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Although large language models (LLMs) have been touted for their ability to generate natural-sounding text, there are growing concerns around possible negative effects of LLMs such as data memorization, bias, and inappropriate language.…

Machine Learning · Computer Science 2023-05-10 Michael Kuchnik , Virginia Smith , George Amvrosiadis

Low-resource languages pose a challenge for machine translation with large language models (LLMs), which require large amounts of training data. One potential way to circumvent this data dependence is to rely on LLMs' ability to use…

Computation and Language · Computer Science 2026-04-09 Jackson Petty , Jaulie Goe , Tal Linzen

Incorporating stronger syntactic biases into neural language models (LMs) is a long-standing goal, but research in this area often focuses on modeling English text, where constituent treebanks are readily available. Extending constituent…

Computation and Language · Computer Science 2022-04-20 Shunsuke Kando , Hiroshi Noji , Yusuke Miyao

The regular languages with a neutral letter expressible in first-order logic with one alternation are characterized. Specifically, it is shown that if an arbitrary $\Sigma_2$ formula defines a regular language with a neutral letter, then…

Logic in Computer Science · Computer Science 2022-03-14 Corentin Barloy , Michaël Cadilhac , Charles Paperman , Thomas Zeume

We show that three fixed point structures equipped with (sequential) composition, a sum operation, and a fixed point operation share the same valid equations. These are the theories of (context-free) languages, (regular) tree languages, and…

Logic in Computer Science · Computer Science 2013-04-16 Zoltan Esik

What can large language models learn? By definition, language models (LM) are distributions over strings. Therefore, an intuitive way of addressing the above question is to formalize it as a matter of learnability of classes of…

Computation and Language · Computer Science 2025-01-14 Nadav Borenstein , Anej Svete , Robin Chan , Josef Valvoda , Franz Nowak , Isabelle Augenstein , Eleanor Chodroff , Ryan Cotterell

We study a formalization of the grammar induction problem that models sentences as being generated by a compound probabilistic context-free grammar. In contrast to traditional formulations which learn a single stochastic grammar, our…

Computation and Language · Computer Science 2020-03-31 Yoon Kim , Chris Dyer , Alexander M. Rush

Several methods are discussed that construct a finite automaton given a context-free grammar, including both methods that lead to subsets and those that lead to supersets of the original context-free language. Some of these methods of…

Computation and Language · Computer Science 2007-05-23 Mark-Jan Nederhof

We study the notion of sparseness for regular languages over finite trees and infinite words. A language of trees is called sparse if the relative number of $n$-node trees in the language tends to zero, and a language of infinite words is…

Formal Languages and Automata Theory · Computer Science 2025-07-08 Kord Eickmeyer , Georg Schindling

The hyperedge replacement grammar (HRG) formalism is a natural and well-known generalization of context-free grammars. HRGs inherit a number of properties of context-free grammars, e.g. the pumping lemma. This lemma turns out to be a strong…

Formal Languages and Automata Theory · Computer Science 2021-12-22 Tikhon Pshenitsyn

A new technique is presented to prove non-termination of term rewriting. The basic idea is to find a non-empty regular language of terms that is closed under rewriting and does not contain normal forms. It is automated by representing the…

Logic in Computer Science · Computer Science 2015-05-05 Jörg Endrullis , Hans Zantema

The central role of the lexicon in Meaning-Text Theory (MTT) and other dependency-based linguistic theories cannot be replicated in linguistic theories based on context-free grammars (CFGs). We describe Tree Adjoining Grammar (TAG) as a…

cmp-lg · Computer Science 2008-02-03 Owen Rambow , Aravind Joshi

In this paper we present a new parsing algorithm for linear indexed grammars (LIGs) in the same spirit as the one described in (Vijay-Shanker and Weir, 1993) for tree adjoining grammars. For a LIG $L$ and an input string $x$ of length $n$,…

cmp-lg · Computer Science 2008-02-03 Pierre Boullier

We use reinforcement learning to learn tree-structured neural networks for computing representations of natural language sentences. In contrast with prior work on tree-structured models in which the trees are either provided as input or…

Computation and Language · Computer Science 2016-11-29 Dani Yogatama , Phil Blunsom , Chris Dyer , Edward Grefenstette , Wang Ling

We study expression learning problems with syntactic restrictions and introduce the class of finite-aspect checkable languages to characterize symbolic languages that admit decidable learning. The semantics of such languages can be defined…

Logic in Computer Science · Computer Science 2023-03-22 Paul Krogmeier , P. Madhusudan

We study tree languages that can be defined in \Delta_2 . These are tree languages definable by a first-order formula whose quantifier prefix is forall exists, and simultaneously by a first-order formula whose quantifier prefix is . For the…

Formal Languages and Automata Theory · Computer Science 2019-03-14 Mikolaj Bojanczyk , Luc Segoufin

To Rogers (1994) we owe the insight that monadic second order predicate logic with multiple successors (MSO) is well suited in many respects as a realistic formal base for syntactic theorizing. However, the agreeable formal properties of…

cmp-lg · Computer Science 2008-02-03 Uwe Moennich

This paper is a continuation of the study of topological properties of omega context free languages (omega-CFL). We proved before that the class of omega-CFL exhausts the hierarchy of Borel sets of finite rank, and that there exist some…

Logic in Computer Science · Computer Science 2010-06-02 Olivier Finkel

Vector models of language are based on the contextual aspects of language, the distributions of words and how they co-occur in text. Truth conditional models focus on the logical aspects of language, compositional properties of words and…

Computation and Language · Computer Science 2018-10-29 Mehrnoosh Sadrzadeh , Reinhard Muskens

Many current NLP systems are built from language models trained to optimize unsupervised objectives on large amounts of raw text. Under what conditions might such a procedure acquire meaning? Our systematic experiments with synthetic data…

Computation and Language · Computer Science 2023-03-07 Zhaofeng Wu , William Merrill , Hao Peng , Iz Beltagy , Noah A. Smith
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