# Evaluating Large Language Models with Human Feedback: Establishing a Swedish Benchmark

arXiv: https://arxiv.org/abs/2405.14006 · HTML: https://mlsanity.com/paper/2405.14006/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Human values and AI feedback alignment, one of 10 directions in LLM alignment / RLHF / preference optimisation.
- Builds on 2 of its 3 references that are in the map.
- First posted on arXiv 22 May 2024.

## Placed in

- LLM alignment / RLHF / preference optimisation / [Human values and AI feedback alignment](https://mlsanity.com/alignment/human-values-and-ai-feedback-alignment/)

## Builds on (most cited first, 2 of 2)

- [TIES-Merging: Resolving Interference When Merging Models](https://mlsanity.com/paper/2306.01708/) (2023)
- [ScandEval: A Benchmark for Scandinavian Natural Language Processing](https://mlsanity.com/paper/2304.00906/) (2023)

## Built on by (most cited first, 1 of 1)

- [UPME: An Unsupervised Peer Review Framework for Multimodal Large Language Model Evaluation](https://mlsanity.com/paper/2503.14941/) (2025)

## Abstract (arXiv)

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.