Beyond Accuracy: Behavioral Testing of NLP Models with CheckList (2020.acl-main)
Copied to clipboard
| Challenge: | In a recent study, we show that holding-out data can overestimate performance of NLP models. |
| Approach: | They propose a task-agnostic methodology for testing NLP models using a matrix of general linguistic capabilities and test types that facilitate comprehensive test ideation. |
| Outcome: | The proposed method identifies critical failures in commercial and state-of-the-art models. |
Similar Papers
SynthEval: Hybrid Behavioral Testing of NLP Models with Synthetic Evaluation (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing frameworks for benchmarking in NLP often overestimate performance . however, manually creating a variety of test types requires significant human labor . |
| Approach: | They propose a framework that leverages large language models to generate a wide range of test types . they first generate sentences via LLMs and then identifies challenging examples . |
| Outcome: | The proposed framework overestimates performance on two classification tasks. |
Adaptive Testing and Debugging of NLP Models (2022.acl-long)
Copied to clipboard
| Challenge: | Current approaches to testing and debugging NLP models rely on variable human creativity and extensive labor to instantiate bugs. |
| Approach: | They propose a process which uses large scale language models to automatically write unit tests highlighting bugs in a target model. |
| Outcome: | The proposed process makes users 5-10x more effective at finding bugs than current approaches. |
TestAug: A Framework for Augmenting Capability-based NLP Tests (2022.coling-1)
Copied to clipboard
| Challenge: | Existing work on capability-based testing requires the developer to compose each individual test template from scratch. |
| Approach: | They propose a capability-based NLP testing framework that requires the developer to only annotate a few test templates while leveraging the GPT-3 engine to generate the majority of test cases. |
| Outcome: | The proposed framework saves the developer's manual efforts and guarantees the correctness of the generated suites with a validity checker. |
Whispers of Doubt Amidst Echoes of Triumph in NLP Robustness (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing approaches to measure robustness are problematic, and out-of-domain evaluations are no longer relevant. |
| Approach: | They examine models of different sizes spanning different architectural choices and pretraining objectives. |
| Outcome: | The results show that not all out-of-domain tests provide insight into robustness . merely scaling models does not make them adequately robust . |
In Benchmarks We Trust ... Or Not? (2025.emnlp-main)
Copied to clipboard
Ine Gevers, Victor De Marez, Jens Van Nooten, Jens Lemmens, Andriy Kosar, Ehsan Lotfi, Nikolay Banar, Pieter Fivez, Luna De Bruyne, Walter Daelemans
| Challenge: | Existing benchmarks for Large Language Models (LLMs) are inadequate and lack a clear solution. |
| Approach: | They propose checklists to cover all aspects of benchmarking issues, both for benchmark creation and usage. |
| Outcome: | The proposed checklists cover all aspects of benchmarking issues, both for benchmark creation and usage. |
White-box Testing of NLP models with Mask Neuron Coverage (2022.findings-naacl)
Copied to clipboard
| Challenge: | Recent research has shown that black-box testing is not applicable to NLP models. |
| Approach: | They propose a set of white-box testing methods that are customized for transformer-based NLP models and adapt them to a black-box test suite. |
| Outcome: | The proposed methods can reduce testing suites by 60% while retaining failing tests, thereby concentrating faultdetection power of the test suite. |
Are Checklists Really Useful for Automatic Evaluation of Generative Tasks? (2025.emnlp-main)
Copied to clipboard
| Challenge: | evaluators using large language models face ambiguous criteria and inconsistent evaluations. |
| Approach: | They investigate whether checklists should be used for all questions or selectively . they generate checklists using six methods and evaluate their effectiveness across eight models . |
| Outcome: | The proposed method improves evaluation performance in pairwise comparisons while ignoring human-written criteria. |
Empowering Cross-lingual Behavioral Testing of NLP Models with Typological Features (2023.acl-long)
Copied to clipboard
| Challenge: | Existing datasets do not allow for a fine-grained cross-lingual evaluation and mainly permit comparisons on a language level. |
| Approach: | They propose a morphologically-aware framework for behavioral testing of NLP models that generates tests in light of specific linguistic features in 12 typologically diverse languages. |
| Outcome: | The proposed framework evaluates state-of-the-art language models on the generated tests. |
Predicting Performance for Natural Language Processing Tasks (2020.acl-main)
Copied to clipboard
| Challenge: | Natural language processing (NLP) is a vast field, with a wide variety of tasks, languages, and domains. |
| Approach: | They build regression models to predict evaluation score of an NLP experiment . they find that their models can produce meaningful predictions over unseen languages . |
| Outcome: | The proposed model outperforms baseline models and human experts on 9 different tasks. |
Reliability Testing for Natural Language Processing Systems (2021.acl-long)
Copied to clipboard
| Challenge: | a lack of rigorous testing and ML implicit assumption of identical training and testing distributions may result in systems that discriminate against minorities. |
| Approach: | They argue that reliability testing is needed to address the issue of demographics . they argue that adversarial attacks can be reframed for this goal . |
| Outcome: | The proposed framework will enable rigorous and targeted testing and aid in the enactment and enforcement of industry standards. |