Summary of Hellofresh: Llm Evaluations on Streams Of Real-world Human Editorial Actions Across X Community Notes and Wikipedia Edits, by Tim Franzmeyer et al.
HelloFresh: LLM Evaluations on Streams of Real-World Human Editorial Actions across X Community Notes and Wikipedia edits
by Tim Franzmeyer, Aleksandar Shtedritski, Samuel Albanie, Philip Torr, João F. Henriques, Jakob N. Foerster
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The paper introduces HelloFresh, a benchmark for evaluating Large Language Models (LLMs) on real-world tasks. The existing benchmarks have limitations, including test data contamination and overfitting. HelloFresh addresses these issues by using continuous streams of human-labeled data from social media platforms like X (formerly Twitter) and Wikipedia. This allows for temporally consistent evaluation of LLMs, which is essential for safe development. State-of-the-art LLMs are backtested with simple web search access, and the results show that HelloFresh yields a ranking consistent over time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to test how well computers can understand language by using real-life data from Twitter and Wikipedia. This is important because it helps make sure that language models don’t get stuck in their ways or only work well for certain types of questions. The model uses human helpers to label the data, which makes it more reliable than other methods. The results show that this new benchmark can help evaluate language models in a fair and consistent way. |
Keywords
» Artificial intelligence » Overfitting
