Papers by Joshua Tint

3 papers
NLP Needs Diversity outside of ‘Diversity’ (2025.findings-emnlp)

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Challenge: a new position paper argues that diversity in NLP is concentrated on a small number of areas surrounding fairness .
Approach: a new position paper argues that diversity in NLP is disproportionately concentrated on fairness areas.
Outcome: a new position paper argues that diversity in NLP is disproportionately concentrated on fairness areas.
PROOD: A Simple LLM Out-of-Distribution Guardrail Leveraging Response Semantics (2025.findings-emnlp)

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Challenge: Existing OOD methods often struggle with deliberately obfuscated, context-dependent, or superficially benign prompts.
Approach: They propose a framework that jointly analyzes LLM prompts and their outputs to improve semantic understanding.
Outcome: The proposed framework outperforms existing OOD methods on three benchmarks and improves F1 scores by up to 6.3 points.
ExpressivityBench: Can LLMs Communicate Implicitly? (2026.findings-eacl)

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Challenge: a new study evaluates the expressivity of large language models for communicating implicitly . authors: models can express tone, identity, and intent beyond literal meanings . phrasing and tone of a message can convey a number of topics beyond literal contexts - authors .
Approach: They propose a framework to evaluate the expressivity of large language models . they use a social-linguistic grader to validate their models against human judgments .
Outcome: The proposed framework quantifies how well LLM-generated text communicates target properties without explicit mention across nine tasks spanning emotion, identity, and tone.

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