Improving the Validity and Practical Usefulness of AI/ML Evaluations Using an Estimands Framework

• URL: http://arxiv.org/abs/2406.10366v1
• Date: Fri, 14 Jun 2024 18:47:37 GMT
• Title: Improving the Validity and Practical Usefulness of AI/ML Evaluations Using an Estimands Framework
• Authors: Olivier Binette, Jerome P. Reiter,
• Abstract summary: We propose an estimands framework adapted from international clinical trials guidelines. This framework provides a systematic structure for inference and reporting in evaluations. We demonstrate how the framework can help uncover underlying issues, their causes, and potential solutions.
• Score: 2.4861619769660637
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Commonly, AI or machine learning (ML) models are evaluated on benchmark datasets. This practice supports innovative methodological research, but benchmark performance can be poorly correlated with performance in real-world applications -- a construct validity issue. To improve the validity and practical usefulness of evaluations, we propose using an estimands framework adapted from international clinical trials guidelines. This framework provides a systematic structure for inference and reporting in evaluations, emphasizing the importance of a well-defined estimation target. We illustrate our proposal on examples of commonly used evaluation methodologies - involving cross-validation, clustering evaluation, and LLM benchmarking - that can lead to incorrect rankings of competing models (rank reversals) with high probability, even when performance differences are large. We demonstrate how the estimands framework can help uncover underlying issues, their causes, and potential solutions. Ultimately, we believe this framework can improve the validity of evaluations through better-aligned inference, and help decision-makers and model users interpret reported results more effectively.

Related papers

• Unveiling Context-Aware Criteria in Self-Assessing LLMs [28.156979106994537]
  We propose a novel Self-Assessing LLM framework that integrates Context-Aware Criteria (SALC) with dynamic knowledge tailored to each evaluation instance. Empirical evaluations demonstrate that our approach significantly outperforms existing baseline evaluation frameworks. Our method also exhibits a improvement in LC Win-Rate in AlpacaEval2 leaderboard up to a 12% when employed for preference data generation.
  arXiv  Detail & Related papers  (2024-10-28T21:18:49Z)
• OCDB: Revisiting Causal Discovery with a Comprehensive Benchmark and Evaluation Framework [21.87740178652843]
  Causal discovery offers a promising approach to improve transparency and reliability. We propose a flexible evaluation framework with metrics for evaluating differences in causal structures and causal effects. We introduce the Open Causal Discovery Benchmark (OCDB), based on real data, to promote fair comparisons and drive optimization of algorithms.
  arXiv  Detail & Related papers  (2024-06-07T03:09:22Z)
• FreeEval: A Modular Framework for Trustworthy and Efficient Evaluation of Large Language Models [36.273451767886726]
  FreeEval is a modular and scalable framework crafted to enable trustworthy and efficient automatic evaluations of large language models. FreeEval's unified abstractions simplify the integration and improve the transparency of diverse evaluation methodologies. The framework integrates meta-evaluation techniques like human evaluation and data contamination detection, which, along with dynamic evaluation modules, enhance the fairness of the evaluation outcomes.
  arXiv  Detail & Related papers  (2024-04-09T04:17:51Z)
• CheckEval: Robust Evaluation Framework using Large Language Model via Checklist [6.713203569074019]
  We introduce CheckEval, a novel evaluation framework using Large Language Models. CheckEval addresses the challenges of ambiguity and inconsistency in current evaluation methods.
  arXiv  Detail & Related papers  (2024-03-27T17:20:39Z)
• Don't Make Your LLM an Evaluation Benchmark Cheater [142.24553056600627]
  Large language models(LLMs) have greatly advanced the frontiers of artificial intelligence, attaining remarkable improvement in model capacity. To assess the model performance, a typical approach is to construct evaluation benchmarks for measuring the ability level of LLMs. We discuss the potential risk and impact of inappropriately using evaluation benchmarks and misleadingly interpreting the evaluation results.
  arXiv  Detail & Related papers  (2023-11-03T14:59:54Z)
• Towards Evaluating Transfer-based Attacks Systematically, Practically, and Fairly [79.07074710460012]
  adversarial vulnerability of deep neural networks (DNNs) has drawn great attention. An increasing number of transfer-based methods have been developed to fool black-box DNN models. We establish a transfer-based attack benchmark (TA-Bench) which implements 30+ methods.
  arXiv  Detail & Related papers  (2023-11-02T15:35:58Z)
• FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets [69.91340332545094]
  We introduce FLASK, a fine-grained evaluation protocol for both human-based and model-based evaluation. We experimentally observe that the fine-graininess of evaluation is crucial for attaining a holistic view of model performance.
  arXiv  Detail & Related papers  (2023-07-20T14:56:35Z)
• From Static Benchmarks to Adaptive Testing: Psychometrics in AI Evaluation [60.14902811624433]
  We discuss a paradigm shift from static evaluation methods to adaptive testing. This involves estimating the characteristics and value of each test item in the benchmark and dynamically adjusting items in real-time. We analyze the current approaches, advantages, and underlying reasons for adopting psychometrics in AI evaluation.
  arXiv  Detail & Related papers  (2023-06-18T09:54:33Z)
• Advancing Counterfactual Inference through Nonlinear Quantile Regression [77.28323341329461]
  We propose a framework for efficient and effective counterfactual inference implemented with neural networks. The proposed approach enhances the capacity to generalize estimated counterfactual outcomes to unseen data. Empirical results conducted on multiple datasets offer compelling support for our theoretical assertions.
  arXiv  Detail & Related papers  (2023-06-09T08:30:51Z)
• Exploring validation metrics for offline model-based optimisation with diffusion models [50.404829846182764]
  In model-based optimisation (MBO) we are interested in using machine learning to design candidates that maximise some measure of reward with respect to a black box function called the (ground truth) oracle. While an approximation to the ground oracle can be trained and used in place of it during model validation to measure the mean reward over generated candidates, the evaluation is approximate and vulnerable to adversarial examples. This is encapsulated under our proposed evaluation framework which is also designed to measure extrapolation.
  arXiv  Detail & Related papers  (2022-11-19T16:57:37Z)

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.