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Improve dataset card: Add description, links, sample usage, and update metadata for PLSemanticsBench
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 ---
license: cc0-1.0
task_categories:
- text-generation
- code-generation
language:
- en
tags:
- programming-languages
- code-reasoning
- semantics
- interpreters
dataset_info:
- config_name: predrule-IMP-SOS-mk-human-written
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- split: train
path: predtrace-IMP-SOS-mk-human-written/train-*
- config_name: predtrace-IMP-SOS-uk-human-written
data_files:
- split: train
path: predtrace-IMP-SOS-uk-human-written/train-*
---
# 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](https://huggingface.co/papers/2510.03415).
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](https://huggingface.co/papers/2510.03415)
**Code Repository:** [https://github.com/EngineeringSoftware/PLSemanticsBench](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](https://github.com/EngineeringSoftware/PLSemanticsBench) for detailed instructions):
```bash
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:
```python
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:
```python
{
'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:
```python
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**<br>(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**<br>(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**<br>(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:
```json
{
"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:
```bibtex
@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},
}
```