CLEAN-EVAL: Clean Evaluation on Contaminated Large Language Models

• URL: http://arxiv.org/abs/2311.09154v3
• Date: Mon, 3 Jun 2024 03:06:55 GMT
• Title: CLEAN-EVAL: Clean Evaluation on Contaminated Large Language Models
• Authors: Wenhong Zhu, Hongkun Hao, Zhiwei He, Yunze Song, Yumeng Zhang, Hanxu Hu, Yiran Wei, Rui Wang, Hongyuan Lu,
• Abstract summary: Clean-Eval mitigates the issue of data contamination and evaluates the models in a cleaner manner. Clean-Eval employs an LLM to paraphrase and back-translate the contaminated data into a candidate set. A semantic detector is then used to filter the generated low-quality samples. The best candidate is finally selected from this set based on the BLEURT score.
• Score: 12.367149496971408
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We are currently in an era of fierce competition among various large language models (LLMs) continuously pushing the boundaries of benchmark performance. However, genuinely assessing the capabilities of these LLMs has become a challenging and critical issue due to potential data contamination, and it wastes dozens of time and effort for researchers and engineers to download and try those contaminated models. To save our precious time, we propose a novel and useful method, Clean-Eval, which mitigates the issue of data contamination and evaluates the LLMs in a cleaner manner. Clean-Eval employs an LLM to paraphrase and back-translate the contaminated data into a candidate set, generating expressions with the same meaning but in different surface forms. A semantic detector is then used to filter the generated low-quality samples to narrow down this candidate set. The best candidate is finally selected from this set based on the BLEURT score. According to human assessment, this best candidate is semantically similar to the original contamination data but expressed differently. All candidates can form a new benchmark to evaluate the model. Our experiments illustrate that Clean-Eval substantially restores the actual evaluation results on contaminated LLMs under both few-shot learning and fine-tuning scenarios.

Related papers

• LiveBench: A Challenging, Contamination-Free LLM Benchmark [101.21578097087699]
  We release LiveBench, the first benchmark that contains frequently-updated questions from recent information sources. We evaluate many prominent closed-source models, as well as dozens of open-source models ranging from 0.5B to 110B in size. Questions will be added and updated on a monthly basis, and we will release new tasks and harder versions of tasks over time.
  arXiv  Detail & Related papers  (2024-06-27T16:47:42Z)
• Data Contamination Can Cross Language Barriers [29.103517721155487]
  The opacity in developing large language models (LLMs) is raising growing concerns about the potential contamination of public benchmarks in the pre-training data. We first present a cross-lingual form of contamination that inflates LLMs' performance while evading current detection methods. We propose generalization-based approaches to unmask such deeply concealed contamination.
  arXiv  Detail & Related papers  (2024-06-19T05:53:27Z)
• Sample-Efficient Human Evaluation of Large Language Models via Maximum Discrepancy Competition [46.949604465227054]
  We propose a sample-efficient human evaluation method based on MAximum Discrepancy (MAD) competition. MAD automatically selects a small set of informative and diverse instructions, each adapted to two LLMs. The pairwise comparison results are then aggregated into a global ranking using the Elo rating system.
  arXiv  Detail & Related papers  (2024-04-10T01:26:24Z)
• How Much are Large Language Models Contaminated? A Comprehensive Survey and the LLMSanitize Library [68.10605098856087]
  Large Language Models (LLMs) are increasingly being used in business applications and fundraising in AI. LLMs' performance may not be reliable anymore, as the high performance may be at least partly due to their previous exposure to the data. We release an open-source Python library named LLMSanitize implementing major contamination detection algorithms.
  arXiv  Detail & Related papers  (2024-03-31T14:32:02Z)
• KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models [53.84677081899392]
  KIEval is a Knowledge-grounded Interactive Evaluation framework for large language models. It incorporates an LLM-powered "interactor" role for the first time to accomplish a dynamic contamination-resilient evaluation. Extensive experiments on seven leading LLMs across five datasets validate KIEval's effectiveness and generalization.
  arXiv  Detail & Related papers  (2024-02-23T01:30:39Z)
• Self-Evaluation Improves Selective Generation in Large Language Models [54.003992911447696]
  We reformulate open-ended generation tasks into token-level prediction tasks. We instruct an LLM to self-evaluate its answers. We benchmark a range of scoring methods based on self-evaluation.
  arXiv  Detail & Related papers  (2023-12-14T19:09:22Z)
• Investigating Data Contamination in Modern Benchmarks for Large Language Models [27.479260572913724]
  Recent observations have underscored a disparity between the inflated benchmark scores and the actual performance of LLMs. We study data contamination by proposing two methods tailored for both open-source and proprietary LLMs. We find that certain commercial LLMs could surprisingly guess the missing option in various test sets.
  arXiv  Detail & Related papers  (2023-11-16T11:03:04Z)
• Rethinking Benchmark and Contamination for Language Models with Rephrased Samples [49.18977581962162]
  Large language models are increasingly trained on all the data ever produced by humans. Many have raised concerns about the trustworthiness of public benchmarks due to potential contamination in pre-training or fine-tuning datasets.
  arXiv  Detail & Related papers  (2023-11-08T17:35:20Z)
• NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark [19.875954121100005]
  We argue that the classical evaluation on Natural Language Processing (NLP) tasks using annotated benchmarks is in trouble. The worst kind of data contamination happens when a Large Language Model (LLM) is trained on the test split of a benchmark, and then evaluated in the same benchmark. This position paper defines different levels of data contamination and argues for a community effort.
  arXiv  Detail & Related papers  (2023-10-27T09:48:29Z)
• ReEval: Automatic Hallucination Evaluation for Retrieval-Augmented Large Language Models via Transferable Adversarial Attacks [91.55895047448249]
  This paper presents ReEval, an LLM-based framework using prompt chaining to perturb the original evidence for generating new test cases. We implement ReEval using ChatGPT and evaluate the resulting variants of two popular open-domain QA datasets. Our generated data is human-readable and useful to trigger hallucination in large language models.
  arXiv  Detail & Related papers  (2023-10-19T06:37:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.