Summary of Lissard: Long and Simple Sequential Reasoning Datasets, by Mirelle Bueno et al.
Lissard: Long and Simple Sequential Reasoning Datasets
by Mirelle Bueno, Roberto Lotufo, Rodrigo Nogueira
First submitted to arxiv on: 12 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 This paper introduces Lissard, a benchmark designed to test the ability of language models to process and generate sequences of varying lengths. Current state-of-the-art language models excel at handling short sequences but struggle with longer ones, even when trained on vast amounts of data. For instance, while they can identify common items in lists up to 20 items long, they falter when dealing with lists containing 80 items or more. The authors evaluate open-source and proprietary models (Mistral-7B, Mixtral-8x7B, GPT-3.5, and GPT-4) on Lissard’s seven tasks and find that all models exhibit a decline in performance as sequence complexity increases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a super smart computer program that can understand language. These programs are called “language models.” Right now, they’re really good at doing things like answering questions or summarizing text. But when it comes to tasks that require repeating simple rules over and over again, even these advanced models struggle. For example, if you ask them to find common items in a list of 20 things, they can do it easily. But if the list has 80 items, they get confused. This paper introduces a new test called Lissard that checks how well language models perform on sequences of different lengths. |
Keywords
» Artificial intelligence » Gpt
