Riley Goodside

ChatGPT has captured LLM headlines and amazed the AI community with its extensive natural language processing capabilities. A new LLM from Anthropic called Claude is competitive with ChatGPT and offers great promise. We evaluate both models head to head and give you our thoughts on how they compare.

Claude vs. ChatGPT: Let’s Think Step by Step

Meet Claude: Anthropic’s Rival to ChatGPT

On November 28th, OpenAI released a new addition to the GPT-3 model family: <code>davinci-003</code>. This latest model builds on <a href="https://openai.com/blog/instruction-following/" target="_blank" rel="noopener noreferrer">InstructGPT</a>, using reinforcement learning with human feedback to better align language models with human instructions. Unlike <code>davinci-002</code>, which uses supervised fine-tuning on human-written demonstrations and highly scored model samples to improve generation quality, <code>davinci-003</code> is a true reinforcement learning with human feedback (RLHF) model. It is trained with <a href="https://openai.com/blog/openai-baselines-ppo/" target="_blank" rel="noopener noreferrer">PPO</a> to optimize the generated text&rsquo;s score against a separate &ldquo;reward model&rdquo;, which is trained on rating comparisons by human graders of different model outputs. More details can be found in OpenAI&rsquo;s <a href="https://beta.openai.com/docs/model-index-for-researchers" target="_blank" rel="noopener noreferrer">model index for researchers</a>. The net result is that <code>davinci-003</code> is tuned to produce outputs that it thinks would be scored highly by humans. Scale is proud to partner with OpenAI to provide this human feedback.
OpenAI&rsquo;s announcement email mentions the following improvements for <code>davinci-003</code>:
<ul>
<li>&ldquo;It produces higher quality writing. This will help your applications deliver clearer, more engaging, and more compelling content.</li>
<li>It can handle more complex instructions, meaning you can get even more creative with how you make use of its capabilities now.</li>
<li>It&rsquo;s better at longer form content generation, allowing you to take on tasks that would have previously been too difficult to achieve.&rdquo;</li>
</ul>
But how much better is <code>davinci-003</code>, really? We decided to put it to the test quantitatively. Using <a href="https://scale.com/spellbook" target="_blank" rel="noopener noreferrer">Scale Spellbook</a>, the platform to build, compare and deploy large language model apps, we evaluated <code>davinci-003</code> versus <code>davinci-002</code> across tasks ranging from few- and zero-shot classification, summarization, and poetry writing. Here&rsquo;s what we found.
<h3>Classification</h3>
It&rsquo;s well known that <code>davinci-002</code> performs very well on classification with a few-shot prompt, and it appears that <code>davinci-003</code> offers comparable (but slightly worse) performance few-shot. Using Spellbook&rsquo;s built-in evaluation feature on 250 Yelp reviews, we compare the two models&rsquo; classification of these reviews as Positive, Negative, or Neutral in a four-shot prompt&mdash;meaning four labeled examples are included in the prompt itself.
<img src="https://cdn.builder.io/api/v1/image/assets%2Fe0438815ba51486bbb6a202747122d4b%2F65f8dfd9c9744c15967d03341c5cdc73">
The davinci-003 model has 87.2% accuracy on this 250 Yelp review dataset sample, slightly lower than davinci-002&rsquo;s 91.6%.
However, if we ask the two models to classify these same reviews in a zero-shot prompt, we find that <code>davinci-003</code> scores more than 30% higher in accuracy than <code>davinci-002</code>&mdash;and on par with a <code>davinci-002</code> few-shot prompt! This is a substantial improvement in zero-shot classification performance. In this classification setting, adding examples to the prompt actually lowers <code>davinci-003</code>&rsquo;s accuracy.
<img src="https://lh6.googleusercontent.com/Wr30hJNdDAekBQTxtBRBN40_GR1CIElZlygaJSrAbGd6OK20V-pGS_bI5BoCF2SS4az3z7EFPYde3k5TnoVKHZd-68dwlhtHctxB7XtW5zqOXOjQkY2P8SxCkAjzlmg1PpKQ1hzEgob2W18laWD8FfSjNYShqjurevSldtMENyh8DUALgcMeYi4HQKi-">
Beyond accuracy, we found that asking <code>davinci-003</code> to explain its rationale in a zero-shot prompt works reliably, and provides a better explanation than a one-shot <code>davinci-002</code> about 75% of the time, based on Scale&rsquo;s human evaluation workforce judging a sample of 100 outputs.
<h3>Text Generation</h3>
The most notable performance leap comes in how <code>davinci-003</code> understands word and sentence structure, especially rhyming and syllables. In prompting for a poem that rhymes, <code>davinci-003</code> constructs a desired output, whereas <code>davinci-002</code> largely fails to implement rhyming.
<img src="https://cdn.builder.io/api/v1/image/assets%2Fe0438815ba51486bbb6a202747122d4b%2F31cb06da7e1a488a91f62ef14feee823">
<code>davinci-003</code> is also better equipped to handle requests for complex output structures, such as iambic pentameter (think Shakespeare). <code>davinci-002</code> certainly provides a poem, but ignores the request for lines of 5 pairs of stressed and unstressed syllables (buh-BUM). While not perfect, <code>davinci-003</code>&rsquo;s output is significantly more faithful to the requested prompt.
<img src="https://cdn.builder.io/api/v1/image/assets%2Fe0438815ba51486bbb6a202747122d4b%2Fc3fc8edf1aea432bb7bb5118a6dc8c82">
<h3>Summarization</h3>
One practical difference between the two variants is verbosity. In our experimentation, <code>davinci-003</code> consistently generates longer outputs than <code>davinci-002</code> on the same prompts. This is most visible in summarization tasks, where the value of the result may depend on its brevity.
In a sample of 60 articles, we tested both variants&rsquo; summarization capabilities by asking them to summarize the introductory paragraphs of lengthy Wikipedia pages. The additional text generated by <code>davinci-003</code> is typically beneficial, including information central to an article&rsquo;s importance. However, the resulting summaries are also wordy. In our analysis, <code>davinci-003</code> generates outputs about 65% longer than <code>davinci-002</code> under identical prompts.
<img src="https://cdn.builder.io/api/v1/image/assets%2Fe0438815ba51486bbb6a202747122d4b%2Fbbd0ed222dff4216ab9173d07d176890">
It will be important for GPT-3 users to bear in mind <code>davinci-003</code>&rsquo;s talkativeness when choosing between the two variants. In our testing, given the task of generating a 1-sentence summary, <code>davinci-003</code> produced 42 words on average, nearly doubling <code>davinci-002</code>&lsquo;s average of 23. The shortest summary <code>davinci-003</code> gave back was a beefy 32 words, compared to only 12 words for <code>davinci-002</code>. This tendency extends to multi-sentence summaries as well.
For use cases that value brevity, <code>davinci-002</code> is the safer choice. Nevertheless, for many use cases, the longer and more detailed completions provided by <code>davinci-003</code> can be beneficial.&nbsp;
<h3>Conclusion</h3>
Overall, <code>davinci-003</code> is a meaningful improvement from <code>davinci-002</code>, especially for zero-shot classification and complex text generation.
While many of the benefits of the latest iteration of GPT-3 are apparent at the surface, concrete evaluation metrics ensure models and prompt strategies are performing as well as possible.
Luckily, Spellbook makes that easy, allowing users to run instant programmatic evaluation in classification use cases, or sending model outputs to Scale&rsquo;s high-quality workforce to get quantitative evaluation metrics for generative use cases.
<a href="https://scale.com/spellbook" target="_blank" rel="noopener noreferrer">Sign up for Spellbook today!</a>
P.S. If you&rsquo;re looking for a little dramatic flair in your emails to coworkers, <code>davinci-003</code> has you covered. It performs significantly better at creatively translating text or even explaining code as different celebrities in a zero-shot setting.
&nbsp;
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<pre class="ql-syntax" spellcheck="false"> Write a short rhyming poem about winter in New York davinci-002
davinci-003
</pre>
In New York City When winter descends
The air is so crisp and chilly
And the wind never ends
The snowflakes are so big and fluffy
You just want to grab a handful
And then make a snowball
People hurry to and fro
Bundled up in coats
Hats, and scarves, and gloves
No one wants to be caught
Lights are twinkling in the night, It's wintertime in New York City so bright.
The snow is glistening, the wind is cold,
The atmosphere is really bold.
The snowflakes fall with a gentle grace,
Adorning the city with a lovely lace.
The air is crisp, and the people merry,
It's a magical winter in New York City.
Text Generation
The most notable performance leap comes in how davinci-003 understands word and sentence structure, especially rhyming and syllables. In prompting for a poem that rhymes, davinci-003 constructs a desired output, whereas davinci-002 largely fails to implement rhyming.

We evaluate davinci-003 across a range of classification, summarization, and generation tasks.

Riley Goodside

Meet Claude: Anthropic’s Rival to ChatGPT

How Much Better is OpenAI’s Newest GPT-3 Model?