Summary of Anls* — a Universal Document Processing Metric For Generative Large Language Models, by David Peer et al.
ANLS* – A Universal Document Processing Metric for Generative Large Language Models
by David Peer, Philemon Schöpf, Volckmar Nebendahl, Alexander Rietzler, Sebastian Stabinger
First submitted to arxiv on: 6 Feb 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 In this paper, researchers investigate the challenges of evaluating generative large language models (GLLMs) in tasks like document classification and information extraction. Traditionally, discriminative models have been the go-to choice for these tasks, but recent advancements in GLLMs have changed the game. While GLLMs offer enhanced zero-shot capabilities, their predictions are more nuanced and don’t fit neatly into a binary true or false evaluation. The authors aim to address this challenge by exploring new methods for evaluating GLLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is all about finding a way to measure how well generative large language models (GLLMs) do on tasks like recognizing what’s in documents and pulling out important information. Right now, we use special kinds of models called discriminative models for these jobs. But new types of GLLMs are getting really good at doing things without needing extra training data. The problem is that it’s hard to tell how well they’re doing because their answers aren’t just “yes” or “no”. This paper tries to solve this challenge by coming up with new ways to figure out how well the GLLMs are working. |
Keywords
» Artificial intelligence » Classification » Zero shot
