Schroedinger's Threshold: When the AUC doesn't predict Accuracy
Not on the map. Why?
Abstract (arXiv)
The Area Under Curve measure (AUC) seems apt to evaluate and compare diverse models, possibly without calibration. An important example of AUC application is the evaluation and benchmarking of models that predict faithfulness of generated text. But we show that the AUC yields an academic and optimistic notion of accuracy that can misalign with the actual accuracy observed in application, yielding significant changes in benchmark rankings. To paint a more realistic picture of downstream model performance (and prepare a model for actual application), we explore different calibration modes, testing calibration data and method.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 21 of its 32 references that are in the map. First posted on arXiv 4 Apr 2024.
Builds on
21 of 32 references.
References · 21
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
- 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
- 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,157
- 2020BLEURT: Learning Robust Metrics for Text Generationcited by 1,196
- 2020On Faithfulness and Factuality in Abstractive Summarizationcited by 895
- 2019Adversarial NLI: A New Benchmark for Natural Language Understandingcited by 798
- 2021BARTScore: Evaluating Generated Text as Text Generationcited by 719
- 2020SummEval: Re-evaluating Summarization Evaluationcited by 666
- 2019Evaluating the Factual Consistency of Abstractive Text Summarizationcited by 578
- 2021SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarizationcited by 402
- 2019PAWS: Paraphrase Adversaries from Word Scramblingcited by 395
9 more
- 2020Asking and Answering Questions to Evaluate the Factual Consistency of Summariescited by 376
- 2021Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metricscited by 285
- 2023AlignScore: Evaluating Factual Consistency with a Unified Alignment Functioncited by 284
- 2021Q2: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answeringcited by 131
- 2021Evaluating Attribution in Dialogue Systems: The BEGIN Benchmarkcited by 75
- 2021Classifier Calibration: A survey on how to assess and improve predicted class probabilitiescited by 71
- 2021DialFact: A Benchmark for Fact-Checking in Dialoguecited by 62
- 2024Fine-Grained Natural Language Inference Based Faithfulness Evaluation for Diverse Summarisation Taskscited by 23
- 2023With a Little Push, NLI Models can Robustly and Efficiently Predict Faithfulnesscited by 13
Built on by
Citing papers per year: 2024: 1; 2025: 1; 2026: 2.
4 papers.
- 2025Verify with Caution: The Pitfalls of Relying on Imperfect Factuality Metricscited by 8
- 2026Closing the Operational Gap in Semantic Cachingcited by 1
- 2024How to Choose a Threshold for an Evaluation Metric for Large Language Modelscited by 0
- 2026PragMatch: Separating Pragmatic Incongruity from Cross-Modal Mismatch in Large Vision-Language Modelscited by 0
- 2026PragMatch: Separating Pragmatic Incongruity from Cross-Modal Mismatch in Large Vision-Language Models10 Aug 2026
- 2026Closing the Operational Gap in Semantic Caching18 Jun 2026
- 2025Verify with Caution: The Pitfalls of Relying on Imperfect Factuality Metrics24 Jan 2025
- 2024How to Choose a Threshold for an Evaluation Metric for Large Language Models10 Dec 2024