Summary of Helmet: How to Evaluate Long-context Language Models Effectively and Thoroughly, by Howard Yen et al.
HELMET: How to Evaluate Long-Context Language Models Effectively and Thoroughly
by Howard Yen, Tianyu Gao, Minmin Hou, Ke Ding, Daniel Fleischer, Peter Izsak, Moshe Wasserblat, Danqi Chen
First submitted to arxiv on: 3 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 Many long-context language models (LCLMs) are evaluated on synthetic benchmarks like needle-in-a-haystack (NIAH), but it’s unclear if these reflect the diverse applications of LCLMs. We investigate and find that existing benchmarks provide noisy signals due to limited coverage, insufficient context lengths, unreliable metrics, and incompatibility with base models. To address this, we introduce HELMET, a comprehensive benchmark covering seven application-centric categories. We also address issues by adding controllable lengths up to 128K tokens, model-based evaluation for reliable metrics, and few-shot prompting for evaluating base models. HELMET offers more reliable and consistent rankings of frontier LCLMs, demonstrating that synthetic tasks like NIAH don’t reliably predict downstream performance, while diverse categories exhibit distinct trends and low correlations with each other. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Long-context language models (LCLMs) are being tested on many different tasks to see how well they work. But some people think these tests might not be a good way to figure out which models will do the best job in real-life situations. They’re looking for ways to test LCLMs that better match what we need them to do. In this paper, scientists created a new way to test LCLMs called HELMET. It includes many different tasks that are more like the kinds of things people usually use LCLMs for. This helps us understand which models will do well in real-life situations. |
Keywords
» Artificial intelligence » Few shot » Prompting
