miket

Open source LLM that outperforms ChatGPT search mode, Gemini, and Perplexity

Here's a good way to identify how entity-dense your text is: https://demo.nl.diffbot.com/

Much easier to exit than standard vim!

Any question asking about the letters of words is bound to underwhelm because GPT3 is trained on sub-word tokens, so it does not have random access to individual letters. The word "prime" is tokenized as a single token, instead of 5 characters:

https://beta.openai.com/tokenizer

https://www.diffbot.com/products/extract/ works pretty well.

MediaWiki, the software that Wikipedia uses is open source and Wikibase, the software that WikiData uses is also open source

> To put it plainly, if you don't have enough data to train a machine learning model, what, exactly, are your options? There is only one option: to do the work by hand. Wikipedia, with its army of volunteers, has a much better shot at getting results this way than any previous effort.

The training data for machine translation models is also human-created. Given some fixed amount of human hours, would you rather them be spent annotating text that can train a translation system that can be used for many things, or a system that can just be used for this project? It all depends on the yield that you get per man-hour.

Hi, founder of Diffbot here, we are an AI research company spinout from Stanford that generate the world's largest knowledge graph from crawling the whole web. I didn't want to comment, but I see a lot of misunderstandings here about knowledge graphs, abstract representations of language, and the extent as to which this project uses ML.

First of all, having a machine-readable database of knowledge(i.e. Wikidata) is no doubt a great thing. It's maintained by a large community of human curators and always growing. However, generating actually useful natural language that rivals the value you get from reading a Wikipedia page from an abstract representation is problematic.

If you look at the walkthrough for how this would work (https://github.com/google/abstracttext/blob/master/eneyj/doc...), this project does not use machine and uses CFG-like production rules to generate natural sentences. Works great for generating toy sentences like "X is a Y".

However, human languages are not programming languages. Many natural languages, like German and Finnish, are so syntactically and morphologically complex that there is no compact ruleset that can describe them. (those that have taken grammar class can relate to the number of exceptions to the ruleset)

Additionally, not every sentence in a typical Wikipedia article can be easily represented in a machine-readable factual format. Plenty of text is opinion, subjective, or describes notions that don't have an proper entity. Of course there are ways that engineer around this, however they will exponential grow the complexity of your ontology, number of properties, and make for a terrible user experience for the annotators.

A much better and direct approach to the stated intention of making the knowledge accessible to more readers is to advance the state of machine translation, which would capture nuance and non-facts present in the original article. Additionally, exploring ML-based ways of NL generation from the dataset this will produce will have academic impact.

The article hardly supports its conclusion with these cherry-picked examples; however, the core reason these results don't meet the author's expectations is that Google's AI does not understand the content of webpages well enough to identify the publication date accurately (at least anywhere near as accurately as a human can). Google's publication date is based on whether it found changes to the HTML on its own crawl date (which is very noisy due to today's dynamically generated website) or based on schema.org/microdata, which as other commentators point it is game-able for purposes of SEO, or simply missing on most sites.

As a contrast, take a look at how Diffbot, an AI system that understands the content of the page by using computer vision and NLP techniques on it, interprets the page in question:

https://www.diffbot.com/testdrive/?url=https://www.reddit.co...

It can reliably extract the publication date on each post, without resorting to using site-specific rules. (You can try it on other discussion threads and article pages, that have a visible publication date).

When people think about using computers for Natural Language Processing, they often think about end-tasks like classification, translation, question answering, and models like BERT that model the statistical regularities in text. However, these tasks only measure indirectly how much the system has understood the meaning of the text, are largely unexplainable black boxes, and require reams of training data.

NLP is good enough that we can now explicitly measure how well a system reads text in terms of what knowledge is extracted from it. This task is called Knowledge Base Population, and we've released the first reproducible dataset called KnowledgeNet that measures this task, along with an open source state-of-the-art baseline.

Direct link to the Github repo: https://github.com/diffbot/knowledge-net EMNLP paper: https://www.aclweb.org/anthology/D19-1069.pdf

Seconded on Insomnia - I switched to it when encountering a bug in the encoding of the curl command generation in Postman, and haven't looked back.

I think there can very much be two segments. Both a high-volume self-service segment (I know this is what I prefer when evaluating developer tools) as well as a high-touch enterprise segment for training and implementation (think Bloomberg Terminal model). Diffbot has a free 14 day self-service evaluation for its individual extraction API, but it is not quite implemented yet for the Knowledge Graph.

Much of the knowledge that humans derive from reading text is implicit rather than explicit. The derived knowledge is also context-dependent and probabilistic, i.e. they are not binary facts but we assign a degree of confidence to them.

In the context of that sentence "She was a .. physicist and chemist who conducted .. research on radioactivity.", I think most people would say a physicist or a chemist who conducts research is a researcher. In other contexts, such as in your example, that would be a questionable inference. What you're describing is why natural language understanding is hard--it's context-dependent and not syntactic.

"She did research." http://relex.diffbot.com:8085/?text=She%20did%20research. "She was a researcher." http://relex.diffbot.com:8085/?text=She%20was%20a%20research....

Return different outputs from a state-of-the-art relation extraction system.

The initial example: http://relex.diffbot.com:8085/?text=Marie%20Curie%20was%20bo....

In your example, being a coder is not inferred: http://relex.diffbot.com:8085/?text=He%27s%20a%20hard-surfac....