Cover Image

ConStat: Performance-Based Contamination Detection in Large Language Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Dekoninck, Jasper, Müller, Mark Niklas, Vechev, Martin
Format:	Preprint
Published:	2024
Subjects:	Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Evading Data Contamination Detection for Language Models is (too) Easy
by: Dekoninck, Jasper, et al.
Published: (2024)

A Unified Approach to Routing and Cascading for LLMs
by: Dekoninck, Jasper, et al.
Published: (2024)

Learning from Saturated Data: Signals Beyond Correctness for LLM Training
by: Hiss, Hanno, et al.
Published: (2026)

Controlled Text Generation via Language Model Arithmetic
by: Dekoninck, Jasper, et al.
Published: (2023)

Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation
by: Dekoninck, Jasper, et al.
Published: (2024)

BrokenMath: A Benchmark for Sycophancy in Theorem Proving with LLMs
by: Petrov, Ivo, et al.
Published: (2025)

Not All Proofs Are Equal: Evaluating LLM Proof Quality Beyond Correctness
by: Petrov, Ivo, et al.
Published: (2026)

Constrained Decoding of Diffusion LLMs with Context-Free Grammars
by: Mündler, Niels, et al.
Published: (2025)

MathArena: Evaluating LLMs on Uncontaminated Math Competitions
by: Balunović, Mislav, et al.
Published: (2025)

Beyond Benchmarks: MathArena as an Evaluation Platform for Mathematics with LLMs
by: Dekoninck, Jasper, et al.
Published: (2026)

Proof or Bluff? Evaluating LLMs on 2025 USA Math Olympiad
by: Petrov, Ivo, et al.
Published: (2025)

Large Language Models are Advanced Anonymizers
by: Staab, Robin, et al.
Published: (2024)

Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigation
by: Mündler, Niels, et al.
Published: (2023)

Adaptive Generation of Bias-Eliciting Questions for LLMs
by: Staab, Robin, et al.
Published: (2025)

StatEval: A Comprehensive Benchmark for Large Language Models in Statistics
by: Lu, Yuchen, et al.
Published: (2025)

A Comprehensive Survey of Contamination Detection Methods in Large Language Models
by: Ravaut, Mathieu, et al.
Published: (2024)

Investigating Data Contamination in Modern Benchmarks for Large Language Models
by: Deng, Chunyuan, et al.
Published: (2023)

Data Contamination Quiz: A Tool to Detect and Estimate Contamination in Large Language Models
by: Golchin, Shahriar, et al.
Published: (2023)

ConInstruct: Evaluating Large Language Models on Conflict Detection and Resolution in Instructions
by: He, Xingwei, et al.
Published: (2025)

A Taxonomy for Data Contamination in Large Language Models
by: Palavalli, Medha, et al.
Published: (2024)

A Survey on Data Contamination for Large Language Models
by: Cheng, Yuxing, et al.
Published: (2025)

DAGER: Exact Gradient Inversion for Large Language Models
by: Petrov, Ivo, et al.
Published: (2024)

Facts in Stats: Impacts of Pretraining Diversity on Language Model Generalization
by: Behnia, Tina, et al.
Published: (2025)

Large Language Models for Code: Security Hardening and Adversarial Testing
by: He, Jingxuan, et al.
Published: (2023)

Benchmark Data Contamination of Large Language Models: A Survey
by: Xu, Cheng, et al.
Published: (2024)

CLEAN-EVAL: Clean Evaluation on Contaminated Large Language Models
by: Zhu, Wenhong, et al.
Published: (2023)

Diagnosing Robotics Systems Issues with Large Language Models
by: Herrmann, Jordis Emilia, et al.
Published: (2024)

The Open Proof Corpus: A Large-Scale Study of LLM-Generated Mathematical Proofs
by: Dekoninck, Jasper, et al.
Published: (2025)

StatBot.Swiss: Bilingual Open Data Exploration in Natural Language
by: Nooralahzadeh, Farhad, et al.
Published: (2024)

Towards Data Contamination Detection for Modern Large Language Models: Limitations, Inconsistencies, and Oracle Challenges
by: Samuel, Vinay, et al.
Published: (2024)

ConExion: Concept Extraction with Large Language Models
by: Norouzi, Ebrahim, et al.
Published: (2025)

LNE-Blocking: An Efficient Framework for Contamination Mitigation Evaluation on Large Language Models
by: Hou, Ruijie, et al.
Published: (2025)

An Open Source Data Contamination Report for Large Language Models
by: Li, Yucheng, et al.
Published: (2023)

Detecting Data Contamination from Reinforcement Learning Post-training for Large Language Models
by: Tao, Yongding, et al.
Published: (2025)

PaCoST: Paired Confidence Significance Testing for Benchmark Contamination Detection in Large Language Models
by: Zhang, Huixuan, et al.
Published: (2024)

Automated Classification of Model Errors on ImageNet
by: Peychev, Momchil, et al.
Published: (2023)

A Synthetic Dataset for Personal Attribute Inference
by: Yukhymenko, Hanna, et al.
Published: (2024)

Unveiling the Spectrum of Data Contamination in Language Models: A Survey from Detection to Remediation
by: Deng, Chunyuan, et al.
Published: (2024)

ConKE: Conceptualization-Augmented Knowledge Editing in Large Language Models for Commonsense Reasoning
by: Zhang, Liyu, et al.
Published: (2024)

Investigating the Impact of Data Contamination of Large Language Models in Text-to-SQL Translation
by: Ranaldi, Federico, et al.
Published: (2024)