Papers by David Esiobu
Lost in Inference: Rediscovering the Role of Natural Language Inference for Large Language Models (2025.naacl-long)
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| Challenge: | In the recent past, a popular way of evaluating natural language understanding was to consider a model’s ability to perform natural language inference (NLI) tasks. |
| Approach: | They focus on five different NLI benchmarks across six models of different scales and examine how their accuracies develop during training. |
| Outcome: | The softmax distributions of models align with human label distributions in cases where statements are ambiguous or vague. |
ROBBIE: Robust Bias Evaluation of Large Generative Language Models (2023.emnlp-main)
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David Esiobu, Xiaoqing Tan, Saghar Hosseini, Megan Ung, Yuchen Zhang, Jude Fernandes, Jane Dwivedi-Yu, Eleonora Presani, Adina Williams, Eric Smith
| Challenge: | generative large language models (LLMs) are becoming more performant and prevalent . we need tools to measure and improve their fairness, authors say . |
| Approach: | They propose to compare 6 different prompt-based bias and toxicity metrics across 12 demographic axes and 5 families of generative large language models. |
| Outcome: | The proposed model can be tested on more datasets to better characterize and mitigate biases . the study compared 6 prompt-based bias and toxicity metrics across 12 demographic axes and 5 families of generative large language models. |