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David Esiobu

2 accepted papers

2025

Lost in Inference: Rediscovering the Role of Natural Language Inference for Large Language Models

NAACL 2025long

In the recent past, a popular way of evaluating natural language understanding (NLU), was to consider a model’s ability to perform natural language inference (NLI) tasks. In this paper, we investigate if NLI tasks, that are rarely used for LLM evaluation, can still be informative for evaluating LLMs…

Cited by 1SourcePDFScholar
2023

ROBBIE: Robust Bias Evaluation of Large Generative Language Models

EMNLP 2023long main

As generative large language models (LLMs) grow more performant and prevalent, we must develop comprehensive enough tools to measure and improve their fairness. Different prompt-based datasets can be used to measure social bias across multiple text domains and demographic axes, meaning that testing…

Cited by 0SourceScholar