Large Language Models, Hype and Prompt Chaining

Meta released Galactica recently to great fanfare and then rapidly removed it.

Janelle Shane poked some fun at Galactica in a post that showed how you can get it give you nonsense answers while making then serious point that you should be very aware of the hype. From a research point of view, Galactica is obviously super exciting. From a real-life point of view, you’re not about to replace your chatbot with Galactica, not while it suffers from hallucinations.

But there are serious use-cases for large language models like Galactica and Googles’ Flan-T5. Just not writing fully-fledged research articles.

You have to ask the model a number of smaller questions one after the other. In the jargon: ‘prompt chaining‘. For example – referring to Janelle’s example question that fooled Galactica:

Prompt: how many giraffes are in a mitochondria?
Galactica: 1

Don’t treat the language model as the holder of all knowledge. Treat the language model as an assistant who is super keen to help and is desperate not to offend you. You have to be careful what you ask, and perhaps ask several questions to get to the real answer. Here is an example I did with Flan T5 using a playground space on HuggingFace.

Prompt: Does mitochondria contain giraffes?
Flan T5: no

Prompt: How many giraffes are in a mitochondria?
Flan T5: ten

Using the same question that Galactica was given, we get a nonsense answer. Flan T5 is even more keen that Galactica to give us an impressive-sounding answer. But if you take both questions together then you can draw a more meaningful conclusion. Chain the prompts and first ask the ‘yes/no’ question and then only ask the second question depending on the answer you get from the first.

Having written all of this, today I learnt about OpenAI’s ChatGPT which seems like a massive step forward towards solving the hallucination problem. I love how fast this space is moving these days.

Entity extraction powered by Flan

A Thanksgiving update on my side project. See here for an outline of the problem. In short, existing natural language processing techniques are good at generic entity extraction, but not at really getting to the core of the story.

I call it the ‘Bloomberg problem’. Imagine this text: “Bloomberg reported that Foo Inc has bought Bar Corp”. Bloomberg is not relevant in this story. But it is relevant in this one: “Bloomberg has just announced a new version of their Terminal”.

I wrote about my first attempt to address this problem, and then followed it up in July. I’ve been doing some more finessing since then and am pretty happy with the results. There is still some tidying up to do but I’m pretty confident that the building blocks are all there.

The big changes since July are:

1. Replacing a lot of the post-processing logic with a model trained on more data. This was heartbreaking (throw away work, sad face emoji) but at the same time exhilarating (it works a lot better with less code in, big smile emoji).
2. Implementing Flan T5 to help with some of the more generic areas.

At a high level this is how it works:

1. The model
   • Approx 400 tagged docs (in total, across train, val and test sets)
   • Some judicious data synthesis
   • Trained a Named Entity Recognition model based on roberta-base
2. Post-processing is a combination of
   • Benepar for constituency parsing to identify the relationships between entities for most cases
   • FlanT5 to help with the less obvious relationships.

Next steps are going to be to start representing this as a knowledge graph, which is a more natural way of exploring the data.

See below for a screenshot of the appointment topics extracted recently. These are available online at https://syracuse-1145.herokuapp.com/appointments

And below are the URLS for these appointment topics

The Native Antigen Company Strengthens Senior Team to Support Growing Product Portfolio

In this example, we have a number of companies listed – some of the company that is appointing these two new individuals and some are companies where the individuals used to work. Not all the company records are equally relevant.

Wolters Kluwer appoints Kevin Hay as Vice President of Sales for FRR

The Native Antigen Company Strengthens Senior Team to Support Growing Product Portfolio (Business Wire version)

Kering boosted by report Gucci’s creative director Michele to step down

Broadcat Announces Appointment of Director of Operations

Former MediaTek General Counsel Dr. Hsu Wei-Fu Joins ProLogium Technology to Reinforce Solid-State Battery IP Protection and Patent Portfolio Strategy

HpVac Appoints Joana Vitte, MD, PhD, as Chief Scientific Officer

Highview Power Appoints Sandra Redding to its Leadership Team

Highview Power Appoints Sandra Redding to its Leadership Team (Business Wire version)

ASML Supervisory Board changes announced

Recommendation from Equinor’s nomination committee

I’m pretty impressed with this one. There are a lot of organizations mentioned in this document with one person joining and one person leaving. The system has correctly identified the relevant individuals and organization. There is some redundancy: Board Member and Board Of Directors are identified as the same role, but that’s something that can easily be cleaned up in some more post-processing.

SG Analytics appoints Rob Mitchell as the new Advisory Board Member

Similarly, this article includes the organization that Rob has been appointed to and the names of organizations where he has worked before.

SG Analytics appoints Rob Mitchell as the new Advisory Board Member (Business Wire version)

In-Person vs Remote

I’m seeing a lot of debate about whether it is right for a company to be in-person, hybrid or remote. There are examples of successful companies in both camps. It’s nonsense to claim that there is one optimal way of organising.

Based on what I’ve seen over the years, people I’ve talked to, and even a survey we did at a company I was once in, I’m pretty confident that the ‘in-person’ vs ‘remote’ debate is more about the sort of person you are and where you are in your life/career. Here are a few examples:

Prefers In-Person	Prefers Remote
Lives alone or in a small flatshare	Has a young family and a garden
Short commute	Long commute
Prefers informal chats / brainstorming	Prefers written communication
Feels that they get the most out of people if they are together	Values being able to access talent wherever it is in the world
Relatively junior / needs guidance	Has pre-existing relationships with co-workers / knows the ropes