Stanford XCS224U: NLU I Behavioral Evaluation of NLU Models, Pt 5: Adversarial Testing I Spring 2023

TL;DR

Recent cases of adversarial tests in NLU reveal weaknesses and progress in language understanding models.

Transcript

hello everyone welcome back this is the fifth screencast in our series on Advanced behavioral testing for nlu what we've done so far in the unit is reflect on the nature of Behavioral testing and think about its motivations and we've tried to come to grips with its strengths and its weaknesses with that context in place I thought it would be good t... Read More

Key Insights

• ⁉️ Language understanding models have surpassed human accuracy in answering questions, as demonstrated by the Squad leaderboard.
• 🏆 Adversarial tests expose potential overfitting and weaknesses in models, such as susceptibility to misleading or redundant sentences.
• ❓ Superhuman performance in NLI benchmarks may not indicate human-like behavior, as models often fail to understand systematicity and language nuances.
• 🏆 Newer Transformer models show promising results in overcoming adversarial tests, indicating progress in language understanding.
• 😫 Adversarial benchmarks, like the Nike benchmark, provide insights into model weaknesses, data set artifacts, and the importance of understanding compositionality and systematicity in language.

Questions & Answers

Q: How did Gia and Liang's squad test reveal potential overfitting in language understanding models?

Gia and Liang appended misleading sentences at the end of context passages and found that models started to answer based on the appended false evidence. Retraining the models on the augmented training set helped them overcome this adversary, but when the misleading sentences were prepended or inserted in the middle, models still struggled.

Q: What insights does the Adversarial Testing in NLI by Glockner provide?

Glockner's adversarial testing in NLI showed that systems overfit to assuming that negation indicates contradiction. By changing words to synonyms or related terms, the models often switched from "entailment" to "contradiction." This highlights the models' failure to understand systematicity and the nuances of language.

Q: How did newer Transformer models perform in overcoming adversarial tests?

A fine-tuned Roberta model, trained on Multi-NLI, achieved high F1 scores and solved the adversarial tests proposed by Glockner, even when tested on examples from SNLI. This demonstrates the progress made by newer Transformer models in overcoming adversarial challenges.

Q: What lessons can be learned from the Nike adversarial benchmark in NLI?

The Nike benchmark shows that models perform well on general NLI tasks but struggle with adversarial examples. Different categories in the benchmark, such as antonyms, numerical reasoning, and confusing elements, reveal weaknesses and artifacts in the models' performance.

Summary & Key Takeaways

• Squad leaderboard shows that computers have surpassed humans in answering questions, highlighting the progress in language understanding.

• Adversarial testing in NLU, such as the Gia and Liang's squad test, reveals that models are susceptible to being misled by appended and prepended sentences, indicating potential overfitting.

• Natural Language Inference (NLI) benchmarks, like SNLI and Multi-NLI, show that published papers have achieved superhuman performance, but adversarial tests by Glockner and Nike demonstrate issues with systematicity and compositionality, as well as data set weaknesses.