We present a new dataset of Wikipedia articles each paired with a knowledge graph, to facilitate the research in conditional text generation, graph generation and graph representation learning. Existing graph-text paired datasets typically contain small graphs and short text (1 or few sentences), thus limiting the capabilities of the models that can be learned on the data. Our new dataset WikiGraphs is collected by pairing each Wikipedia article from the established WikiText-103 benchmark (Merity et al., 2016) with a subgraph from the Freebase knowledge graph (Bollacker et al., 2008). This makes it easy to benchmark against other state-of-the-art text generative models that are capable of generating long paragraphs of coherent text. Both the graphs and the text data are of significantly larger scale compared to prior graph-text paired datasets. We present baseline graph neural network and transformer model results on our dataset for 3 tasks: graph -> text generation, graph -> text retrieval and text -> graph retrieval. We show that better conditioning on the graph provides gains in generation and retrieval quality but there is still large room for improvement.
Truck stuff – need to verify that they know it’s a 2016
Continuing to work on Svelte. Trying to get previous useful lessons to show up as pages, but they are svelte files, not HTML, so I’m not sure how to point to them
Scheduling. Orest wants to finish Oct 29, but we’re already a week into September, so I’m going to counter with Nov 5
Get slides done for Thurs meeting. Tried to get MARCOM to help with formatting, but the fuse is too short
Orest set up a meeting that conflicts with the GPT meeting. Trying to get him to move it, otherwise send a note that I will be about 15 min late
Go over untrained model results
See if we can make the chess models talk about having tea with the Queen
Neural networks have been adapted to leverage the structure and properties of graphs. We explore the components needed for building a graph neural network – and motivate the design choices behind them.
Working on tweaks for today’s meeting
Continue with Svelte
I seem to have been able to get typescript set up and running:
Which gives us this:
Work on finding a venue for the automating imagination paper
OED Definition of imagination:
The power or capacity to form internal images or ideas of objects and situations not actually present to the senses, including remembered objects and situations, and those constructed by mentally combining or projecting images of previously experienced qualities, objects, and situations. Also (esp. in modern philosophy): the power or capacity by which the mind integrates sensory data in the process of perception.
Also, using GNNs as ways of storing the relationships between the text generated by the GPT
No public health authority should rely on an AI system to make recommendations, of course. But as they grow in power and reach, AI systems could become another tool in leaders’ belts, allowing them to quickly parse existing scientific knowledge for insights that could help to guide in-the-moment decision-making. As the systems become better at citing their sources and explaining their output, their value as tools for guiding decision-making will only grow, because the validity of their predictions can be checked and vetted.
7:30 Meeting with Zach. I’m going to see if he agrees with the “front-end-first” approach I’d like to try. He agrees, so I’m working my way through the tutotial
To install a template project as per here, you have to use the git command line app
That creates the following structure:
Then to run the app, I use the terminal and use <ctrl> enter:
This handles hot deployment in the browser, so I think I’m doing it right?
Looking more deeply at Svelte and thinking about building a standalone frontend that doesn’t interact with websockets, but fakes the functionality so that when the Python connections are added in it works?