137 research outputs found
Do language models make human-like predictions about the coreferents of Italian anaphoric zero pronouns?
Some languages allow arguments to be omitted in certain contexts. Yet human
language comprehenders reliably infer the intended referents of these zero
pronouns, in part because they construct expectations about which referents are
more likely. We ask whether Neural Language Models also extract the same
expectations. We test whether 12 contemporary language models display
expectations that reflect human behavior when exposed to sentences with zero
pronouns from five behavioral experiments conducted in Italian by Carminati
(2005). We find that three models - XGLM 2.9B, 4.5B, and 7.5B - capture the
human behavior from all the experiments, with others successfully modeling some
of the results. This result suggests that human expectations about coreference
can be derived from exposure to language, and also indicates features of
language models that allow them to better reflect human behavior.Comment: Accepted at COLING 202
Probability in Phonological Generalizations: Modeling French Optional Final Consonants
Proceedings of the Twenty-Sixth Annual Meeting of the Berkeley Linguistics Society: General Session and Parasession on Aspect (2000
Can Peanuts Fall in Love with Distributional Semantics?
The context in which a sentence appears can drastically alter our
expectations about upcoming words - for example, following a short story
involving an anthropomorphic peanut, experimental participants are more likely
to expect the sentence 'the peanut was in love' than 'the peanut was salted',
as indexed by N400 amplitude (Nieuwland & van Berkum, 2006). This rapid and
dynamic updating of comprehenders' expectations about the kind of events that a
peanut may take part in based on context has been explained using the construct
of Situation Models - updated mental representations of key elements of an
event under discussion, in this case, the peanut protagonist. However, recent
work showing that N400 amplitude can be predicted based on distributional
information alone raises the question whether situation models are in fact
necessary for the kinds of contextual effects observed in previous work. To
investigate this question, we attempt to model the results of Nieuwland and van
Berkum (2006) using six computational language models and three sets of word
vectors, none of which have explicit situation models or semantic grounding. We
find that the effect found by Nieuwland and van Berkum (2006) can be fully
modeled by two language models and two sets of word vectors, with others
showing a reduced effect. Thus, at least some processing effects normally
explained through situation models may not in fact require explicit situation
models
When Is Multilinguality a Curse? Language Modeling for 250 High- and Low-Resource Languages
Multilingual language models are widely used to extend NLP systems to
low-resource languages. However, concrete evidence for the effects of
multilinguality on language modeling performance in individual languages
remains scarce. Here, we pre-train over 10,000 monolingual and multilingual
language models for over 250 languages, including multiple language families
that are under-studied in NLP. We assess how language modeling performance in
each language varies as a function of (1) monolingual dataset size, (2) added
multilingual dataset size, (3) linguistic similarity of the added languages,
and (4) model size (up to 45M parameters). We find that in moderation, adding
multilingual data improves low-resource language modeling performance, similar
to increasing low-resource dataset sizes by up to 33%. Improvements depend on
the syntactic similarity of the added multilingual data, with marginal
additional effects of vocabulary overlap. However, high-resource languages
consistently perform worse in multilingual pre-training scenarios. As dataset
sizes increase, adding multilingual data begins to hurt performance for both
low-resource and high-resource languages, likely due to limited model capacity
(the "curse of multilinguality"). These results suggest that massively
multilingual pre-training may not be optimal for any languages involved, but
that more targeted models can significantly improve performance
Structural Priming Demonstrates Abstract Grammatical Representations in Multilingual Language Models
Abstract grammatical knowledge - of parts of speech and grammatical patterns
- is key to the capacity for linguistic generalization in humans. But how
abstract is grammatical knowledge in large language models? In the human
literature, compelling evidence for grammatical abstraction comes from
structural priming. A sentence that shares the same grammatical structure as a
preceding sentence is processed and produced more readily. Because confounds
exist when using stimuli in a single language, evidence of abstraction is even
more compelling from crosslingual structural priming, where use of a syntactic
structure in one language primes an analogous structure in another language. We
measure crosslingual structural priming in large language models, comparing
model behavior to human experimental results from eight crosslingual
experiments covering six languages, and four monolingual structural priming
experiments in three non-English languages. We find evidence for abstract
monolingual and crosslingual grammatical representations in the models that
function similarly to those found in humans. These results demonstrate that
grammatical representations in multilingual language models are not only
similar across languages, but they can causally influence text produced in
different languages.Comment: Accepted at EMNLP 202
Report on Transfer Tax Restructuring
This report is submitted to the Council of the Section of Taxation, American Bar Association as a proposed response to the request of the Treasury Department for suggestions for reform of the Federal transfer taxes (the estate, gift, and generation-skipping transfer taxes). That request was contained in a letter dated November 19, 1985, from Ronald A. Pearlman, the Assistant Secretary (Tax Policy), to Hugh Calkins, then Section Chair.\u27 After receiving individual comment papers on the subject from members of the Section\u27s Committee on Estate and Gift Taxes, Mr. Calkins, on April 14, 1986, created this Task Force and asked it to prepare a more fundamental response
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