Claude 3 was recently released, and results have shown that it has beaten the crowned king of large language models. But Did Claude 3 Really Beat GPT-4, or Was It a Marketing Gimmick. We and the entire AI community were excited, not because we have something against OpenAI, but because we hoped that this might force Sam Altman to release GPT-5 or at least get a reaction out of them. However, it seems it was all just a gimmick. We know companies are prone to push a myriad of marketing strategies. Among these tactics, a concerning trend has emerged: the practice of disseminating misleading, exaggerated, or entirely false information to enhance brand image or product desirability. For example Google admits that a Gemini AI demo video was staged.
It looks like Anthropic is learning from google and used similar marketing tactics to make Claude 3 better than rest in LLM benchmarks. We are going to discuss in this article how Anthropic manipulated the benchmarks for Claude 3 and why LLM benchmarks are not always the best way to measures which AI model is the best.
Table of contents
Claude 3 and Anthropic Marketing Gimmicks
The community was excited after the release of Claude 3,which dethroned GPT-4, and was patiently waiting for response from OpenAI. However, Tolga Bilge recently pointed out that Anthropic used marketing tricks to fool the audience. They chose to compare Claude 3 to GPT-4 upon its release in March 2023.
According to Promptbase’s benchmarking, GPT-4-turbo scores better than Claude 3 on every benchmark where we can make a direct comparison.
As the saying goes, “The devil is in the details”. Anthropic subtly disclosed in a footnote that they were not comparing Claude 3 with the most powerful GPT-4 version.
However, the community is split on this issue. Some user claims that aside from the gimmickry from Anthropic, Claude still outperforms the current GPT-4. While others say that even if Claude 3 is better than GPT-4, it still is not a better option due to Claude 3 being a more expensive option available right now.
Regardless of the situation, we can always say that LLM benchmarks are not very reliable. There are reasons for this assumption, which we will discuss further.
Limitation of LLM Benchmarks
Narrow Scope of Evaluation
LLM benchmarks often focus on specific tasks or types of knowledge, which can limit their ability to measure an LLM’s overall ability or intelligence. This means that high performance on a benchmark might not translate to real-world effectiveness, as these benchmarks may not capture the full range of skills required for practical applications.
Overfitting and Gaming the Benchmarks
There is a risk that models are specifically trained or fine-tuned to excel on benchmark tests, which can lead to overfitting. This means the model might perform well on the benchmarks. But this also mean they will perform poorly in real-world tasks that differ even slightly from the benchmark setup. Developers might inadvertently (or deliberately) “teach to the test,” optimizing models in ways that maximize benchmark scores rather than focusing on genuine understanding or versatility.
Lack of Novelty and Creativity Assessment
Many benchmarks fail to evaluate a model’s ability to generate novel ideas or solutions, focusing instead on its ability to recall information or perform well-understood tasks. Creativity and the ability to handle unexpected situations are critical for many applications of LLMs, but these qualities are difficult to quantify in standardized tests.
Cultural and Linguistic Bias
Benchmarks can encode cultural and linguistic biases, making them less useful for evaluating models intended for global, diverse applications. If a benchmark is heavily skewed towards a particular language, culture, or way of thinking, it may not accurately reflect a model’s performance in other contexts.
Rapid Advancements Outpace Benchmarks
The field of AI and machine learning is advancing rapidly, and benchmarks can quickly become outdated. As models evolve, the challenges that were once difficult may become trivial, rendering benchmarks less meaningful. Additionally, new capabilities emerge that benchmarks may not yet test, leaving gaps in our understanding of a model’s full capabilities.
Ethical and Societal Impact Not Addressed
Most benchmarks do not assess the ethical implications or societal impacts of LLMs, such as their potential for bias, misinformation, or privacy concerns. A model might score highly on technical benchmarks while still being harmful or unethical in certain applications.
Conclusion
The release of Claude 3, positioned by Anthropic as a superior alternative to GPT-4. The excitement surrounding Claude 3’s purported victory over GPT-4 highlights the limitations of relying solely on LLM benchmarks. While the initial claims of Claude 3’s dominance may have been misleading. It’s crucial to remember that these benchmarks offer a narrow window into the capabilities of complex AI models.
Therefore, we should approach LLM benchmarks with a critical eye. While they can provide some insights into model performance, they should not be the sole metric for judging an LLM’s overall effectiveness. Ultimately, the true “winner” in the race between AI models can not be measured by these benchmarks.
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