Papers by Carlos Guestrin
Benchmarking Distributional Alignment of Large Language Models (2025.naacl-long)
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| Challenge: | Language models are increasingly being used as simulacra for people, yet their ability to match the distribution of views of a specific demographic group remains uncertain. |
| Approach: | They construct a dataset expanding beyond political values and create human baselines for this task and evaluate the extent to which an LM can align with a particular group’s opinion distribution. |
| Outcome: | The proposed model can better describe opinion distributions than simulate demographic groups. |
Semantically Equivalent Adversarial Rules for Debugging NLP models (P18-1)
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| Challenge: | Complex machine learning models are often brittle, making different predictions for input instances that are extremely similar semantically. |
| Approach: | They propose to generalize semantically equivalent adversarial rules that induce adversaries on many instances to detect brittle models. |
| Outcome: | The proposed rules generate high-quality local adversaries for more instances than humans and induce four times as many mistakes as human experts. |
Are Red Roses Red? Evaluating Consistency of Question-Answering Models (P19-1)
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| Challenge: | Existing question-answering systems are limited in their ability to test reasoning and comprehension. |
| Approach: | They propose a method to automatically extract implications from QA datasets to evaluate models' consistency . they use a heuristic to generate such questions and retrain models with implication-augmented data . |
| Outcome: | The proposed method shows that generated implications are well formed and valid . retraining with implication-augmented data improves consistency on both synthetic and human-generated implications. |
Beyond Accuracy: Behavioral Testing of NLP Models with CheckList (2020.acl-main)
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| 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. |