Summary of Clembench-2024: a Challenging, Dynamic, Complementary, Multilingual Benchmark and Underlying Flexible Framework For Llms As Multi-action Agents, by Anne Beyer et al.
clembench-2024: A Challenging, Dynamic, Complementary, Multilingual Benchmark and Underlying Flexible Framework for LLMs as Multi-Action Agents
by Anne Beyer, Kranti Chalamalasetti, Sherzod Hakimov, Brielen Madureira, Philipp Sadler, David Schlangen
First submitted to arxiv on: 31 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 explores the use of Large Language Models (LLMs) in self-play conversational games to evaluate their capabilities. By prompting LLMs to play these games, researchers can automatically score their performance and test various dimensions such as data contamination and human vs. model performance. The proposed framework demonstrates its effectiveness in keeping up with new developments and investigating questions like the impact of prompting language on performance. This approach has the potential to inform decisions on model choice for building applied interactive systems and even set up a closed-loop development environment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how computers can play games to test their language skills. By making them play games, researchers can see how well they’re doing and compare it to human performance. The system works well at keeping up with new developments and helps answer questions about what makes the computer models better or worse. This is useful for building systems that interact with people. |
Keywords
» Artificial intelligence » Prompting
