Understanding Completeness and Accuracy in LLMs.
Understanding Completeness and Accuracy in Large Language Models.
As the pace of advancements in artificial intelligence continues to accelerate, we find ourselves amid the digital whirlwind of evolving language models. For example, a post in social media we found discusses this topic, arguing that OpenAI's GPT-4 is worse now than some months ago.
The focus of this post is a recent academic paper that delves into the complex landscape of evolving language models and offers a nuanced critique of the latest iterations.
The perception that the blog post infers the paper declares newer versions of these language models as inherently deficient.
In our view, this amounts to a fallacy. It's essential to clarify that while these models may not exhibit optimal efficiency in certain aspects, they simultaneously demonstrate considerable advancements in others.
Delving into the Paper's Insights
The conclusion drawn by the paper isn't that the newer models, namely GPT-3.5 and GPT-4, are subpar. Contrarily, it asserts that:
On sensitive queries: The models performed better than their predecessors, as they responded to fewer sensitive questions. However, the paper does criticize the lack of explanatory responses from the models, which often resort to default responses such as "Sorry, I can't help with that."
On code generation: The models created more explanatory and superior quality code. Nevertheless, one cannot directly copy and paste this code; it requires the removal of formatting elements.
On Visual Reasoning:The models showed improvement, although the paper seems to argue that the improvement wasn't significant enough.
The paper's authors utilized their self-created benchmarks rather than using established ones available in the market. For instance, they applied a dataset of 50 LeetCode tests for code, and another dataset for sensitive questions and mathematical problems. However, more comprehensive benchmarks, such as the open-source HumanEval developed by OpenAI, were available.
Decoding the Paper's Conclusions
A key excerpt from the paper states:
Our findings demonstrate that the behaviour of GPT-3.5 and GPT-4 has varied significantly over a relatively short amount of time. This highlights the need to continuously evaluate and assess the behaviour of LLMs in production applications.
This infers that as the digital landscape shifts, it is beneficial to have a system to detect these changes, be they positive or negative.
Our key takeaways
With the evolution of language models, they now produce varying outputs. While OpenAI trains and validates its models using its datasets, it's not guaranteed that these changes will impact other datasets or experiments in the same way.
Large Language Models (LLMs) have a wide array of applications, making it challenging for them to excel in all areas. As it stands, no dataset is comprehensive enough to train and validate this wide application spectrum.
The more an LLM is optimized for a specific task, the higher the likelihood of it underperforming in areas not under focus.
To better understand this, consider an AI trained to paint yellow balls. As a collateral effect, the AI may also learn to paint purple balls. With continued training to enhance its ability to paint yellow balls, the AI might lose its ability to paint purple balls because the model is being optimized for a different task.
This is reflective of the current scenario, where GPT-4, capable of performing a multitude of actions, might experience undesirable changes for users with very specific use cases.
In conclusion, it's essential to view the evolution of language models as a double-edged sword. While they may seem to deteriorate in one aspect, such as generating executable code, they could improve significantly in others, like providing more comprehensible explanations for novice programmers. As these models continue to evolve, it's crucial to remain agile and adaptable, ensuring their diverse capabilities are harnessed effectively.←