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Overview The SMTResults class interprets the output from SMT solvers (like CVC5) to determine whether a first-order logic formula represents a valid statement or contains a logical fallacy. It also uses LLMs to generate human-readable counterexamples when fallacies are detected.
Class Definition class SMTResults : def __init__ ( self , output_file_path , sentence_data_path )
Parameters Path to the SMT solver output file containing SAT/UNSAT result and model
Path to JSON file containing original sentence data with keys:
Claim: The claim portion of the sentenceImplication: The implication portionReferring expressions: Entities identifiedProperties: Properties of entitiesFormula: The generated FOL formula Instance Attributes Classification of the formula. Values:
"valid statement": Formula is satisfiable (UNSAT in negated form)"logical fallacy": Formula is unsatisfiable (SAT in negated form) Human-readable counterexample generated by LLM (only for logical fallacies)
How It Works The SMT solver receives the negation of the original formula. The interpretation logic:
UNSAT Result : Negation is unsatisfiable → Original formula is validSAT Result : Negation is satisfiable → Original formula is invalid (fallacy)
When the solver returns SAT, it provides a model (counterexample) showing variable assignments that make the negated formula true, thereby disproving the original claim.
Methods get_results def get_results ( get_interpretation = True )
Prints the interpretation of SMT results to stdout.
Parameters: Whether to print the counterexample explanation for fallacies
Output Format: The given statement is a <formula_type> [If fallacy: <counter_example>]
Example: Valid Statement
Example: Logical Fallacy
results = SMTResults( "output.txt" , "sentence_data.json" ) results.get_results() # Output: The given statement is a valid statement
SMT Solver Output File Expected format:
sat (model (define-fun x () BoundSet @uc_BoundSet_0) (define-fun P ((x!0 BoundSet)) Bool (= x!0 @uc_BoundSet_0)) )
Or:
Sentence Data JSON File Required structure:
{ "Claim" : "All cats are animals" , "Implication" : "Therefore, all animals are cats" , "Referring expressions" : "cats, animals" , "Properties" : "Cat(x), Animal(x)" , "Formula" : "forall x (Cat(x) -> Animal(x)) -> forall y (Animal(y) -> Cat(y))" }
Counterexample Generation When a fallacy is detected, the class:
Loads the original sentence data from JSON
Reads the prompt template from prompt_counter_example.txt
Formats the prompt with:
Original claim and implication
Referring expressions and properties
The FOL formula
The SMT solver’s model (counterexample)
Sends to LLM via get_llm_result()
Stores the natural language explanation in self.counter_example
The get_llm_result() function must be defined in llm.py module. Ensure this module is available and properly configured with API credentials.
Complete Usage Example import json import subprocess from nl_to_fol import NL2FOL from cvc import CVCGenerator from interpret_smt_results import SMTResults # Step 1: Convert natural language to FOL sentence = "All politicians are corrupt. John is a politician. Therefore, everyone is corrupt." nl2fol = NL2FOL( sentence = sentence, model_type = 'gpt' , pipeline = None , tokenizer = None , nli_model = nli_model, nli_tokenizer = nli_tokenizer ) fol_formula, _ = nl2fol.convert_to_first_order_logic() # Step 2: Generate CVC5 script generator = CVCGenerator(fol_formula) cvc_script = generator.generateCVCScript() with open ( "formula.smt2" , "w" ) as f: f.write(cvc_script) # Step 3: Run SMT solver result = subprocess.run( [ "cvc5" , "formula.smt2" ], capture_output = True , text = True ) with open ( "solver_output.txt" , "w" ) as f: f.write(result.stdout) # Step 4: Save sentence data sentence_data = { "Claim" : nl2fol.claim, "Implication" : nl2fol.implication, "Referring expressions" : nl2fol.claim_ref_exp, "Properties" : nl2fol.claim_properties, "Formula" : fol_formula } with open ( "sentence_data.json" , "w" ) as f: json.dump(sentence_data, f) # Step 5: Interpret results results = SMTResults( "solver_output.txt" , "sentence_data.json" ) results.get_results() # Access results programmatically if results.formula_type == "logical fallacy" : print ( f " \n Counterexample: \n { results.counter_example } " )
Command-Line Usage python interpret_smt_results.py < output_fil e > < sentence_data_jso n >
python interpret_smt_results.py solver_output.txt sentence_data.json
Error Handling If either input file doesn’t exist, Python will raise FileNotFoundError: FileNotFoundError : [Errno 2 ] No such file or directory: 'output.txt'
Ensure both files exist before creating SMTResults instance. If sentence_data.json is malformed: json.decoder.JSONDecodeError: Expecting property name enclosed in double quotes
Validate JSON structure matches expected format. If required keys are missing from sentence data: Ensure all required keys are present: Claim, Implication, Referring expressions, Properties, Formula. Show Invalid Solver Output
If solver output doesn’t start with “sat” or “unsat”: ValueError : not enough values to unpack
Verify the solver output file format is correct. Integration with Evaluation import pandas as pd from pathlib import Path def evaluate_dataset ( results_dir ): """ Evaluate a batch of SMT results """ results_dir = Path(results_dir) evaluations = [] for output_file in results_dir.glob( "*_output.txt" ): # Find corresponding sentence data base_name = output_file.stem.replace( "_output" , "" ) json_file = results_dir / f " { base_name } _data.json" if not json_file.exists(): continue # Interpret results results = SMTResults( str (output_file), str (json_file)) evaluations.append({ "file" : base_name, "type" : results.formula_type, "has_counterexample" : results.counter_example is not None }) return pd.DataFrame(evaluations) # Run evaluation df = evaluate_dataset( "results/" ) print (df[ 'type' ].value_counts())
Dependencies The module requires:
llm.py: Must define get_llm_result(prompt) functionjson: Standard library for JSON parsingsys: Standard library for command-line argumentsprompt_counter_example.txt: Prompt template file
Ensure prompt_counter_example.txt exists in the working directory with appropriate placeholders for claim, implication, referring expressions, properties, formula, and counter model.
See Also 