Summary of Evaluating Large Language Models with Human Feedback: Establishing a Swedish Benchmark, by Birger Moell
Evaluating Large Language Models with Human Feedback: Establishing a Swedish Benchmark
by Birger Moell
First submitted to arxiv on: 22 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a new human benchmark to evaluate the performance of large language models (LLMs) in understanding and generating Swedish language texts. The study focuses on prominent LLMs, including GPT-4, GPT-3.5, Claude, Llama, Dolphin-2.9-llama3b-8b-flashback, and BeagleCatMunin, to assess their capabilities in languages with fewer resources. To evaluate these models, the authors employ a modified version of the ChatbotArena benchmark, incorporating human feedback through forced choice ranking. The study aims to improve our understanding of language model performance in Swedish by releasing this benchmark as a tool for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about testing how well computers can understand and create text in Swedish. Right now, we don’t know much about how these computer programs work in languages like Swedish that are used less often. The researchers created a new way to test these programs using human feedback. They chose some of the best computer language models and tested them on this benchmark. By doing this, they hope to help other people understand more about how well computers can do things in different languages. |
Keywords
» Artificial intelligence » Claude » Gpt » Language model » Llama
