Orca vs ChatGPT4 (according to GPT4)

The Brief

Can you summarize and compare the capabilities of Orca in this paper: 'https://arxiv.org/pdf/2306.02707.pdf' against ChatGPT-4 in a table and write an exec summary of your findings in under 500 words

The Response

Based on the abstract of the Orca paper, we can identify several key features and capabilities of Orca that can be compared to ChatGPT-4: Orca is a 13-billion parameter model that is focused on enhancing the capabilities of smaller models through imitation learning, taking cues from the outputs generated by large foundation models (LFMs) like GPT-4.

Orca learns to imitate the reasoning process of LFMs, a step up from models that learn only to imitate the style of LFMs. Orca uses explanation traces, step-by-step thought processes, and other complex instructions from GPT-4 in its learning, guided by teacher assistance from ChatGPT. Orca uses large-scale and diverse imitation data with judicious sampling and selection to promote its progressive learning.

Orca surpasses other state-of-the-art instruction-tuned models like Vicuna-13B by more than 100% in complex zero-shot reasoning benchmarks like Big-Bench Hard (BBH) and by 42% on AGIEval. Orca reaches parity with ChatGPT on the BBH benchmark and shows competitive performance in professional and academic examinations like the SAT, LSAT, GRE, and GMAT in zero-shot settings without CoT (Co-training). However, Orca still trails behind GPT-4 in performance​1​.

As of my knowledge cutoff in September 2021, I can provide some general information about ChatGPT-4, but for the most up-to-date and detailed comparison, I would need to look up the current capabilities of ChatGPT-4. As of my last update, GPT-4 or ChatGPT-4 specifically had not been released by OpenAI. However, its predecessor, GPT-3, had 175 billion parameters and had demonstrated impressive capabilities in generating human-like text, answering questions, writing essays, summarizing text, and even creating poetry. It also performed well in several benchmarks and was able to handle a wide range of tasks without specific training due to its zero-shot learning capabilities.

Given the general trend in the development of these models, we can speculate that ChatGPT-4 would likely have more parameters than GPT-3, further improved text generation and understanding capabilities, and perhaps more advanced reasoning abilities. To provide a detailed comparison with Orca, I would need to search for the most recent information on ChatGPT-4. Shall I proceed with that?

User: Yes

ChatGPT-4 has a number of advancements over its predecessors, which provide it with improved capabilities. Specifically, ChatGPT-4 is more advanced in three key areas:

1. Creativity: GPT-4 is much better at both creating and collaborating with users on creative projects. This includes music, screenplays, technical writing, and even learning and adopting a user's writing style.
2. Longer Context: GPT-4 can now process up to 25,000 words of text from the user. This means it's capable of handling extended conversations and creating long-form content. You can also send GPT-4 a web link, and it will interact with the text from that page.
3. Visual Input: GPT-4 can now receive images as a basis for interaction. In one example, the chatbot is given an image of a few baking ingredients and is asked what can be made with them.

OpenAI also states that GPT-4 is significantly safer to use than the previous generation. It reportedly produces 40% more factual responses in OpenAI's own internal testing and is 82% less likely to respond to requests for disallowed content【27†source】.

Orca, a 13-billion parameter model developed by Hugging Face, learns from rich signals from GPT-4 including explanation traces; step-by-step thought processes; and other complex instructions, guided by teacher assistance from ChatGPT. This progressive learning approach enables Orca to surpass conventional state-of-the-art instruction-tuned models such as Vicuna-13B by more than 100% in complex zero-shot reasoning benchmarks like Big-Bench Hard (BBH) and 42% on AGIEval. Moreover, Orca reaches parity with ChatGPT on the BBH benchmark and shows competitive performance in professional and academic examinations like the SAT, LSAT, GRE, and GMAT, both in zero-shot settings without CoT; while trailing behind GPT-4【19†source】.

Comparison

	Orca	ChatGPT-4
Pros	Imitates the reasoning process of LFMs Learns from rich signals including explanation traces and step-by-step thought processes Uses large-scale and diverse imitation data Surpasses other state-of-the-art instruction-tuned models in complex zero-shot reasoning benchmarks Reaches parity with ChatGPT on BBH benchmark Shows competitive performance in professional and academic examinations	Improved creativity and collaboration capabilities Capable of processing up to 25,000 words of text from the user Can interact with visual input Produces more factual responses Less likely to respond to requests for disallowed content
Cons	Still trails behind GPT-4 in performance May face the same limitations as other imitation learning models	As an advanced AI model, may require significant computational resources Detailed performance metrics on benchmarks and examinations are unknown (as of the time of this comparison)

Please note that this comparison is based on the latest available information about Orca and ChatGPT-4. The capabilities and performance of these models could change as they are updated and improved over time【19†source】【27†source】.