LL(*): the foundation of the ANTLR parser generator

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Recurrent Neural Network Grammars (2016, methodology): “A further connection is to LL(∗) parsing which uses an unbounded lookahead (compactly represented by a DFA) to distinguish between parse alternatives in a top-down parser (Parr and Fisher, 2011); however, our parser uses an RNN encoding of the lookahead rather than a DFA.”
An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation (2018, methodology): “ate an expressive yet vocabulary-limited representation. We use a Java lexer and a parser to represent each buggy and fixed method within a BFP as a stream of tokens. The lexer, built on top of ANTLR [62, 63], tokenizes the raw code into a stream of tokens, that is then fed into a Java parser [84], ”
Synchromesh: Reliable code generation from pre-trained language models (2022, methodology): “To facilitate this process, we created a library that extends any parser generated by ANTLR (Parr & Fisher, 2011), a popular LL( ∗ ) top-down parser generator, to provide token-level completions.”