| --- |
| 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 |
| features: |
| - name: id |
| 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 |
| - name: mutated-program |
| dtype: string |
| - name: mutated |
| dtype: bool |
| - name: mutation-pattern |
| dtype: string |
| - name: K-evaluatable |
| dtype: bool |
| - name: ground-truth |
| dtype: string |
| - name: sampled-statements |
| list: |
| - name: line_number |
| dtype: int64 |
| - name: prior_state |
| struct: |
| - name: ble |
| dtype: int64 |
| - name: ans |
| dtype: int64 |
| - name: num |
| dtype: int64 |
| - name: a |
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| dtype: int64 |
| - name: rules |
| list: string |
| - name: cleaned_stmt |
| dtype: string |
| - name: control_stack |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 6777258 |
| num_examples: 324 |
| download_size: 208154 |
| dataset_size: 6777258 |
| - config_name: predrule-IMP-SOS-uk-human-written |
| features: |
| - name: id |
| 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 |
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| dtype: string |
| - name: mutated |
| dtype: bool |
| - name: mutation-pattern |
| dtype: string |
| - name: K-evaluatable |
| dtype: bool |
| - name: ground-truth |
| dtype: string |
| - name: sampled-statements |
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| dtype: int64 |
| - name: rules |
| list: string |
| - name: cleaned_stmt |
| dtype: string |
| - name: control_stack |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 3359261 |
| num_examples: 162 |
| download_size: 142836 |
| dataset_size: 3359261 |
| - config_name: predstate-IMP-K-mk-fuzzer-generated |
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| - name: mutation-pattern |
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| - name: K-evaluatable |
| dtype: bool |
| - name: ground-truth |
| dtype: string |
| splits: |
| - name: train |
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| - name: ground-truth |
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| - name: train |
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| - name: ground-truth |
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| splits: |
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| - name: ground-truth |
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| splits: |
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| num_examples: 165 |
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| - name: ground-truth |
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| - config_name: predstate-IMP-K-uk-llm-translated |
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| num_examples: 324 |
| download_size: 2093225 |
| dataset_size: 36617209 |
| - config_name: predtrace-IMP-SOS-uk-human-written |
| features: |
| - name: id |
| 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 |
| - name: mutated-program |
| dtype: string |
| - name: mutated |
| dtype: bool |
| - name: mutation-pattern |
| dtype: string |
| - name: K-evaluatable |
| dtype: bool |
| - name: ground-truth |
| dtype: string |
| - name: max-loop-depth |
| dtype: int64 |
| - name: max-if-depth |
| dtype: int64 |
| splits: |
| - name: train |
| num_bytes: 18280353 |
| num_examples: 162 |
| download_size: 1061112 |
| dataset_size: 18280353 |
| configs: |
| - 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: 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}, |
| } |
| ``` |
