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Why Metas large language model can not work for researchers

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When Alan Turing developed the Turing Test in 1950, it had been a test of a machines ability toexhibit intelligent behaviorindistinguishable from that of a human. Turing proposed a computer could be thought to possess artificial intelligence (AI) if it could create human-like responses to questions.

Because of large language models, were now at the stage where computers can write text on virtually any subject we provide them with and generally, its very convincing and human-like.

Tell it to create a sentence on, How come Elon Musk prefer to knit? and what it outputs is arguably as effective as what any human could write:

Some possible explanations why Elon Musk might enjoy knitting could are the fact that this is a relaxing and meditative activity that will help to clear one's mind, looked after allows for a lot of creativity and self-expression.Additionally, knitting could be a very social activity, and Elon Musk may benefit from the possibility to chat and connect to other knitters.[Source: OpenAI Playground using text-davinci-002 model]

Summarizing complex text

Examples such as this are fun, however the bigger value proposition of using large language models is less about writing wacky prose and much more concerning the summarization of complex text. These use cases are exciting across industries. For example, AI can distill information regarding customers for sales intelligence purposes, or it could summarize investment documents in finance.

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However, what Im particularly thinking about is summarizing scientific papers for researchers.

The thing is there’s an overload of research being published all over the world. A lot more than 4.2 million academic research papers were published in 2020. Even within specialized fields of research, you can find a huge selection of papers published each year how do a researcher keep an eye on everything while pursuing their very own research? A papers abstract only hints at the study detail within.

When Meta recently open-sourced its language model, OPT-175B, it sounded promising for academic researchers. Its thought to offer better performance than OpenAIs GPT-3 and uses just 15% of GPT-3s compute resources to teach it.

Putting models to the test

Just how does Meta perform on scientific research?

We tested it first on a technical news release, which was the summary we received:

Forward Looking Statements, which speak only by the date of the news release. Artelo undertakes no obligation to publicly update any forward-looking statement, whether due to new information, future events or elsewhere.Investor Relations Contact:Westwicke Partners with respect to Artelo BiosciencesMike Piccinino, CFA

Its not just a great start. The model targets the investor legalese by the end of the news release, calculating that it’s the most crucial information readers have to know. You may argue that it is necessary, but its not what were searching for. The investor contact isnt even correct, it had been fabricated by the model!

Next, we tried it on a paper from the Journal of Electronic Materials:

Journal of ELECTRONIC MATERIALS, Vol. 38, No. 7, 2009 DOI: 10.1007/s11664-009-0701-y (cid:1) 2009 TMS Special Issue Paper thermally. The samples were then pulverized and analyzed by XRD, TGA, and XPS. The XRD patterns showed that CaCo2O4+d crystallizes as a calcium-ferrite-type structure, which includes a corner- and edge- shared CoO6 octahedron network including one-dimensional double chains. The CaCo2O4+d phase includes a nite density of states at the Fermi level. The XPS results showed that CaCo2O4+d includes a composition of CaCo2O4+d = (Ca2CoO3)0.62CoO2. The XPS results also showed that CaCo2O4+d has[Source: Meta OPT-175B]

Initially, it appears to possess important info, but theres clearly superfluous information like the information on the paper that arent vital that you the summary, and Id more accurately describe the effect as paraphrasing part of the text instead of summarizing all areas of it.

Small-scale models outperform

Just how does an inferior language model compare? Through experience in the field, we designed ourIris.ai IPG model to possess just 25 million parameters instead of 175 billion from Meta and OpenAI but trained it on an incredible number of domain-specific scientific articles. Our research shows that model performs very differently on a single paper:

Metallic temperature dependence of the seebeck coefficient s with a big thermoelectric power (s=151lv/kat387k) includes a finite density of states at the fermi level. In this paper, we report the crystal structure and physical properties of caco2o4+d. We look for a new compound caco2o4+d, which exhibits a big thermoelectric power, though it includes a finite density of states at the fermi level. Motivated by the easy guiding principle mentioned previously, we sought out new phases thermoelectric properties related as a thermoelectric material applicable to high-temperature use.[Source: Iris.ai IPG]

You can view the syntax is slightly more simplistic when compared to a large language model, however the information is a lot more relevant. Whats more, the computational costs to create that news article summary is significantly less than $0.23. To accomplish exactly the same on OPT-175 would cost about $180.

The container ships of AI models

Youd assume that large language models backed with enormous computational power, such as for example OPT-175B can process exactly the same information faster also to an increased quality. But where in fact the model falls down is in specific domain knowledge. It doesnt understand the structure of a study paper, it doesnt know very well what information is essential, also it doesnt understand chemical formulas. Its not the models fault it simply hasnt been trained with this information.

The answer, therefore, would be to just train the GPT model on materials papers, right?

Somewhat, yes. If we are able to train a GPT model on materials papers, then itll execute a good job of summarizing them, but large language models are by their nature large. They’re the proverbial container ships of AI models its very hard to improve their direction. This implies to evolve the model with reinforcement learning needs thousands of materials papers. Which is really a problem this level of papers simply doesnt exist to teach the model. Yes, data could be fabricated (since it often is in AI), but this reduces the standard of the outputs GPTs strength originates from all of the data its trained on.

Revolutionizing the how

For this reason smaller language models are better. Natural language processing (NLP) ‘s been around for years, and even though GPT models have hit the news, the sophistication of smaller NLP models is improving at all times.

In the end, a model trained on 175 billion parameters is definitely likely to be difficult to take care of, but a model using 30 to 40 million parameters is a lot more maneuverable for domain-specific text. The excess benefit is that it’ll use less computational power, so that it costs much less to perform, too.

From the scientific research viewpoint, that is what interests me most, AI will probably accelerate the prospect of researchers both in academia and in industry. The existing pace of publishing produces an inaccessible quantity of research, which drains academics time and companies resources.

Just how we designed Iris.ais IPG model reflects my belief that one models supply the opportunity not only to revolutionize what we study or how quickly we study it, but additionally how we approach different disciplines of scientific research all together. They provide talented minds a lot more time and resources to collaborate and generate value.

This prospect of every researcher to harness the worlds research drives me forward.

Victor Botev may be the CTO at Iris AI.

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