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40.8 kB
| license: cc-by-4.0 | |
| dataset_info: | |
| - config_name: predrule-IMP-K-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 | |
| dtype: int64 | |
| - name: b | |
| dtype: int64 | |
| - name: c | |
| dtype: int64 | |
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| dtype: int64 | |
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| dtype: int64 | |
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| dtype: int64 | |
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| dtype: int64 | |
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| dtype: int64 | |
| - name: n | |
| dtype: int64 | |
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| - name: r2y1 | |
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| - name: r2y2 | |
| dtype: int64 | |
| - name: rules | |
| list: string | |
| - name: cleaned_stmt | |
| dtype: string | |
| - name: control_stack | |
| dtype: string | |
| splits: | |
| - name: train | |
| num_bytes: 5013516 | |
| num_examples: 324 | |
| download_size: 187603 | |
| dataset_size: 5013516 | |
| - config_name: predrule-IMP-K-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: sampled-statements | |
| list: | |
| - name: line_number | |
| dtype: int64 | |
| - name: prior_state | |
| struct: | |
| - name: r1x1 | |
| dtype: int64 | |
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| dtype: int64 | |
| - name: ans | |
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| - name: rules | |
| list: string | |
| - name: cleaned_stmt | |
| dtype: string | |
| - name: control_stack | |
| dtype: string | |
| splits: | |
| - name: train | |
| num_bytes: 2478661 | |
| num_examples: 162 | |
| download_size: 129218 | |
| dataset_size: 2478661 | |
| - 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: | |
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| dtype: string | |
| - name: control_stack | |
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| splits: | |
| - name: train | |
| num_bytes: 6783414 | |
| num_examples: 324 | |
| download_size: 208241 | |
| dataset_size: 6783414 | |
| - 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 | |
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| - name: semantics | |
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| - name: language | |
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| - name: mutated-program | |
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| - name: mutated | |
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| - name: mutation-pattern | |
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| - name: K-evaluatable | |
| dtype: bool | |
| - name: ground-truth | |
| dtype: string | |
| - name: sampled-statements | |
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| splits: | |
| - name: train | |
| num_bytes: 3362339 | |
| num_examples: 162 | |
| download_size: 142922 | |
| dataset_size: 3362339 | |
| - config_name: predstate-IMP-K-mk-fuzzer-generated | |
| features: | |
| - name: id | |
| dtype: string | |
| - name: program | |
| dtype: string | |
| - name: src-filename | |
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| - name: semantics-type | |
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| - name: semantics | |
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| - name: mutated | |
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| - name: mutation-pattern | |
| dtype: string | |
| - name: K-evaluatable | |
| dtype: bool | |
| - name: ground-truth | |
| dtype: string | |
| splits: | |
| - name: train | |
| num_bytes: 26058090 | |
| num_examples: 330 | |
| download_size: 7001572 | |
| dataset_size: 26058090 | |
| - config_name: predstate-IMP-K-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 | |
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| - name: mutated | |
| dtype: bool | |
| - name: mutation-pattern | |
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| - name: K-evaluatable | |
| dtype: bool | |
| - name: ground-truth | |
| dtype: string | |
| splits: | |
| - name: train | |
| num_bytes: 3235751 | |
| num_examples: 324 | |
| download_size: 63156 | |
| dataset_size: 3235751 | |
| - config_name: predstate-IMP-K-mk-llm-translated | |
| features: | |
| - name: id | |
| dtype: string | |
| - name: program | |
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| - name: src-filename | |
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| - name: semantics | |
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| - name: language | |
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| - name: mutated-program | |
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| - name: mutated | |
| dtype: bool | |
| - name: mutation-pattern | |
| dtype: string | |
| - name: K-evaluatable | |
| dtype: bool | |
| - name: ground-truth | |
| dtype: string | |
| splits: | |
| - name: train | |
| num_bytes: 4354979 | |
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| - config_name: predrule-IMP-K-mk-human-written | |
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| - config_name: predrule-IMP-K-uk-human-written | |
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| path: predrule-IMP-SOS-mk-human-written/train-* | |
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| path: predrule-IMP-SOS-uk-human-written/train-* | |
| - config_name: predstate-IMP-K-mk-fuzzer-generated | |
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| path: predstate-IMP-K-mk-fuzzer-generated/train-* | |
| - config_name: predstate-IMP-K-mk-human-written | |
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| path: predstate-IMP-K-mk-human-written/train-* | |
| - config_name: predstate-IMP-K-mk-llm-translated | |
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| 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: | |
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| 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-* | |
| <div align="center"> | |
| <p>The 43rd International Conference on Machine Learning (ICML 2026), Seoul, South Korea</p> | |
| <h1> | |
| LLMs Lean on Priors, Not Programming Language | |
| <span style="white-space: nowrap;"> | |
| Semantics | |
| <img | |
| src="https://raw.githubusercontent.com/EngineeringSoftware/PLSemanticsBench/main/docs/logo.png" | |
| alt="PLSemanticsBench logo" | |
| width="60" | |
| style="display: inline-block !important; | |
| vertical-align: -1.0em; | |
| margin-left: 10px; | |
| margin-bottom: 0;"> | |
| </span> | |
| </h1> | |
| <p style="font-size: 20px;"> | |
| by | |
| <a href="https://www.adityathimmaiah.com">Aditya Thimmaiah</a><sup>1</sup>, | |
| <a href="https://jiyangzhang.github.io/">Jiyang Zhang</a><sup>1</sup>, | |
| <a href="https://scholar.google.com/citations?user=HtNfeKYAAAAJ&hl=en">Jayanth Srinivasa</a><sup>2</sup>, | |
| <a href="https://www.jessyli.com">Junyi Jessy Li</a><sup>1</sup>, | |
| <a href="https://users.ece.utexas.edu/~gligoric/">Milos Gligoric</a><sup>1</sup> | |
| </p> | |
| <p> | |
| <sup>1</sup>The University of Texas at Austin | |
| <sup>2</sup>Cisco Research | |
| </p> | |
| </div> | |
| <div align="center"> | |
| [](https://engineeringsoftware.github.io/PLSemanticsBench/) | |
| [](https://arxiv.org/pdf/2510.03415v3) | |
| [](https://github.com/EngineeringSoftware/PLSemanticsBench) | |
| [](https://huggingface.co/datasets/EngineeringSoftware/PLSemanticsBench) | |
| </div> | |
| --- | |
| **TLDR:** Frontier LLMs execute programs with up to 90–100% accuracy when symbols retain their usual meanings (e.g., + means addition). Under counterfactual semantic shifts (e.g., redefining + to mean subtraction) accuracy collapses by 40–70 percentage points. Despite handing the complete formal rules, the models keep answering as if the rules were never changed. | |
| LLMs don't faithfully interpret the semantics they are given—they retrieve what symbols usually mean from pretraining. | |
| ## Abstract | |
| Recent work asks whether large language models (LLMs) condition their reasoning on explicit rules rather than statistical regularities from pre-training. | |
| Program execution provides a canonical instance: formal semantics define behavior through symbolic transition rules that can be systematically altered | |
| under distribution shift. We investigate whether LLMs can condition their reasoning on formal semantics through program execution and introduce PLSEMANTICSBENCH, | |
| pairing featherweight C programs with two semantic systems—small-step operational semantics and K semantics—and probing four capabilities: composing rules for | |
| final states, selecting rules when state is unmutated, sustaining such conditioning over long traces, and following supplied rules under novel semantics. To | |
| decouple semantic reasoning from syntactic familiarity, we redefine familiar operators to induce symbol-meaning conflict and introduce novel symbols defined only | |
| through the supplied rules, and stress-test models on Human-Written, LLM-Translated, and Fuzzer-Generated splits with increasing structural complexity. Across | |
| 11 frontier LLMs, strong final-state accuracy under standard semantics (up to 90%) drops sharply—by as much as 40–60% points—under semantic mutations and increasing | |
| structural complexity. Only a handful of models achieve non-zero long-horizon conditioning accuracy, and even the best systems reach just 35%. Together, these results | |
| suggest that contemporary LLMs often rely on pretrained lexical associations rather than systematically conditioning on supplied formal rules. | |
| ## Table of Contents | |
| - [About](#about) | |
| - [Installation](#installation) | |
| - [Quick Start](#quick-start) | |
| - [Detailed Usage](#detailed-usage) | |
| - [Benchmark](#benchmark) | |
| - [Citation](#citation) | |
| ## About | |
| PLSemanticsBench is the first counterfactual PL semantics dataset for rule-conditioned reasoning of LLMs. 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 | |
| @inproceedings{ThimmaiahETAL25PLSemanticsBench, | |
| title = {LLMs Lean on Priors, Not Programming Language Semantics}, | |
| author = {Thimmaiah, Aditya and Zhang, Jiyang and Srinivasa, Jayanth | |
| and Li, Junyi Jessy and Gligoric, Milos}, | |
| booktitle = {ICML}, | |
| year = {2026} | |
| } | |
| ``` | |
| ## License | |
| This project is licensed under the [CC BY 4.0 License](https://creativecommons.org/licenses/by/4.0/). | |