#Grammars hashtag - Bluesky

@tmlr-pub.bsky.social

1 month ago

Physics of Language Models: Part 1, Learning Hierarchical Language Structures

Zeyuan Allen-Zhu, Yuanzhi Li

Action editor: Jonathan Berant

https://openreview.net/forum?id=mPQKyzkA1K

#parsing #parse #grammars

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TMLR Published Papers

@tmlr-pub.bsky.social

2 months ago

Physics of Language Models: Part 1, Learning Hierarchical Language Structures

New #TMLR-Paper-with-Video:

Physics of Language Models: Part 1, Learning Hierarchical Language Structures

Zeyuan Allen-Zhu, Yuanzhi Li

https://tmlr.infinite-conf.org/paper_pages/mPQKyzkA1K

#parsing #parse #grammars

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Scribaland

@scribaland.bsky.social

11 months ago

#scribaland LOVE SONG FOR ETERNITY
The #miracle of this love song! Whenever you hear it, you must open your #eyes, wherever you are! #scribaland1 #love #song #eternity #faculty #faculties #galeries #game #games #gaming #genius #grammar #grammars #grammatical #google #googleadvertising #googleadwords

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Scribaland

@scribaland.bsky.social

11 months ago

#scribaland REVOLUTION OF IDIOTS
Morons are never unemployed nor retired! This human bullshit ruins lives and causes so much damage! #scribaland1 #revolution #idiots #forums #fact #facts #faculty #faculties #galerie #galeries #game #games #gaming #genius #grammar #grammars #grammatical #audible #art

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Scribaland

@scribaland.bsky.social

11 months ago

#scribaland A PLUNGED WORLD INTO DARKNESS?
After a #blackout in a #darkworld? #scribaland1 #plungedworld #darkness #aplungedworldintodarkness #films #flickr #forum #forums #fact #facts #faculty #faculties #galerie #galeries #game #games #gaming #genius #grammar #grammars #grammatical #google #figure

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Scribaland

@scribaland.bsky.social

11 months ago

#scribaland DECLINING OF MANHOOD
The #fallofman empire? #scribaland1 #declining #manhood #decliningofmanhood #appleadvertising #applebook #applebooks #applemusic #appleplay #appletv #appletvplus #artgalery #artgaleries #audiobook #grammars #grammatical #google #googleadvertising #genius #grammar #ai

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Scribaland

@scribaland.bsky.social

11 months ago

#scribaland ECOLOGICAL-ANXIETY
Is eco-anxiety a new #disease of #climatecrisis? #scribaland1 #ecological #anxiety #forum #forums #fact #facts #faculty #faculties #galerie #galeries #game #games #gaming #genius #grammar #grammars #grammatical #college #colleges #interviewer #interviewers #institutes

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Andreas Zeller

@andreaszeller.mastodon.social.ap.brid.gy

11 months ago

FANDANGO: Evolving Language-Based Testing José Antonio Zamudio Amaya, Marius Smytzek, Andreas Zeller Language-based fuzzers leverage formal input specifications (languages) to generate arbitrarily large and diverse sets of valid inputs for a program under test. Modern language-based test generators combine grammars and constraints to satisfy syntactic and semantic input constraints. ISLa, the leading input generator in that space, uses symbolic constraint solving to solve input constraints. Using solvers places ISLa among the most precise fuzzers but also makes it slow. In this paper, we explore search-based testing as an alternative to symbolic constraint solving. We employ a genetic algorithm that iteratively generates candidate inputs from an input specification, evaluates them against defined constraints, evolving a population of inputs through syntactically valid mutations and retaining those with superior fitness until the semantic input constraints are met. This evolutionary procedure, analogous to natural genetic evolution, leads to progressively improved inputs that cover both semantics and syntax. This change boosts the efficiency of language-based testing: In our experiments, compared to ISLa, our search-based FANDANGO prototype is faster by one to three orders of magnitude without sacrificing precision...

What do you get if you combine #grammars, #constraints, #evolutionary algorithms, and #Python in one? A mighty fuzzer! Check out our latest #FANDANGO work, to appear at #ISSTA2025 […]

[Original post on mastodon.social]

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Andreas Zeller

@andreaszeller.bsky.social

11 months ago

FANDANGO: Evolving Language-Based Testing José Antonio Zamudio Amaya, Marius Smytzek, Andreas Zeller Language-based fuzzers leverage formal input specifications (languages) to generate arbitrarily large and diverse sets of valid inputs for a program under test. Modern language-based test generators combine grammars and constraints to satisfy syntactic and semantic input constraints. ISLa, the leading input generator in that space, uses symbolic constraint solving to solve input constraints. Using solvers places ISLa among the most precise fuzzers but also makes it slow. In this paper, we explore search-based testing as an alternative to symbolic constraint solving. We employ a genetic algorithm that iteratively generates candidate inputs from an input specification, evaluates them against defined constraints, evolving a population of inputs through syntactically valid mutations and retaining those with superior fitness until the semantic input constraints are met. This evolutionary procedure, analogous to natural genetic evolution, leads to progressively improved inputs that cover both semantics and syntax. This change boosts the efficiency of language-based testing: In our experiments, compared to ISLa, our search-based FANDANGO prototype is faster by one to three orders of magnitude without sacrificing precision. The search-based approach no longer restricts constraints to constraint solvers' (miniature) languages. In FANDANGO, constraints can use the whole Python language and library. This expressiveness gives testers unprecedented flexibility in shaping test inputs. It allows them to state arbitrary goals for test generation: "Please produce 1,000 valid test inputs where the <voltage> field follows a Gaussian distribution but never exceeds 20 mV."

What do you get if you combine #grammars, #constraints, #evolutionary algorithms, and #Python in one? A mighty fuzzer! Check out our latest #FANDANGO work, to appear at #ISSTA2025: publications.cispa.de/articles/sta... To try out Fandango yourself, check out its home page: fandango-fuzzer.github.io