metadata
license: cc0-1.0
task_categories:
- text-generation
- code-generation
language:
- en
tags:
- programming-languages
- code-reasoning
- semantics
- interpreters
dataset_info:
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PLSemanticsBench: Large Language Models As Programming Language Interpreters
This repository hosts PLSemanticsBench, a benchmark introduced in the paper PLSEMANTICSBENCH: Large Language Models As Programming Language Interpreters.
PLSemanticsBench is the first benchmark for evaluating Large Language Models (LLMs) as programming language interpreters. It aims to study whether an LLM can execute programs purely based on a programming language's formal semantics. If successful, this capability would enable rapid prototyping of new programming languages and language features.
Paper: https://huggingface.co/papers/2510.03415 Code Repository: https://github.com/EngineeringSoftware/PLSemanticsBench
About PLSemanticsBench
The benchmark uses the imperative language IMP (a subset of C), formalized via small-step operational semantics (SOS) and rewriting-based operational semantics (K-semantics). Models are evaluated on three tasks ranging from coarse to fine:
| Task | Description |
|------|-------------|
| ✨ PredState| Predicts the final program state |
| ✨ PredRule | Predicts the ordered sequence of semantic rules needed to evaluate a program|
| ✨ PredTrace| Predicts the step-by-step execution of a program |
To distinguish pretraining memorization from semantic competence, the benchmark includes two nonstandard semantics obtained through systematic mutations of the standard rules. The evaluation sets—Human-Written, LLM-Translated, and Fuzzer-Generated—control difficulty by code-complexity metrics spanning size, control-flow, and data-flow axes.
Sample Usage
This section demonstrates how to get started with the plsemanticsbench library and how to load the dataset using the datasets library.
Installation and Basic Example
First, ensure you have the necessary Python environment set up and the plsemanticsbench library installed (refer to the GitHub repository for detailed instructions):
conda env create -f env.yaml
conda activate plsemanticsbench
export OPENAI_API_KEY='your-api-key-here'
Here's a minimal example to run an experiment:
from plsemanticsbench import GPTRunner, GPT_MODEL_ENUM
from plsemanticsbench import ExperimentArgs, LLMEvaluator
from plsemanticsbench import (
PROMPT_STRATEGY,
Task,
Formalization,
Semantics_Type,
Language,
PLDataset
)
# Model name
model_name = "o3-mini"
# Experiment args: Run the PredState task on the IMP language with
# standard semantics formalized using SOS and with direct prompting
exp_args = ExperimentArgs(
dataset=PLDataset.Human_Written,
task=Task.PredState,
language=Language.IMP,
formalization=Formalization.SOS,
semantics_type=Semantics_Type.Standard,
model_name=model_name,
prompt_strategy=PROMPT_STRATEGY.DA,
num_datapoints_to_run=2, # Run just 2 datapoints (omit to run entire dataset)
)
# Run inference using the OpenAI API
gpt_runner = GPTRunner(
gpt_model=GPT_MODEL_ENUM.O3_MINI,
args=exp_args,
)
# If prediction file is provided, the predictions will be saved to the file
predictions = gpt_runner.do_experiment()
llm_eval = LLMEvaluator(exp_args)
evaluation_result = llm_eval.evaluate_from_list(results=predictions, model_name=model_name)
print(evaluation_result)
Expected Output
The evaluation results will look like:
{
'accuracy': 1,
'malformed-count': 0,
}
Benchmark Access (Loading the Dataset)
You can load the dataset using the datasets library. Here are some examples for different configurations:
from datasets import load_dataset
# Load PredState task with standard semantics (uk) and K-semantics formalization (K) and with the Human Written (human-written) dataset
predstate_IMP_K_uk_human_written = load_dataset("EngineeringSoftware/PLSemanticsBench", name="predstate-IMP-K-uk-human-written")
# Load PredRule task with nonstandard semantics (mk) ans SOS formalization (SOS) and with the LLM Translated (llm-translated) dataset
predrule_IMP_SOS_mk_llm_translated = load_dataset("EngineeringSoftware/PLSemanticsBench", name="predrule-IMP-SOS-mk-llm-translated")
# Load PredState task with no-semantics (nk) and with the Fuzzer Generated (fuzzer-generated) dataset
predstate_IMP_nk_fuzzer_generated = load_dataset("EngineeringSoftware/PLSemanticsBench", name="predstate-IMP-nk-fuzzer-generated")
Dataset Splits
The benchmark dataset is organized into various configurations based on the task, formalization, semantics type (standard uk, nonstandard mk, or no semantics nk), and generation method (human-written, LLM-translated, or fuzzer-generated).
| Task | Split | Description |
|---|---|---|
| ✨ PredState (Final State Prediction) |
predstate-IMP-nk-{dataset-name} |
No semantics |
predstate-IMP-K-uk-{dataset-name} |
Standard semantics with K-semantics formalization | |
predstate-IMP-K-mk-{dataset-name} |
Nonstandard semantics with K-semantics formalization | |
predstate-IMP-SOS-uk-{dataset-name} |
Standard semantics with SOS formalization | |
predstate-IMP-SOS-mk-{dataset-name} |
Nonstandard semantics with SOS formalization | |
| ✨ PredRule (Semantic Rule Prediction) |
predrule-IMP-K-uk-human-written |
Standard semantics with K-semantics formalization |
predrule-IMP-K-mk-human-written |
Nonstandard semantics with K-semantics formalization | |
predrule-IMP-SOS-uk-human-written |
Standard semantics with SOS formalization | |
predrule-IMP-SOS-mk-human-written |
Nonstandard semantics with SOS formalization | |
| ✨ PredTrace (Execution Trace Prediction) |
predtrace-IMP-K-uk-human-written |
Standard semantics with K-semantics formalization |
predtrace-IMP-K-mk-human-written |
Nonstandard semantics with K-semantics formalization | |
predtrace-IMP-SOS-uk-human-written |
Standard semantics with SOS formalization | |
predtrace-IMP-SOS-mk-human-written |
Nonstandard semantics with SOS formalization |
Note: {dataset-name} can be human-written, llm-translated, or fuzzer-generated.
Data Example
One example of a dataset entry is as follows:
{
"program": "int ans; ans = 1; ...",
"syntax": "<program> :: ...",
"semantics": "ℤ := Set of integers ...",
"mutated-program": "int ans; ans = 1; ...",
"mutation-pattern": "KeyWordSwap",
"exec-trace": [
{
"linenumber": 1,
"rule": ["Rule 38", "Rule 39"],
"state": {"ans": 1}
}
],
"ground-truth": "<answer>...</answer>"
}
Citation
If you find this benchmark useful for your research, please cite the paper:
@article{ThimmaiahETAL25PLSemanticsBench,
title={PLSemanticsBench: Large Language Models As Programming Language Interpreters},
author={Aditya Thimmaiah, Jiyang Zhang, Jayanth Srinivasa, Junyi Jessy Li, Milos Gligoric},
year={2025},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2510.03415},
}