Papers with naturalistic tasks
Posing Fair Generalization Tasks for Natural Language Inference (D19-1)
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| Challenge: | Existing evaluation methods for deep learning semantics rely on naturalistic corpora, but they often fail to support the kind of generalization we are asking for. |
| Approach: | They define and motivate a formal notion of fairness for evaluations of deep learning models for semantics . they then apply it to natural language inference by constructing challenging but provably fair artificial datasets based on the results . |
| Outcome: | The proposed evaluations show that standard neural models fail to generalize in the required ways and even these models do not solve the task perfectly. |
A Scalable Entity-Based Framework for Auditing Bias in Large Language Models (2026.findings-acl)
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| Challenge: | Existing approaches to bias evaluation in large language models trade ecological validity for statistical control, or use artificial prompts that lack scale and rigor. |
| Approach: | They propose a framework that uses named entities as probes to measure bias in large language models. |
| Outcome: | The proposed framework reproduces bias patterns observed in natural text, enabling large-scale analysis. |