Re: ChatGPT, ontologies and SPARQL

Hello,

Do you think their choice of GPT was a good choice which has a bias towards
the decoder architecture?
What if it was chatT5 a balance of encoder-decoder architecture layers in a
LLM with RL. Would make it more flexible.
One could still use IFT (instruction fine tuning), SFT (supervised
fine-tuning), RLHF (reinforcement learning from human feedback), and CoT
(chain of thought).
Where IFT is a tiny fraction, SFT uses human annotations, CoT improves on
the model performance for given tasks.

Thanks,

Adeel



On Wed, 25 Jan 2023 at 14:04, Dave Raggett <dsr@w3.org> wrote:

> Hi Adam,
>
> You can see more about my proposed research roadmap at:
>
> https://www.w3.org/2023/02-Raggett-Human-like-AI.pdf
>
> Slides 5 and 6 include examples of the kinds of reasoning that ChatGPT can
> do, showing that it is well beyond what is practical today with RDF and the
> Semantic Web. There is however a great deal more research needed to evolve
> from ChatGPT to practical everyday artificial general intelligence. See
> slide 7 for a summary of what’s needed.
>
> Best regards,
> Dave
>
> On 25 Jan 2023, at 06:47, Adam Sobieski <adamsobieski@hotmail.com> wrote:
>
> Dave,
>
> Thank you and I agree with your points. I’m excited about the near future
> when scientists will better understand the emergence of abilities in
> large-scale artificial neural networks. A related topic is that of
> multi-task learning (https://en.wikipedia.org/wiki/Multi-task_learning).
>
> I'm also excited about artificial neural networks interoperating with
> external knowledgebases. This interoperation could utilize SPARQL.
>
> With respect to large-scale dialogue systems like ChatGPT, some topics
> that I previously considered in the contexts of intelligent tutoring
> systems include dialogue context, user modeling, and A/B testing.
>
> Large-scale systems like ChatGPT are engaging with a large volume of users
> at an instant, over the course of a day, and with, I imagine, some task
> redundancy. Large-scale dialogue systems which can measure or infer quality
> of service and user satisfaction could vary their outputs, e.g., their
> framings or phrasings, over populations of users to explore whether, which,
> and why variations result in increased quality of service or user
> satisfaction.
>
> Beyond providing users with buttons with which to obtain their feedbacks,
> e.g., a thumbs up button, scientists could also explore more clever means
> of measuring user comprehension and satisfaction. If a user exits a
> dialogue quickly after an answer is provided, was their question answered
> directly and to their satisfaction or, did they, instead, in frustration,
> seek another system to engage with? If a user responds to an AI system more
> rapidly in a dialogue, was the AI system’s previous content phrased in a
> more readable and comprehensible manner for that user and/or was the
> dialogue more engaging? We can also consider that video-chat-based dialogue
> systems should have more data to utilize towards ascertaining quality of
> service and user satisfaction during dialogues.
>
> These techniques would be milestones on the journey to metadialogical
> capabilities, where dialogue systems could receive feedback about their
> dialogues in those dialogues.
>
> In addition to the topics of semantic content curation and engineering
> (which might be otherwise-named subtopics of operating the systems), there
> are also to consider large-scale AI systems which can perform A/B testing
> over populations of users to maximize quality of service and user
> satisfaction. In my opinion, care should be taken to avoid the pitfalls of
> personalization, e.g., filter bubbles.
>
>
> Best regards,
> Adam
>
> ------------------------------
> *From:* Dave Raggett <dsr@w3.org>
> *Sent:* Tuesday, January 24, 2023 4:18 AM
> *To:* Adam Sobieski <adamsobieski@hotmail.com>
> *Cc:* Adeel <aahmad1811@gmail.com>; public-aikr@w3.org <public-aikr@w3.org
> >
> *Subject:* Re: ChatGPT, ontologies and SPARQL
>
> Dropping back to AIKR ...
>
> Scaling up language models has been shown to predictably improve
> performance and sample efficiency on a wide range of downstream tasks. This
> paper instead discusses an unpredictable phenomenon that we refer to as
> emergent abilities of large language models. We consider an ability to be
> emergent if it is not present in smaller models but is present in larger
> models. Thus, emergent abilities cannot be predicted simply by
> extrapolating the performance of smaller models. The existence of such
> emergence raises the question of whether additional scaling could
> potentially further expand the range of capabilities of language models.
>
>
> A deeper understanding of how ChatGPT is able to generate its results
> should allow us to devise smaller and more climate friendly systems.
> Practical applications don’t need the vast breadth of knowledge that
> ChatGPT got from scraping most of the web.
>
> A deeper understanding will also facilitate research on fixing major
> limitations of large language models, e.g. continuous learning, integration
> of explicit domain knowledge, metacognition, introspection and better
> explanations that cite provenance, etc.
>
> Dave Raggett <dsr@w3.org>
>
>
> Dave Raggett <dsr@w3.org>
>
>
>
>