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cs.CL2024
Ranking Unraveled: Recipes for LLM Rankings in Head-to-Head AI Combat
Roland Daynauth, Christopher Clarke, Krisztian Flautner +2
Deciding which large language model (LLM) to use is a complex challenge. Pairwise ranking has emerged as a new method for evaluating human preferences for LLMs. This approach entai…
cs.CL2024
Aligning Model Evaluations with Human Preferences: Mitigating Token Count Bias in Language Model Assessments
Roland Daynauth, Jason Mars
The SLAM paper demonstrated that on-device Small Language Models (SLMs) are a viable and cost-effective alternative to API-based Large Language Models (LLMs), such as OpenAI's GPT-…
cs.CL2024
Guylingo: The Republic of Guyana Creole Corpora
Christopher Clarke, Roland Daynauth, Charlene Wilkinson +2
While major languages often enjoy substantial attention and resources, the linguistic diversity across the globe encompasses a multitude of smaller, indigenous, and regional langua…