In this video, we will look at stress testing results of GPT-4 Turbo's 128K context window. We will look at some analysis that is done by the community to retrieve information from different lengths of context. Results will surprise you!
Connect:
☕ Buy me a Coffee: [ Ссылка ]
|🔴 Support my work on Patreon: Patreon.com/PromptEngineering
🦾 Discord: [ Ссылка ]
📧 Business Contact: engineerprompt@gmail.com
💼Consulting: [ Ссылка ]
Links:
@DataIndependent
X-links:
[ Ссылка ]
[ Ссылка ]
[ Ссылка ]
Paper:
Lost in the Middle: [ Ссылка ]
Attention Sorting: [ Ссылка ]
Timestamps:
[00:00] Introduction to GPT-4 Turbo and Contextual Performance Analysis
[00:19] Overview of "Lost in the Middle" Paper on LLM Context Usage
[01:04] Performance Degradation in LLMs with Longer Input Contexts
[02:07] Comparative Analysis of Different LLMs in Context Retrieval
[03:48] GPT-4 Turbo Performance at Different Context Lengths
[06:50] Greg's In-Depth Analysis on GPT-4 Turbo Context Retrieval
[10:03] Conclusions and Recommendations for Using Large Context LLMs
[10:47] Final Thoughts on Empirical Evidences and Future Research Directions
