Evaluating Large Language Models with Human Feedback: Establishing a Swedish Benchmark
This work addresses the problem of evaluating LLMs in low-resource languages like Swedish for researchers and developers, but it is incremental as it adapts an existing benchmark.
The study tackled the under-explored performance of large language models in Swedish by introducing a human benchmark to assess eleven models, including GPT-4 and Claude, using forced choice ranking, with results indicating varied efficacy but no specific numerical gains reported.
In the rapidly evolving field of artificial intelligence, large language models (LLMs) have demonstrated significant capabilities across numerous applications. However, the performance of these models in languages with fewer resources, such as Swedish, remains under-explored. This study introduces a comprehensive human benchmark to assess the efficacy of prominent LLMs in understanding and generating Swedish language texts using forced choice ranking. We employ a modified version of the ChatbotArena benchmark, incorporating human feedback to evaluate eleven different models, including GPT-4, GPT-3.5, various Claude and Llama models, and bespoke models like Dolphin-2.9-llama3b-8b-flashback and BeagleCatMunin. These models were chosen based on their performance on LMSYS chatbot arena and the Scandeval benchmarks. We release the chatbotarena.se benchmark as a tool to improve our understanding of language model performance in Swedish with the hopes that it will be widely used. We aim to create a leaderboard once sufficient data has been collected and analysed.