What are the limitations of ChatGPT ? A 7-minute explanation (stereotypes, factual errors, etc.)
Channel: Inria Flowers
Date: 13/03/2024

This video showcases the major limitations and weaknesses of language models.

Language models, like ChatGPT, Gemini or Mistral, have shown significant capacities for diverse tasks (see the first video of the series). However, they do have many limitations and weaknesses ! In this video, we focus on the main ones :

- these models are trained with data whose quality and origin can be variable. The choice of this data is often not very monitored.
- this data, and thus language models, can express “bias”, which is an automatic way of thinking, which does not rely on any reasoning nor any facts. These biases are unjust and can reinforce stereotypes. If they are not controlled, they will be present in the productions and usages that these language models take part in.
- difficulty to trace back the source of information : a specific piece of  knowledge expressed by a model cannot be traced back to the corresponding training data.
- the training of these giant models requires vast amounts of data and compute
- their productions can contain factual errors, or even made up facts (we talk about “hallucinations”).
- especially, these models haven’t learned through physical interaction as human do, they can thus make elementary errors about the understanding of the physical world, or more generally common sense (we talk about the “grounding problem”)
- the answers of these language models often have a ton resembling one of an expert, absolutely certain of its knowledge. Their responses are often so credible that it is easy to believe that these models have an intention, and that they took into account the human they are speaking with to create their answer, which is not the case.
- models like ChatGPT, Gemini or Mistral are owned and controlled by private actors : not much information about how they work is available, as for example the way the training data was selected.

Chapters:
0:00 : Introduction
0:41 : Bias and alignment
2:10 : Frozen knowledge
2:34 : Inability to provide sources
3:05 : Data quantity
4:17 : Hallucinations
4:56 : Lack of practical understanding
5:56 : Interpretation we make of it
6:25 : Conclusion

**** “ChatGPT explained in 5 minutes” series ****
This video is the third episode of a video series (http://developmentalsystems.org/chatgpt_5_minutes/en/)  that presents language models. It is aimed at a large public, students and professors in middle and high school, and more generally anyone not accustomed to computer science or AI.
The other videos of the series :
- How does ChatGPT work? A 5-minute overview : https://www.youtube.com/watch?v=YXT6mFjInaU
- Prompting, or the art of making yourself understood by ChatGPT : https://youtu.be/AscZJQBot2I
- Strengths of language models : COMING SOON
- Advanced prompting, chain of thought reasoning and their limits : COMING SOON

**** Production ****
This video was made by the Inria Flowers team, working in the domain of developmental AI : https://flowers.inria.fr/

The voice was synthesized using ElevenLabs (elevenlabs.io).

Contributors :
-Alexandre Torres–Leguet (Twitter: @AlexandreTL2)
-Clément Romac (@ClementRomac)
-Thomas Carta (@CartaThomas2)
-Pierre-Yves Oudeyer (@pyoudeyer)

**** License ****
This video is distributed under the Creative Commons CC-BY license.

**** Sources ****
-babylm.github.io
-flaticon.com was used for icons