GPT-HateCheck: Can LLMs Write Better Functional Tests for Hate Speech Detection?

• URL: http://arxiv.org/abs/2402.15238v2
• Date: Mon, 27 May 2024 13:14:12 GMT
• Title: GPT-HateCheck: Can LLMs Write Better Functional Tests for Hate Speech Detection?
• Authors: Yiping Jin, Leo Wanner, Alexander Shvets,
• Abstract summary: HateCheck is a suite for testing fine-grained model functionalities on synthesized data. We propose GPT-HateCheck, a framework to generate more diverse and realistic functional tests from scratch. Crowd-sourced annotation demonstrates that the generated test cases are of high quality.
• Score: 50.53312866647302
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Online hate detection suffers from biases incurred in data sampling, annotation, and model pre-training. Therefore, measuring the averaged performance over all examples in held-out test data is inadequate. Instead, we must identify specific model weaknesses and be informed when it is more likely to fail. A recent proposal in this direction is HateCheck, a suite for testing fine-grained model functionalities on synthesized data generated using templates of the kind "You are just a [slur] to me." However, despite enabling more detailed diagnostic insights, the HateCheck test cases are often generic and have simplistic sentence structures that do not match the real-world data. To address this limitation, we propose GPT-HateCheck, a framework to generate more diverse and realistic functional tests from scratch by instructing large language models (LLMs). We employ an additional natural language inference (NLI) model to verify the generations. Crowd-sourced annotation demonstrates that the generated test cases are of high quality. Using the new functional tests, we can uncover model weaknesses that would be overlooked using the original HateCheck dataset.

Related papers

• Automatic Generation of Test Cases based on Bug Reports: a Feasibility Study with Large Language Models [4.318319522015101]
  Existing approaches produce test cases that either can be qualified as simple (e.g. unit tests) or that require precise specifications. Most testing procedures still rely on test cases written by humans to form test suites. We investigate the feasibility of performing this generation by leveraging large language models (LLMs) and using bug reports as inputs.
  arXiv  Detail & Related papers  (2023-10-10T05:30:12Z)
• DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature [143.5381108333212]
  We show that text sampled from an large language model tends to occupy negative curvature regions of the model's log probability function. We then define a new curvature-based criterion for judging if a passage is generated from a given LLM. We find DetectGPT is more discriminative than existing zero-shot methods for model sample detection.
  arXiv  Detail & Related papers  (2023-01-26T18:44:06Z)
• On the Blind Spots of Model-Based Evaluation Metrics for Text Generation [79.01422521024834]
  We explore a useful but often neglected methodology for robustness analysis of text generation evaluation metrics. We design and synthesize a wide range of potential errors and check whether they result in a commensurate drop in the metric scores. Our experiments reveal interesting insensitivities, biases, or even loopholes in existing metrics.
  arXiv  Detail & Related papers  (2022-12-20T06:24:25Z)
• HateCheckHIn: Evaluating Hindi Hate Speech Detection Models [6.52974752091861]
  multilingual hate is a major emerging challenge for automated detection. We introduce a set of functionalities for the purpose of evaluation. Considering Hindi as a base language, we craft test cases for each functionality.
  arXiv  Detail & Related papers  (2022-04-30T19:09:09Z)
• NLI Data Sanity Check: Assessing the Effect of Data Corruption on Model Performance [3.7024660695776066]
  We propose a new diagnostics test suite which allows to assess whether a dataset constitutes a good testbed for evaluating the models' meaning understanding capabilities. We specifically apply controlled corruption transformations to widely used benchmarks (MNLI and ANLI) A large decrease in model accuracy indicates that the original dataset provides a proper challenge to the models' reasoning capabilities.
  arXiv  Detail & Related papers  (2021-04-10T12:28:07Z)
• HateCheck: Functional Tests for Hate Speech Detection Models [3.4938484663205776]
  We introduce HateCheck, a first suite of functional tests for hate speech detection models. We specify 29 model functionalities, the selection of which we motivate by reviewing previous research. We test near-state-of-the-art transformer detection models as well as a popular commercial model, revealing critical model weaknesses.
  arXiv  Detail & Related papers  (2020-12-31T13:44:56Z)
• Debugging Tests for Model Explanations [18.073554618753395]
  Methods tested are able to diagnose a spurious background bug, but not conclusively identify mislabeled training examples. We complement our analysis with a human subject study, and find that subjects fail to identify defective models using attributions, but instead rely, primarily, on model predictions.
  arXiv  Detail & Related papers  (2020-11-10T22:23:25Z)
• Understanding Classifier Mistakes with Generative Models [88.20470690631372]
  Deep neural networks are effective on supervised learning tasks, but have been shown to be brittle. In this paper, we leverage generative models to identify and characterize instances where classifiers fail to generalize. Our approach is agnostic to class labels from the training set which makes it applicable to models trained in a semi-supervised way.
  arXiv  Detail & Related papers  (2020-10-05T22:13:21Z)
• Beyond Accuracy: Behavioral Testing of NLP models with CheckList [66.42971817954806]
  CheckList is a task-agnostic methodology for testing NLP models. CheckList includes a matrix of general linguistic capabilities and test types that facilitate comprehensive test ideation. In a user study, NLP practitioners with CheckList created twice as many tests, and found almost three times as many bugs as users without it.
  arXiv  Detail & Related papers  (2020-05-08T15:48:31Z)
• Evaluating Models' Local Decision Boundaries via Contrast Sets [119.38387782979474]
  We propose a new annotation paradigm for NLP that helps to close systematic gaps in the test data. We demonstrate the efficacy of contrast sets by creating them for 10 diverse NLP datasets. Although our contrast sets are not explicitly adversarial, model performance is significantly lower on them than on the original test sets.
  arXiv  Detail & Related papers  (2020-04-06T14:47:18Z)

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.