come to process and acquire exceptions to a general "rule" has been a topic of debate since early connectionist models @xcite . hence, knowledge of lm capabilities in acquiring and processing these linguistic anomalies may serve as valuable insight to linguists, cognitive scientists, and nlp practitioners alike. ## methodology we constructed three experiments, each targeting one of the above questions through the lens of agentivity. we will first give a broad overview of each, and then go into detail about the general approach. experiment 1 ( §3.1) tests whether language models are sensitive to the word-level semantics of nouns with regards to agentivity, such as whether nouns like author and passage are more likely to be agents or patients without any surrounding context. this is analogous to the idea that speakers have intuition for how entities prototypically act in events, e.g. that authors write and passages are written, and that this extends to how we categorize their roles in events @xcite . experiment 2 ( §3.2) tests whether language models can disambiguate between the possible readings of sentences of the form in (1)-i.e. if figure 1 : prompt setup for each experiment. note that the examples given for exp 1 are not meant to be hard labels, rather they are "tendencies" for these nouns. in exp 2, the noun itself determines whether the sentence is considered intr-agent or intr-patient; in exp 3, we force the noun to take the agent or patient role by placing it in subject (trans-agent) or object (trans-patient) position. finally, experiment 3 ( §3.3) tests whether language models can disregard word-specific priors to identify whether the noun of interest in a sentence with a transitive verb (such as those in 2) is an agent or patient. since the semantic role of the noun maps directly to its syntactic position in these sentences, all subjects should be agents and all objects should be patients. for our test set, we create sentences where the position of the noun is the subject (trans-agent) and sentences where it is the object (trans-patient) for every noun. ## experimental results we evaluate bloom (scao et al., 2022), gpt-2 @xcite , and gpt-3 @xcite ) models of varying sizes for all experiments. since previous work has shown that models are highly sensitive to the ordering of examples @xcite , we run each experiment twice: once with the order shown in figure 1 where an agent is first (apap ordering) and again with the first example moved to the bottom (papa ordering). we compare models based on their average performance across both orderings. note, however, that some models are more sensitive to orderings than others; some models (like text-davinci-003) are largely invariant to example ordering. in appendix d, we report results from both experiments. ## a closer look at davinci-003 given that gpt-3 davinci-003 does extremely well, a natural question to ask is whether davinci-003 "fails" in similar ways to humans-i.e. we can see whether the nouns that are misclassified in the intransitive sentence setting ( §3.2) are more ambiguous to humans as well. in both apap and papa orderings, all or nearly all of what davinci-003 gets incorrect are patient subjects; all 78 incorrectly classified subjects of sentences in the apap ordering are patients, and 69 of the 70 incorrect subjects in the papa ordering are patients. from this, one way to answer the above question is to compare this subset of nouns with the subset of nouns with a "patient" label (in the intransitive construction) that humans tend to rate as more agentive. ## related works there has been extensive work in the psycholinguistics literature investigating how humans make use of the relationship between events described by verbs and nouns that may participate in these events, which is especially relevant to the analysis described in §4.1. works such as @xcite and @xcite have shown that humans utilize information about thematic fit to resolve ambiguity in sentence processing, mainly focusing on garden-path sentences. along this line of work, @xcite and @xcite created human judgement datasets for thematic fit by asking humans to rate nouns associated with events (e.g. a crook arresting/being arrested by someone) on a scale from 1 (very uncommon/implausible) to 7 (very common/plausible). as stimuli, humans are given the noun, the verb describing the event, and the role of the noun. while this setup is similar to our dataset, they focus on the explicit relationship between the event and the noun, while our data is meant to focus on the relationship between the prototypical role of a noun (out of context) and its role in a controlled syntactic environment. furthermore, as we would like the agent/patient distinction to be a minimal pair resulting changing the noun in an identical surface form, the sets of nouns and verbs between their studies and ours only partially overlap. this study also follows a well-established line of work on lms as psycholinguistic subjects @xcite @xcite . a large portion of this work focuses on probing lms for sensitivity to the wellformedness of sentences containing various syntactic structures such as subject-verb agreement @xcite , relative clauses @xcite , and filler-gap dependencies @xcite , among others. a closely-related work by @xcite investigates how bert classifies grammatical role of entities in non-prototypical syntactic positions, similar to our setup in experiment 3. there have also been works on evaluating and probing lms for semantic/pragmatic knowledge. ettinger (2020) created a suite of tests drawn from human language experiments to evaluate commonsense reasoning, event knowledge, and negation. the cogs challenge @xcite , which contains related tests to ours with regards to argument alternation, tests for whether lms can learn to generalize about passivization and unnacusative-transitive alternations in english. @xcite test lms for their ability to attribute properties to concepts and further test property inheritance. with regards to lexical semantics, @xcite investigate how type-level lexical information from words in context is stored in models across six typologically diverse languages. however, our work is distinct from both previous syntax-and semantics-focused probing and evaluation in its focus on the interactions between the aspects of meaning in individual lexical items with larger syntactic structures or constructions. nevertheless, methodologies from these research areas have informed the construction of our experiments. our use of minimal pairs to form sentences with contrasting semantic roles is similar to the construction of the blimp dataset @xcite and other test suites. furthermore, we treat the "agent"/"patient" labelling task as classification based on the generation probabilities of the labels, following @xcite 's method of using generation probabilities for grammaticality judgements. another relevant recent line of work within nlp is inspired by construction grammar (cxg), a branch of theories within cognitive linguistics that posits that constructions-defined as formmeaning pairings-are the basic building blocks of language @xcite . @xcite conducted a similar prompting experiment on the english article-adjective-numeral-noun construction, though this was focused on grammaticality judgements as opposed to aspects of semantics. @xcite probe for both syntactic and semantic understanding of the english comparative correlative. our study differs in that we analyze the impact of individual lexical items in what otherwise appears to be an identical syntactic construction, as opposed to analyzing competence of the construction as a whole. finally, @xcite find that sentences sharing the same argument structure constructions (ascs) are closer in the embedding space than those sharing the main verb; in light of our results, an interesting direction would be to see if sentences of the same surface construction may cluster based on finer-grained semantic distinctions. one consequence of our work-specifically with regards to davinci-003's extremely high correlation with human judgements-is the potential for lms as a tool for discovery in theoretical linguistics. this also has been argued recently by @xcite , who demonstrate this in the realm of lexical semantics through a case study of the english verb break. ## conclusion in order to gain insight into the behavior of lms with respect to the syntax-semantics interface, we created a suite of prompting experiments focusing on agentivity. we prompt varying sizes of bloom, gpt-2, and gpt-3 to see if they are sensitive to aspects of agentivity at the lexical level, and then to see if they can either utilize or discard these word-level priors given the appropriate syntactic context. gpt-3 davinci-003 performs exceptionally well in all three of our experimentsoutperforming all other models tested by far-and is even better correlated with human judgements than some proxy corpus statistics. we find it surprising that davinci-003 is able to capture an abstract notion of agentivity extremely well, but this ability does not appear to come from the size of the model alone as performance does not increase monotonically across any of the model families tested. what aspects of model training/data contribute to davinci-003's (or other models') performance on linguistic tasks may be an interesting area for future work. furthermore, a qualitative analysis of what davinci-003 gets incorrect reveals examples involving a number of linguistic confounders that make them more ambiguous to humans as well. the model's ability to "pick out" these linguistically interesting examples, combined with the high correlation with human ratings in experiment 1, showcases the potential of lms as tools for linguistic discovery for new phenomena, such as finding new classes of words or syntactic constructions that behave in unexpected ways. we hope these results encourage a more lively discussion between nlp researchers and linguists to unlock the potential of lms as tools for theoretical linguistics research.