Pith. sign in

REVIEW 1 cited by

The Heap: A Contamination-Free Multilingual Code Dataset for Evaluating Large Language Models

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2501.09653 v2 pith:WTU2NKKI submitted 2025-01-16 cs.CL cs.AI

classification cs.CLcs.AI
keywords largecodelanguagemodelsdatadatasetdatasetsheap
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

The recent rise in the popularity of large language models has spurred the development of extensive code datasets needed to train them. This has left limited code available for collection and use in the downstream investigation of specific behaviors, or evaluation of large language models without suffering from data contamination. To address this problem, we release The Heap, a large multilingual dataset covering 57 programming languages that has been deduplicated with respect to other open datasets of code, enabling researchers to conduct fair evaluations of large language models without significant data cleaning overhead.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. $\texttt{Droid}$: A Resource Suite for AI-Generated Code Detection

    cs.SE 2025-07 conditional novelty 6.0 of 10

    DroidCollection and DroidDetect provide the largest open resource for detecting AI-generated code, including adversarial 'humanized' samples, and show that training on a small amount of such data restores detector robustness.

Pith tools