Unlocking the Power of GPT-4 Turbo 128K Context

Unlocking the Power of GPT-4 Turbo 128K Context

Table of Contents:

1. Introduction
2. Lost in the Middle: Understanding Language Models and Long Contexts
   • Performance of LLMs with Long Contexts
   • U-Shaped Performance Curve
3. GPT-4 Turbo: A Longer Context Window
   • Experimental Findings from Sean
   • Comparative Performance with Other LM Models
   • Analysis by Greg
4. Discoveries and Limitations
   • Performance Degradation with Larger Contexts
   • Low Recall Performance
5. Recommendations for Working with GPT-4 Turbo
   • Using Less Context for Improved Accuracy
   • Considerations for Retrieval Augmented Generation (RAG)
6. Conclusion
7. FAQ

Lost in the Middle: Assessing the Performance of GPT-4 Turbo in Long Contexts

Introduction

The release of GPT-4 Turbo with an extended context of 128,000 tokens raises the question of whether the model can effectively utilize such a context. In this article, we will Delve into empirical evidence to analyze the performance of GPT-4 Turbo. Before that, let's explore a crucial research paper titled "Lost in the Middle: How Language Models Use Long Contexts." This paper investigates the ability of language models (LMs) to extract information from extensive contexts and sheds light on some key findings.

Lost in the Middle: Understanding Language Models and Long Contexts

In the study on the performance of LLMs with large context windows, it was observed that if the information being sought is located either at the beginning or end of the context, the model performs remarkably well. However, when the desired information is situated in the middle of a long context, the performance of these LLMs significantly declines. Another noteworthy discovery was the substantial decrease in performance as input contexts grow longer, even for models explicitly designed for extended context windows.

GPT-4 Turbo: A Longer Context Window

To assess the performance of GPT-4 Turbo in relation to the "Lost in the Middle" phenomenon, we will explore different perspectives. Sean, the founder of Small Models, conducted an experiment involving 10 different random facts placed within the context of GPT-4 Turbo. The results depict that as the context window increases, the accuracy of information retrieval diminishes for both GPT-4 and GPT-4 Turbo. However, GPT-4 Turbo still delivers relatively good performance, retrieving around 8 out of 10 facts when using approximately 8,000 tokens.

Comparing GPT-4 Turbo with other proprietary models for information retrieval tasks, we can refer to a study by Alex. In their research paper, "Attention Sorting Combats Recency Bias and Long Context Language Models," they reveal that GPT-4 Turbo's performance declines significantly compared to smaller context windows when exceeding around 32,000 tokens. On the other HAND, Clot-2 demonstrates a better ability to retrieve information from large context windows.

Analyzing GPT-4 Turbo performance, Greg offers insightful observations. In his experiment, facts were placed within the document at different depths, and GPT-4 Turbo was tasked with retrieving the specified information. Results exhibit that up to 64,000 tokens, GPT-4 Turbo performs well in retrieving facts regardless of their placement within the context. However, beyond this threshold, the model struggles, particularly when facts are positioned towards the end of the context window.

Discoveries and Limitations

To summarize the findings from Sean, Alex, and Greg, it becomes evident that GPT-4 Turbo's performance starts to degrade when surpassing 73,000 tokens and when facts are situated between the 7th and 50th percentile of document depth. Placing facts at the beginning of the document facilitates successful retrieval, notwithstanding the context window's size. It is crucial to understand that while LLMs with larger context windows offer potential advantages, certain limitations exist concerning accuracy in retrieving specific information.

Recommendations for Working with GPT-4 Turbo

Considering the insights gained from analyzing GPT-4 Turbo, it is advisable to employ less context for improved accuracy. Although GPT-4 boasts an extended context window, adopting conventional wisdom in situations involving the "Lost in the Middle" phenomenon proves to be more beneficial. It is worth mentioning that these observations pertain solely to extracting information already present within the model's context. Retrieval augmented generation (RAG) may yield different results and should be considered for alternative approaches.

Conclusion

This article explored the empirical evidence regarding the performance of GPT-4 Turbo in long contexts. The analysis demonstrated that while GPT-4 Turbo offers an extensive context window, there are inherent limitations associated with information retrieval accuracy. By using smaller context windows, users can enhance the model's performance. However, different strategies may Apply when employing retrieval augmented generation techniques. Although scientific studies in this specific area are limited, the evidence presented thus far suggests consistent Patterns in LLMs.

FAQ

Q: Does GPT-4 Turbo perform better with longer context windows? A: The performance of GPT-4 Turbo declines as the context window exceeds a certain threshold, typically around 32,000 tokens. While a longer context window offers potential advantages, there are limitations in accurately retrieving information from extensive contexts.

Q: Can GPT-4 Turbo retrieve information placed in the middle of a long context? A: Empirical evidence suggests that the performance of GPT-4 Turbo deteriorates significantly when the desired information is located in the middle of a long context. Retrieving information towards the beginning or end of the context yields better results.

Q: Are there any alternative models that outperform GPT-4 Turbo in long contexts? A: Comparative analysis demonstrates that Clot-2, another LM model, exhibits better performance in retrieving information from large context windows compared to GPT-4 Turbo. However, more comprehensive research is required to validate these claims across multiple datasets.

Q: How can I enhance the accuracy of information retrieval with GPT-4 Turbo? A: It is recommended to use smaller context windows to improve the accuracy of GPT-4 Turbo in information retrieval tasks. While GPT-4 Turbo has an extensive context window, relying on conventional wisdom regarding the "Lost in the Middle" phenomenon can yield more accurate results.

Q: What is the significance of attention priors in GPT-4 Turbo's performance? A: Attention priors learned during the pre-training of language models contribute to the recency bias and "Lost in the Middle" phenomenon. These attention mechanisms affect GPT-4 Turbo's ability to retrieve information accurately from long context windows.

Note: The findings and recommendations presented here are Based on empirical evidence and may be subject to further research and analysis.