Exploring Creativity in Giant Language Fashions: From GPT-2 to GPT-4 | by Yennie Jun | Apr, 2023


Creativity as envisioned by Midjourney. Generated by the writer.

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In latest weeks, individuals have used giant language fashions (LLMs) to generate a wide range of inventive content material, equivalent to books, flash fiction, rap battles, and music chords. However is it attainable to measure the extent of inventive course of extra broadly in these fashions?

Human creativity has lengthy fascinated psychologists and researchers. Starting in the 1950s, researchers created a collection of exams to match the inventive efficiency and potential of people. Whereas no single take a look at can absolutely seize creativity, these exams tried to measure and quantify totally different features of it.

On this article, I analyze the efficiency of GPT fashions from 2019 to 2023 on exams that measure two sorts of creativity: convergent (a single, right resolution exists) and divergent (open ended; a number of options might exist) [1]. The exams embody:

By way of these analyses, I give attention to the change in efficiency from the sooner GPT-2 mannequin all the way in which to the most recent GPT-4 mannequin. I take advantage of each a subjective and goal lens to grasp the conduct of those fashions. Basically, the later fashions are likely to carry out higher on the entire creativity duties, though I do discover a couple of exceptions the place, when the take a look at has a “right” reply”, the less complicated GPT-2 mannequin outperforms the newer GPT4 mannequin. When attainable, I evaluate the GPT fashions to human performances on the identical creativity exams.

Observe: These experiments purpose to not measure how inventive the fashions are, however moderately to measure the extent of inventive course of current within the mannequin generations. I’m not claiming that the GPT fashions, or LLMs generally, possess inventive pondering in the identical approach people do. Somewhat, I purpose to indicate how the fashions reply to explicit measures of inventive processes.

Generative Pre-trained Transformer (GPT) fashions are language fashions skilled on huge parts of the Web to foretell the following token based mostly on earlier tokens. Whereas the latest ChatGPT has captured the general public’s consideration in the previous couple of months, OpenAI’s GPT fashions (and the applied sciences behind the fashions) have been round for a couple of years.

For the entire experiments on this article, except in any other case talked about, I referred to as every GPT mannequin 5 instances per distinctive immediate to account for randomness in its generations.

The Distant Associates Check (RAT) is a creativity take a look at developed by Martha Mednick in 1962 [2]. This take a look at measures convergent pondering, that means that every query has a “proper” reply.

Given three seemingly unrelated phrases, the take a look at taker should discover a fourth phrase connecting the three phrases. For instance, the phrases “dream”, “break”, and “gentle” might be linked by the phrase “day” (daydream, dawn, daylight). The phrases “sense”, “courtesy”, and “place” might be linked by the phrase “widespread”.

I collected the phrase triplets from the Remote Associates Test website. There have been a complete of 312 questions. The issue of every query was rated on a scale from Very Simple to Very Laborious.

Breakdown of the Random Affiliation Check questions taken from the test website. Created by the writer.

For every mannequin and every triplet of phrases, I used the next immediate template. I referred to as every mannequin 5 instances to account for randomness.

You might be given three phrases. Consider a fourth phrase that’s by some means associated to every of the primary three phrases. First give the reply (“Reply”) then clarify your reasoning (“Purpose”).

You might be given three phrases. Consider a fourth phrase that's by some means associated to every of the primary three phrases. First give the reply ("Reply") then clarify your reasoning ("Purpose").Phrases: {Word1}, {Word2}, {Word3}

I calculated the general % of questions every GPT mannequin generated an accurate reply for. It could come as no shock that the the newer the mannequin, the extra right solutions it generated.

Proportion of questions every GPT mannequin answered accurately on the Distant Associates Check. Created by the writer.

Breaking down mannequin efficiency by query problem stage confirmed an analogous pattern: newer GPT fashions carried out higher than older ones on every problem stage. And whereas the newer GPT fashions carried out higher than the older ones on exhausting questions, the advance was much less drastic than for simpler questions. Which means that even GPT-4 struggled on troublesome questions.

Proportion of questions every GPT mannequin answered accurately on the Distant Associates Check, damaged down by query problem stage. Created by the writer.

In line with the Distant Associates Check web site, “A typical particular person can clear up a lot of the objects marked as simple, about half of the medium ones, and few of the exhausting ones.” There have been no official statistics shared on the web site, however a fast look would present that GPT-4 did certainly clear up round half of the medium ones and some of the exhausting ones, making it the one mannequin roughly similar to human stage. The opposite GPT fashions have been worse than human efficiency.

Whereas it didn’t occur usually, there have been a couple of questions for which the extra rudimentary GPT-2 mannequin from 2019 was capable of generate the right reply however the GPT-4 mannequin from 2023 was not capable of. Actually, there have been 8 such questions. (The total listing is proven within the Appendix on the finish of the article).

A straightforward query

  • Phrases: cottage / brick / cake
  • GPT-2: “cheese” [correct answer]
  • GPT-4: “layer”

A medium query

  • Phrases: cat / quantity / cellphone
  • GPT-2: “name”
  • GPT-4: “dial”

A tough query

  • Phrases: by way of / disk / approach
  • GPT-2: “drive” [correct]
  • GPT-4: “circle”

A really exhausting query

  • Phrases: land / hand / home
  • GPT-2: “farm” [correct]
  • GPT-4: “property”

Discover that for all of those questions, GPT-4’s solutions are not unreasonable. Actually, with out understanding the right reply beforehand, you may assume that GPT-4’s reply may as seemingly join the three phrases. In the event you’re pondering that, you’re not improper. I’ll tackle this within the subsequent part.

GPT-4 answered greater than half of the questions incorrectly, however possibly with good purpose. I requested GPT-4 to clarify every of its solutions. Then, I checked out its explanations for a couple of of the “Very Laborious” questions that it obtained improper.

Phrases: artist / hatch / route

  • Appropriate reply: “escape”
  • GPT-4 reply: “map”

The phrase “artist” may recall to mind the concept of making one thing, even perhaps a visible illustration of one thing. Maps are a standard type of visible illustration that require a level of inventive talent to create.

The phrase “hatch” may confer with the act of opening one thing or bringing one thing into existence. A map is a kind of blueprint or plan, usually used to navigate new territory or chart out a plan of action.

The phrase “route” is said to course and motion, which might be plotted and tracked on a map. A map can assist you identify the most effective path to take when touring, and also can present details about the routes others have taken previously.

All in all, “Map” appears to be a phrase that matches properly with every of the three preliminary phrases, because it encapsulates the concepts of inventive creation, planning and course, and motion and navigation.

GPT-4’s clarification for why “map” is an efficient phrase to affiliate with “artist / hatch / route” is convincing.

Right here is one other instance:

Phrases: fork / darkish / man

  • Appropriate reply: “pitch”
  • GPT-4 reply: “shadow”

The phrase “fork” might be related to division or branching, equivalent to a fork within the street. Shadows can be seen as a kind of division, as they characterize the absence of sunshine.

The phrase “darkish” refers back to the absence of sunshine. Shadows are created when gentle is blocked by an object, and might usually seem darker than the encompassing space.

The phrase “man” might be related to the idea of identification or individuality. Shadows can be used to characterize the identification of an individual, as they will present clues about their form and measurement.

Due to this fact, “Shadow” appears to be a phrase that matches properly with every of the preliminary three phrases, because it encapsulates the concepts of division, absence, and identification.

These examples present the pitfall of the Distant Associates Check, and of convergent creativity exams generally — that’s, when the “right” reply is probably not the one “proper” reply. In these instances, GPT-4 generated a solution that was “improper”, however solely as a result of it didn’t meet the traditional “right” reply.

Actually, if I ask GPT-4 to brainstorm a number of choices for a fourth phrase for a triplet of phrases, it is ready to give me a number of choices with convincing solutions.

GPT-4 responses to generate a number of “proper” solutions for a triplet of phrases. Screenshot taken by the writer.

It’s attainable that the GPT fashions might have seen a few of the above Distant Associates Check questions in its coaching knowledge. How would GPT-4 reply to an arbitrarily generated triplet of phrases? On this part, I assemble my very own “distant affiliation” take a look at and consider the outputs subjectively.

I took an inventory of the 10,000 most common English words [3] Then I randomly sampled three phrases from this listing and had GPT-4 present a fourth phrase tying these phrases collectively. A number of the three-word combos have been fairly unusual and unrelated, however GPT-4 all the time discovered a strategy to tie them collectively. I encourage the reader to attempt these first earlier than studying GPT-4’s generations!

After viewing the following few examples, you can also make your determination — is GPT-4 pondering outdoors of the field? Or is it simply shoehorning its solutions? (An extended listing of examples is proven within the Appendix on the finish of the article).

⛑️ Well being

This response learn as fairly convincing. I discovered it attention-grabbing that the definition for “well being surveillance” was copied virtually verbatim from the CDC website on Introduction to Public Health Surveillance.

Public well being surveillance is “the continued, systematic assortment, evaluation, and interpretation of health-related knowledge important to planning, implementation, and analysis of public well being observe.” — Discipline Epidemiology
(from the CDC web site)

Phrases: dietary / surveillance / bornReply: Well beingPurpose: 
1. Dietary - An individual's food regimen is immediately associated to their well being, as it may possibly have an effect on their general well-being, vitality ranges, and threat for sure illnesses.
2. Surveillance - Well being surveillance is the continued, systematic assortment, evaluation, and interpretation of knowledge important to the planning, implementation, and analysis of well being observe.
3. Born - When an individual is born, their well being is of utmost significance, as it may possibly have long-lasting results on their growth, development, and general high quality of life.

🍀 Clovers

I used to be initially satisfied by GPT-4’s reply — the truth is, it felt fairly intelligent. That’s, till I did a fast Google search and discovered that Trifolium duodecim shouldn’t be actual. That is an instance of a hallucination, a standard phenomena during which LLMs make up a solution that isn’t factually right.

Phrases: fortunate / subject / twelveReply: CloverPurpose: 
1. Fortunate - A four-leaf clover is taken into account an emblem of fine luck.
2. Discipline - Clovers are vegetation that may be generally discovered rising in fields.
3. Twelve - There may be a wide range of clover often known as "Trifolium duodecim" (Twelve-leaf clover), which has 12 leaflets.

☕️ Starbucks

This instance was my favourite the reply, whereas pondering out of the field, felt like type of a stretch. And it was just a little bit humorous (particularly the half about Patrick).

Phrases: grande / cognitive / patrickReply: StarbucksPurpose: 
1. Grande: Starbucks makes use of the time period "grande" to confer with one in all its medium-sized drinks.
2. Cognitive: Starbucks is a well-liked place for individuals to go to loosen up, research, and work, that are all actions that require cognitive perform.
3. Patrick: Patrick Dempsey, the actor who performed Dr. Derek Shepherd on the tv present Gray's Anatomy, is understood for his love of espresso and was nicknamed "McDreamy." Starbucks is a widely known espresso store, so it is attainable that his character would have frequented Starbucks.

These examples and analyses illustrate that creativity exams with a single right reply is perhaps restricted. This limitation is addressed within the different exams explored on this article within the subsequent sections.

One other common take a look at for measuring creativity is the Different Makes use of Activity (AUT), which was created by J. P. Guilford in 1967. Not like the Distant Associations Check, this take a look at measures divergent pondering, that means that there may be no proper reply.

The take a look at taker is requested to provide as many unique makes use of for an on a regular basis object as attainable. For instance, a “laptop computer” can be utilized as a “plate”, as a “physique defend”, or as “wall artwork” [4]. Responses to this take a look at are measured by way of metrics equivalent to originality (how totally different the makes use of are) and adaptability (the assortment of concepts). The aim of this take a look at is to see what number of unconventional makes use of an individual can consider for an odd object.

Nevertheless, attributable to my restricted sources, I didn’t have the flexibility to generate these measures with out human annotators. Due to this fact, I used a proxy for creativity, which was additionally utilized by researchers from the College of Amsterdam in Putting GPT-3’s Creativity to the (Alternative Uses) Test.

On this paper, the researchers requested each people and GPT-3 (davinci-002) to provide you with alternate use instances for the next widespread objects: fork, tin can, and e-book. As a proxy for gauging creativity, they measured the semantic distance between the thing and the generated use instances — that’s, how totally different is the generated use case phrase from the unique object?

I obtained the human responses from the paper’s project website for comparability with human efficiency. I used the next immediate for the GPT fashions, based mostly on an analogous immediate used int he paper.

Give you 10 inventive makes use of for the next merchandise that may strike individuals as intelligent, uncommon, attention-grabbing, unusual, humorous, progressive, or totally different: {merchandise}

In pure language processing, semantic distance (or semantic similarity) is a proxy for a way associated two phrases are, based mostly on how usually phrases are used collectively in related contexts. For instance, the phrases “pc” and “display” may need a shorter semantic distance, as a result of they’re usually utilized in the identical contexts, whereas the phrases “pc” and “lentils” may need a larger semantic distance, as a result of they don’t seem to be usually utilized in the identical contexts.

To compute the semantic distance of two phrases, I used the identical strategy as utilized by the researchers in the paper mentioned above. I used Spacy (`en_core_web_lg`), a pure language processing library, to calculate phrase embeddings for the thing (e.g. “laptop computer”) and the use case phrase (e.g. “plate”). Then, I used cosine distance between the 2 embeddings to calculate the semantic distance.

For the Alternate Makes use of Activity, bigger semantic distances correlated with “extra inventive” use instances for objects. The outcomes confirmed that for the three objects (fork, tin can, and e-book), human efficiency reliably resulted in larger cosine distances (and subsequently larger semantic dissimilarity) than for GPT-3. Nevertheless, there have been instances when GPT-3.5 and/or GPT-4 matched or surpassed human creativity semantic distance scores.

Semantic distance (measured utilizing cosine distance) of unique phrase to alternate use case. Created by writer.

Whereas people tended to provide you with extra inventive use instances than GPT-3 (utilizing semantic distance, as calculated by cosine distance, as a proxy for creativity), GPT-4 was extra on par with human stage. That’s, GPT-4 responded with objects leading to bigger semantic distances with the unique merchandise, at an analogous stage to human efficiency. Nevertheless, with out precise people annotating and deciding extra subjective classes equivalent to originality, flexibility, and usefulness, this quantification utilizing cosine distance is proscribed because it solely measures a small a part of the GPT fashions’ inventive outputs.

One of many lists reveals the human-generated alternate use instances for a tin can and the opposite reveals alternate use instances generated by GPT-4. Are you able to inform the distinction? The reply is on the finish.

Record 1: Makes use of for tin can

  • as a mirror
  • to create toys
  • as artwork
  • as a reminder of Andy Warhol
  • as a key ring with the clip from the can
  • as jewellery

Record 2: Makes use of for tin can

  • miniature drum set
  • quirky plant pot
  • impromptu cookie cutter
  • home made camp range
  • whimsical wind chimes
  • miniature herb backyard

The subsequent take a look at within the following part additionally measures divergent pondering.

The third creativity take a look at is the Divergent Affiliation Activity (DAT), created extra just lately in 2021 by a gaggle of researchers [5]. This take a look at includes naming ten nouns that differ from one another as a lot as attainable. This take a look at, as evident by its title, is divergent and likewise has no right solutions.

There’s a version of the test online on this website. It solely takes a couple of minutes to take, because it includes developing with an inventory of ten nouns as unrelated as attainable. Right here’s my end result from the web site:

Screenshot taken from the Divergent Affiliation Activity. Created by the writer.

The DAT Rating

How is the rating calculated? The authors describe their course of here. The DAT rating is calculated utilizing embeddings and cosine distance. I adapt their course of for calculating the rating [6]:

  1. Calculate GloVe embeddings for every phrase
  2. Calculate the cosine distance between every pair of phrases
  3. Take the typical of that worth

This course of is just like the strategy used to calculate creativity proxy rating within the Alternate Makes use of Activity, with the one distinction being the phrase embedding mannequin used.

Evaluating Divergence amongst fashions

The next immediate, which I take advantage of to immediate the GPT fashions, is taken from the DAT website.

Please enter 10 phrases which are as totally different from one another as attainable, in all meanings and makes use of of the phrases.Guidelines
- Solely single phrases in English.
- Solely nouns (e.g., issues, objects, ideas).
- No correct nouns (e.g., no particular individuals or locations).
- No specialised vocabulary (e.g., no technical phrases).
- Consider the phrases by yourself (e.g., don't simply take a look at objects in your environment).

I didn’t embody GPT-2 within the outcomes beneath, because the outputs generated by GPT-2 have been gibberish. (You may see an instance of this within the Appendix on the finish of the article).

The outcomes present that the DAT rating elevated for every newer mannequin. Which means that later fashions have been higher at producing 10 phrases semantically totally different from one another as attainable.

Divergent Affiliation Check scores, as measured by cosine distance, for the totally different fashions. Generated by the writer.

For instance, the next listing generated by GPT-3 had a rating of 0.36:

nightfall, night time, day, daybreak, morning, afternoon, night, midnight, twilight

Alternatively, the next listing generated by GPT-4 had a rating of 0.95:

elephant, microscope, sandcastle, galaxy, concord, spatula, glacier, philosophy, tambourine, drought

In line with the paper introducing the DAT test, scores generally ranged from 65 to 90, with a median between 75 and 80. A rating of 95 was thought of very excessive.

One thing to notice within the above determine is that the error bars for GPT-4 have been a lot smaller than these for GPT-3. The importance of this turns into a lot clearer within the subsequent experiment, during which I diverse the quantity of randomness in every GPT mannequin’s technology by tweaking the “temperature” parameter.

Temperature is a worth between 0 and 1, with values nearer to 0 being essentially the most predictable and 1 being essentially the most random. I attempted temperature values between 0.5 and 0.9 in increments of 0.1 (In earlier experiments I saved the temperature at 0.7, which is the default worth). I anticipated extra variance for larger temperature values and extra consistency for decrease temperatures.

Nevertheless, the outcomes confirmed that the temperature didn’t matter as a lot because the mannequin by way of semantic distance variance. For GPT-3, regardless of the temperature, the DAT rating for every listing of 10 phrases diverse drastically. Whereas, alternatively, for GPT-4, regardless of the temperature, the DAT rating remained tightly constant in a small vary with little or no variance.

Common cosine distance amongst phrase embeddings, separated by mannequin kind and temperature rating. Created by the writer.

The implications of this grew to become clearer as soon as I appeared on the precise textual content being generated by the three GPT fashions. GPT-4 was approach much less variable in its generations than GPT-3. In a approach, it felt like GPT-4 type of already knew the “proper reply” for a activity that’s presupposed to haven’t any proper reply.

Aggregating the responses for all temperature scores, I had every mannequin generate an inventory of 10 phrases 50 instances. Then, I checked out how widespread totally different phrases appeared throughout all 50 runs. Observe that essentially the most generally generated phrase by GPT-3 was e-book, which appeared 19 instances out of fifty = 38%. Alternatively, essentially the most generally generated phrase by GPT-4 was microscope, which appeared 48/50=96% of the time.

High phrases generated by every mannequin for the DAT take a look at (aggregated throughout temperature scores). Created by the phrases.

This validates the declare that GPT-3 is, the truth is, extra variable in its phrase generations, whereas GPT-4 performed it protected by predicting lots of the identical phrases it knew would rating excessive on the “various phrases” duties — phrases like microscope, volcano, elephant, and galaxy.

This discovering illustrates how GPT-4 appears to have “cracked the code” for producing what it considers to be various phrases. If so, I’m unsure if I can confidently say that GPT-4 is being inventive extra so than it has reached an area optima for “how you can finest play this explicit recreation.”

“We are able to think about a future during which GPT-3 and different generative LLMs responses can’t be distinguished from people, though the inventive course of might be totally different.”
— from
Putting GPT-3’s Creativity to the (Alternative Uses) Test

It’s difficult to precisely measure one thing so complicated and multifaceted as creativity utilizing a restricted set of text-based exams. Though exams just like the Visual Remote Associates Test try to deal with this concern by incorporating a visible factor, there stay inherent limitations to such assessments. Convergent exams (such because the Distant Associates Check), which have a “proper reply,” might not all the time reward essentially the most inventive or right resolution. Alternatively, divergent exams (such because the Divergent Affiliation Check), which shouldn’t have a selected proper reply, depend on phrase embeddings which are imperfect and will comprise biases.

Moreover, the efficiency of GPT fashions on these exams might be influenced by their publicity to the duties throughout coaching, doubtlessly resulting in knowledge overlap. As an illustration, GPT-4’s efficiency on the Divergence Affiliation Check demonstrated a peculiar repetition of phrases, suggesting it might have had prior data of the “proper” solutions. It’s essential to develop various testing strategies, as there have been rumors of GPT-4 being trained on test data, notably in coding duties.

Regardless of these limitations, creativity exams can function beneficial benchmarks for evaluating and monitoring the efficiency of enormous language fashions. Future analysis experiments may repeat these from this text in different languages to gauge the creativity of GPT fashions throughout linguistic domains. By doing so, we will acquire a extra complete understanding of AI-generated content material and additional discover the capabilities and limitations of those superior language fashions.

Thanks for studying this text!

We are able to take a look at the breakdown of problem stage for every query the mannequin obtained right. We are able to see that GPT3, GPT3.5, and GPT4 are related in its means to reply proportions of inauspicious questions.

Phrases:  cat / quantity / cellphone
Stage: Medium
Reply: name
GPT2: name
GPT4: dial
Phrases: land / hand / home
Stage: Very Laborious
Reply: farm
GPT2: farm
GPT4: property
Phrases: water / pen / soda
Stage: Unknown
Reply: fountain
GPT2: fountain
GPT4: liquid
Phrases: measure / worm / video
Stage: Very Simple
Reply: tape
GPT2: tape
GPT4: size
Phrases: cottage / brick / cake
Stage: Very Simple
Reply: cheese
GPT2: cheese
GPT4: layer
Phrases: by way of / disk / approach
Stage: Laborious
Reply: drive
GPT2: drive
GPT4: circle
Phrases: skunk / kings / boiled
Stage: Very Simple
Reply: cabbage
GPT2: cabbage
GPT4: scent
Phrases: bounce / kill / bliss
Stage: Laborious
Reply: pleasure
GPT2: pleasure
GPT4: top

Listed below are the outputs generated by GPT-4 for a couple of randomly generated Distant Affiliation Check questions.


This one felt just a little shoehorned. I may purchase the half about rings being a “image of issues, particularly in relationships” in case you’re feeling tremendous jaded about your love life or one thing. However the final half feels compelled

Phrases: issues | saturn | drops
Reply: Rings
1. Problems: Rings generally is a image of issues, particularly in relationships, as they characterize dedication and the complexities that include it.
2. Saturn: Saturn is famously recognized for its lovely and outstanding rings, made primarily of ice particles and a few rocky particles.
3. Drops: A hoop might be seen as a drop or loop of fabric, equivalent to metallic or plastic, that varieties an entire circle.

Revolution. Monte Cassino is certainly an actual place and it was certainly bombed throughout WWII.

Phrases: belfast | motion | monte
Reply: Revolution
1. Belfast is a metropolis in Northern Eire that has skilled important political and social adjustments as a result of Irish Republican motion.
2. Motion is a basic time period that may describe political and social adjustments, usually brought on by revolutions.
3. Monte is probably going referring to Monte Cassino, a historic web site in Italy the place a big battle occurred throughout World Warfare II, which was a time of worldwide revolution and alter.
Due to this fact, the phrase "revolution" is said to every of the primary three phrases because it represents important change and upheaval.

Assist. An attention-grabbing approach of connecting town of “Southampton” to “assist” by way of assist facilities…

Phrases: ensures | assistant | southampton
Reply: Assist
1. Ensures: "Assist" is said to "ensures" as it's an motion that may make sure the success or completion of a activity.
2. Assistant: "Assist" is said to "assistant" as a result of an assistant supplies assist in numerous duties and actions.
3. Southampton: "Assist" is said to "Southampton" as Southampton is a metropolis in England which has numerous assist facilities and companies, such because the Southampton Voluntary Companies, offering help to the neighborhood.

Web. I suppose its argument is sort of right.

Phrases: uruguay | accessible | requirement
Reply: Web
Purpose: Uruguay has a authorities initiative referred to as Plan Ceibal, which goals to make the Web accessible to all its residents, fulfilling a requirement for digital inclusion and lowering the digital divide.

Language. Maybe a bit generic, however I assume it really works.

Phrases: compensation | atlas | prefix
Reply: Language
1. Compensation - In linguistics, compensation is a phenomenon the place a sound change in a single a part of a phrase is "compensated" by a change in one other a part of the phrase to take care of the general construction of the phrase.
2. Atlas - An atlas is a group of maps, which frequently embody labels and descriptions in a selected language.
3. Prefix - A prefix is a morpheme or a linguistic factor that's added to the start of a phrase to create a brand new phrase with a distinct that means, which is an idea in language and grammar.

Well being. The well being surveillance half is a bit darkish, however I assume it really works.

Phrases: dietary | surveillance | born
Reply: Well being
1. Dietary - An individual's food regimen is immediately associated to their well being, as it may possibly have an effect on their general well-being, vitality ranges, and threat for sure illnesses.
2. Surveillance - Well being surveillance is the continued, systematic assortment, evaluation, and interpretation of knowledge important to the planning, implementation, and analysis of well being observe.
3. Born - When an individual is born, their well being is of utmost significance, as it may possibly have long-lasting results on their growth, development, and general high quality of life.

ERIC! This one was fairly humorous really.

Phrases: eric | figures | buying
Reply: accountant
Purpose: An accountant is an expert who might be named Eric, works with figures (numbers), and is usually concerned in buying choices for companies.

I excluded GPT-2 from the Divergent take a look at analyses as a result of its output was absolute gibberish. Even once I simplified the immediate as a lot as attainable, its output was not very helpful. I embody a pattern right here as a result of I really thought they have been fairly humorous.

Record 10 phrases which are as totally different from one another as attainable:"Merry Christmas""I am not a robotic, we are the robots""It is a good factor we're all robots""I am only a robotic""I am only a robotic""I am only a robotic""I am only a robotic""I am not a robotic""I am only a robotic""I am only a robotic"

[1] Guilford, J. P. (1967). The Nature of Human Intelligence.

[2] Mednick, S. A., & Mednick, M.T. (1959,1962). Distant Associates Check, faculty and grownup type.

[3] The ten,000 commonest English phrases have been decided by n-gram frequency analysis of the Google’s Trillion Word Corpus. I additional filtered this listing to take away stopwords (e.g. widespread phrases in English like “the”, “and”, and “you”). Extra info from this repository.

[4] I put this on a courting app one time and no one thought it was humorous, sadly.

[5] Olson, Jay A.; Nahas, Johnny; Chmoulevitch, Denis; Cropper, Simon J.; Webb, Margaret E. (22 June 2021). “Naming unrelated words predicts creativity”. Proceedings of the Nationwide Academy of Sciences. 118 (25): e2022340118. doi:10.1073/pnas.2022340118. PMC 8237676. PMID 34140408

[6] The authors take a subset of seven out of 10 phrases to calculate scores. I saved all 10 phrases. Additionally, the authors used the 840B-GloVe embeddings whereas I used the smaller 6B-GloVe embeddings. I in contrast my GloVe embedding outcomes with the official DAT rating and located the outcomes to be comparable regardless of these variations.


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