Summary of Efficient Solutions For An Intriguing Failure Of Llms: Long Context Window Does Not Mean Llms Can Analyze Long Sequences Flawlessly, by Peyman Hosseini et al.
Efficient Solutions For An Intriguing Failure of LLMs: Long Context Window Does Not Mean LLMs Can Analyze Long Sequences Flawlessly
by Peyman Hosseini, Ignacio Castro, Iacopo Ghinassi, Matthew Purver
First submitted to arxiv on: 3 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 Medium Difficulty summary: This paper explores the limitations of Large Language Models (LLMs) when processing lengthy sequential inputs, despite their ability to comprehend millions of tokens in a single forward pass. The study uses three datasets and two tasks across various LLMs, including Claude 3, Gemini Pro, GPT 3.5 Turbo, Llama 3 Instruct, and Mistral Instruct models. The results show that LLMs fall short when handling long input sequences, but proposing ad-hoc solutions can enhance performance by up to 50% while reducing API costs and latency. This research highlights the importance of optimizing LLMs for long input processing in applications such as sentiment analysis and news categorization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper looks at how well Large Language Models (LLMs) do when dealing with very long pieces of text, like articles or reports. Despite being really good at understanding lots of words, LLMs actually struggle when the text is too long. The study tested different LLMs and found that they can improve their performance by up to 50% when handling long text. This means we can get more accurate results from our AI models while also making them faster and cheaper to use. |
Keywords
» Artificial intelligence » Claude » Gemini » Gpt » Llama
