Summary of Tower: Tree Organized Weighting For Evaluating Complex Instructions, by Noah Ziems et al.
TOWER: Tree Organized Weighting for Evaluating Complex Instructions
by Noah Ziems, Zhihan Zhang, Meng Jiang
First submitted to arxiv on: 8 Oct 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 Large language models (LLMs) must be able to understand and follow complex human-written instructions for effective deployment in real-world applications. While benchmarks like Chatbot Arena rely on human judges, this approach is resource-intensive and time-consuming. Alternative methods using LLMs as judges, such as AlpacaEval, MT Bench, WildBench, and InFoBench offer improvements, but they do not capture the nuanced importance of certain complex instruction aspects. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models need to be able to understand and follow instructions for real-world use. Right now, we rely on humans to test how well these models work, which takes a lot of time and resources. Instead, we can train other models to do the testing, but even this approach doesn’t fully capture the differences in importance between different parts of an instruction. |
