gramforge 1.0.10

Efficient and multi-language generation from context free or sensitive grammars (CFG/CSG)

gramforge ⚒️

gramforge is a pythonic library for random (depth first) generation with declarative context-sensitive grammars (but also context free grammars) for synthetic data creation. One particularity is the option to generate in multiple languages in parallel (for example, tptp and pseudo-english).

Example with LogicNLI grammar with the gramforge DSL:
pip install gramforge

from gramforge import init_grammar, generate

def LogicNLI():
    ADJECTIVES = ['rich', 'quiet', 'old', 'tall', 'kind', 'brave', 'wise',
                  'happy', 'strong', 'curious', 'patient', 'funny', 'generous', 'humble']
    # (We selected adjectives with no clear semantic interference)
    NAMES = ['mary', 'paul', 'fred', 'alice', 'john', 'susan', 'lucy']

    R = init_grammar(['tptp','eng'])
    R('start(' + ','.join(['rule']*16) + ',' + ','.join(['fact']*8) + ')',
      '&\n'.join([f'({i})' for i in range(24)]),
      '\n'.join([f'{i}' for i in range(24)]))

    R('hypothesis(person,a)', '1(0)', '0 is 1')
    for a in ADJECTIVES:
        R('adj', a)
        R('adj', f'~{a}', f'not {a}', weight=0.2)

    R('property(adj,adj)', '(0(?)&1(?))', 'both 0 and 1')
    R('property(adj,adj)', '(0(?)|1(?))', '0 or 1')
    R('property(adj,adj)', '(0(?)<~>1(?))', 'either 0 or 1', weight=0.5)
    R('property(adj)', '0(?)', '0')

    R('rule(property,property)', '![X]:(0[?←X]=>1[?←X])',
      'everyone who is 0 is 1')
    R('rule(property,property)', '![X]:(0[?←X]<=>1[?←X])',
      'everyone who is 0 is 1 and vice versa')

    for p in NAMES:
        R('person', p)

    R('fact(person,property)', '1[?←0]', '0 is 1')
    R('fact(property)', '?[X]:(0[?←X])', 'someone is 0', weight=0.2)
    R('rule(fact,fact)', '(0)=>(1)', 'if 0 then 1')
    R('rule(fact,fact)', '(0)<=>(1)', 'if 0 then 1 and vice versa')
    return R

eng, tptp = "eng","tptp"
grammar = LogicNLI()
x=generate(grammar)
print(x@eng)
print(x@tptp)

Pre-loaded grammars

We feature pre-written grammars including:

• tinypy_grammar reproducing the tinypy, a synthetic toy grammar of python for LLM training/evaluation
• pygram_grammar ab advanced, state of the art python grammar, with valid functions, recursion, types, etc.
• FOL_grammar a sophisticated controlled grammar for first order logic (tptp) aligned with simplified English
• simple_english_grammar a subset of english
• arith_grammar a simple grammar for arithmetics
• regex_grammar a grammar generating regular expressions
• dyck_grammar nested parentheses

Example:

from gramforge.grammars import FOL_grammar, tinypy_grammar
from gramforge import generate
g=tinypy_grammar()
x=generate(g)
print(x@'py')

Abstract syntax trees

Generated expressions (x.generate) behave like anytree trees, fully exposing the abstract syntax tree which can be helpful for debugging, visualization or analysis of the generated examples.

Depth constraints

Generating synthetic data requires complexity management. gramforge implements efficient management of min_depth and max_depth constraints, with a "bushiness" knob (default=0.7) preventing the generated expressions from generating "spikes" that just overfit the minimum depth requirement.

Citation for the gramforge framework:

@inproceedings{sileo-2024-scaling,
    title = "Scaling Synthetic Logical Reasoning Datasets with Context-Sensitive Declarative Grammars",
    author = "Sileo, Damien",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.emnlp-main.301/",
    doi = "10.18653/v1/2024.emnlp-main.301",
    pages = "5275--5283",
}