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 ---
license: cc-by-4.0
dataset_info:
- config_name: predrule-IMP-K-mk-human-written
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features:
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dtype: string
- name: program
dtype: string
- name: src-filename
dtype: string
- name: language
dtype: string
- name: mutated-program
dtype: string
- name: mutated
dtype: bool
- name: mutation-pattern
dtype: string
- name: K-evaluatable
dtype: bool
- name: ground-truth
dtype: string
splits:
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download_size: 41198
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- config_name: predstate-IMP-nk-llm-translated
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- name: program
dtype: string
- name: src-filename
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splits:
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dtype: string
- name: program
dtype: string
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dtype: string
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- name: max-if-depth
dtype: int64
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dtype: bool
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dtype: int64
- name: max-if-depth
dtype: int64
splits:
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- config_name: predtrace-IMP-SOS-mk-human-written
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- name: id
dtype: string
- name: program
dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: bool
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dtype: string
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dtype: bool
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dtype: int64
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dtype: int64
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dtype: string
- name: program
dtype: string
- name: src-filename
dtype: string
- name: semantics-type
dtype: string
- name: syntax
dtype: string
- name: semantics
dtype: string
- name: language
dtype: string
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dtype: string
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dtype: bool
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dtype: string
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dtype: int64
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num_bytes: 18283431
num_examples: 162
download_size: 1061198
dataset_size: 18283431
configs:
- config_name: predrule-IMP-K-mk-human-written
data_files:
- split: train
path: predrule-IMP-K-mk-human-written/train-*
- config_name: predrule-IMP-K-uk-human-written
data_files:
- split: train
path: predrule-IMP-K-uk-human-written/train-*
- config_name: predrule-IMP-SOS-mk-human-written
data_files:
- split: train
path: predrule-IMP-SOS-mk-human-written/train-*
- config_name: predrule-IMP-SOS-uk-human-written
data_files:
- split: train
path: predrule-IMP-SOS-uk-human-written/train-*
- config_name: predstate-IMP-K-mk-fuzzer-generated
data_files:
- split: train
path: predstate-IMP-K-mk-fuzzer-generated/train-*
- config_name: predstate-IMP-K-mk-human-written
data_files:
- split: train
path: predstate-IMP-K-mk-human-written/train-*
- config_name: predstate-IMP-K-mk-llm-translated
data_files:
- split: train
path: predstate-IMP-K-mk-llm-translated/train-*
- config_name: predstate-IMP-K-uk-fuzzer-generated
data_files:
- split: train
path: predstate-IMP-K-uk-fuzzer-generated/train-*
- config_name: predstate-IMP-K-uk-human-written
data_files:
- split: train
path: predstate-IMP-K-uk-human-written/train-*
- config_name: predstate-IMP-K-uk-llm-translated
data_files:
- split: train
path: predstate-IMP-K-uk-llm-translated/train-*
- config_name: predstate-IMP-SOS-mk-fuzzer-generated
data_files:
- split: train
path: predstate-IMP-SOS-mk-fuzzer-generated/train-*
- config_name: predstate-IMP-SOS-mk-human-written
data_files:
- split: train
path: predstate-IMP-SOS-mk-human-written/train-*
- config_name: predstate-IMP-SOS-mk-llm-translated
data_files:
- split: train
path: predstate-IMP-SOS-mk-llm-translated/train-*
- config_name: predstate-IMP-SOS-uk-fuzzer-generated
data_files:
- split: train
path: predstate-IMP-SOS-uk-fuzzer-generated/train-*
- config_name: predstate-IMP-SOS-uk-human-written
data_files:
- split: train
path: predstate-IMP-SOS-uk-human-written/train-*
- config_name: predstate-IMP-SOS-uk-llm-translated
data_files:
- split: train
path: predstate-IMP-SOS-uk-llm-translated/train-*
- config_name: predstate-IMP-nk-fuzzer-generated
data_files:
- split: train
path: predstate-IMP-nk-fuzzer-generated/train-*
- config_name: predstate-IMP-nk-human-written
data_files:
- split: train
path: predstate-IMP-nk-human-written/train-*
- config_name: predstate-IMP-nk-llm-translated
data_files:
- split: train
path: predstate-IMP-nk-llm-translated/train-*
- config_name: predtrace-IMP-K-mk-human-written
data_files:
- split: train
path: predtrace-IMP-K-mk-human-written/train-*
- config_name: predtrace-IMP-K-uk-human-written
data_files:
- split: train
path: predtrace-IMP-K-uk-human-written/train-*
- config_name: predtrace-IMP-SOS-mk-human-written
data_files:
- 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
## Table of Contents
- [About](#about)
- [Installation](#installation)
- [Quick Start](#quick-start)
- [Detailed Usage](#detailed-usage)
- [Benchmark](#benchmark)
- [Citation](#citation)
## About
PLSemanticsBench is the first benchmark for evaluating LLMs as programming language interpreters. We introduce three tasks to evaluate this:
| 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 |
You must implement [BaseRunner](https://github.com/EngineeringSoftware/PLSemanticsBench/blob/main/src/plsemanticsbench/core/exps/base_experiment.py)(`_query` method) to evaluate your models. We provide two example implementations for OpenAI models ([GPTRunner](https://github.com/EngineeringSoftware/PLSemanticsBench/blob/main/src/plsemanticsbench/core/exps/gpt_experiment.py)) and Ollama models ([OllamaRunner](https://github.com/EngineeringSoftware/PLSemanticsBench/blob/main/src/plsemanticsbench/core/exps/ollama_experiment.py)).
## Installation
### System Requirements
- Python 3.11 or higher
- OpenAI API key (for running experiments with OpenAI models)
### Step-by-Step Installation
1. Create and activate the conda environment:
```bash
conda env create -f env.yaml
conda activate plsemanticsbench
```
2. Set up your OpenAI API key (only for OpenAI models):
```bash
export OPENAI_API_KEY='your-api-key-here'
```
## Quick Start
We provide a bash script `quick` that:
1. Sets up the `plsemanticsbench` conda environment.
2. Pulls the `DeepSeek-R1 1.5B` model.
3. Evaluates the `DeepSeek-R1 1.5B` model on the `PredState` task with `no-semantics` and `chain-of-thought` prompting on the `Human-Written` dataset.
4. Prints the `accuracy` and `malformed-count` to screen.
5. Creates `metrics-predstate-deepseek-r1:1.5b.json` that contains the evaluation result.
```bash
bash quick
```
## Detailed Usage
### Basic Example
Here's a minimal example to get started:
```python
from plsemanticsbench import GPTRunner
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(args=exp_args)
# Generation (generate LLM prediction on the predstate task)
predictions = gpt_runner.do_experiment() # path to dump results can be provided
# Evaluation (evaluate LLM prediction against ground-truth)
llm_eval = LLMEvaluator(task=exp_args.task, semantics_type=exp_args.semantics_type)
evaluation_result = llm_eval.evaluate_from_list(results=predictions, model_name=model_name)
print(evaluation_result)
```
### Expected Output
```python
{
'accuracy': 1,
'malformed-count': 0,
}
```
## Benchmark
You can load the dataset using the `datasets` library. Here is an example:
```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 Split
<table>
<tr>
<th>Task</th>
<th>Split</th>
<th>Description</th>
</tr>
<tr>
<td rowspan="5">✨ <strong>PredState</strong><br>(Final State Prediction)</td>
<td> predstate-IMP-nk-{dataset-name} </td>
<td> No semantics </td>
</tr>
<tr>
<td> predstate-IMP-K-uk-{dataset-name} </td>
<td>Standard semantics with K-semantics formalization</td>
</tr>
<tr>
<td> predstate-IMP-K-mk-{dataset-name} </td>
<td>Nonstandard semantics with K-semantics formalization</td>
</tr>
<tr>
<td> predstate-IMP-SOS-uk-{dataset-name} </td>
<td>Standard semantics with SOS formalization</td>
</tr>
<tr>
<td> predstate-IMP-SOS-mk-{dataset-name} </td>
<td>Nonstandard semantics with SOS formalization</td>
</tr>
<tr>
<td rowspan="4">✨ <strong>PredRule</strong><br>(Semantic Rule Prediction)</td>
<td> predrule-IMP-K-uk-human-written </td>
<td>Standard semantics with K-semantics formalization</td>
</tr>
<tr>
<td> predrule-IMP-K-mk-human-written </td>
<td>Nonstandard semantics with K-semantics formalization</td>
</tr>
<tr>
<td> predrule-IMP-SOS-uk-human-written </td>
<td>Standard semantics with SOS formalization</td>
</tr>
<tr>
<td> predrule-IMP-SOS-mk-human-written </td>
<td>Nonstandard semantics with SOS formalization</td>
</tr>
<tr>
<td rowspan="4">✨ <strong>PredTrace</strong><br>(Execution Trace Prediction)</td>
<td> predtrace-IMP-K-uk-human-written </td>
<td>Standard semantics with K-semantics formalization</td>
</tr>
<tr>
<td> predtrace-IMP-K-mk-human-written </td>
<td>Nonstandard semantics with K-semantics formalization</td>
</tr>
<tr>
<td> predtrace-IMP-SOS-uk-human-written </td>
<td>Standard semantics with SOS formalization</td>
</tr>
<tr>
<td> predtrace-IMP-SOS-mk-human-written </td>
<td>Nonstandard semantics with SOS formalization</td>
</tr>
</table>
### Data Example
One example of the dataset 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
```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},
}
```
## License
This project is licensed under the [CC BY 4.0 License](https://creativecommons.org/licenses/by/4.0/).