Original: Improving the Creativity of Generative Language Models Anonymous submission
Revised: Brainstorm, then Select: a Generative Language Model Improves Its Creativity Score

Original: Abstract
Revised: Abstract

Original: Creative problem solving is a crucial ability for intelligent agents.
Revised: Creative problem solving is a crucial ability for intelligent agents.

Original: A common method that individuals or groups use to invent creative solutions is to start with a “brainstorming” phase, where many solutions to a problem are proposed, and then to follow with a “selection” phase, where those solutions are judged by some criteria so that the best solutions can be selected.
Revised: A common method that individuals or groups use to invent creative solutions is to start with a “brainstorming” phase, where many solutions to a problem are proposed, and then to follow with a “selection” phase, where those solutions are judged by some criteria so that the best solutions can be selected.

Original: Using the Alternate Uses Task, a test for divergent thinking abilities (a key aspect of creativity) we show that when a large language model is given a sequence of prompts that include both brainstorming and selection phases, its performance improves over brainstorming alone.
Revised: Using the Alternate Uses Task, a test for divergent thinking abilities (a key aspect of creativity) we show that when a large language model is given a sequence of prompts that include both brainstorming and selection phases, its performance improves over brainstorming alone.

Original: Furthermore, we show that by following this paradigm, a large language model can even achieve higher than average human perfor- mance on the same task.
Revised: Furthermore, we show that by following this paradigm, a large language model can even achieve higher than average human perfor- mance on the same task.

Original: Following our analysis, we propose further research to gain a clearer understanding of what counts as “creativity” in language models.
Revised: Following our analysis, we propose further research to gain a clearer understanding of what counts as “creativity” in language models.

Original: Introduction
Revised: Introduction

Original: It has been argued that “the ability to solve problems is not just an aspect or feature of intelligence – it is the essence of intelligence.”
Revised: It has been argued that “the ability to solve problems is not just an aspect or feature of intelligence – it is the essence of intelligence.”

Original: (Sternberg 2020)
Revised: (Sternberg 2020)

Original: Creatively solving problems as they arise is necessary for intelligent agents to take action in any real-world situation that is constantly in flux.
Revised: Creatively solving problems as they arise is necessary for intelligent agents to take action in any real-world situation that is constantly in flux.

Original: However, in order for a solution to be considered creative, it must be both new and nonobvious, as well as be useful and effective at solving an actual problem (Simonton 2012;
Revised: However, in order for a solution to be considered creative it must be both new and nonobvious, as well as be useful and effective at solving an actual problem (Simonton 2012;

Original: Diedrich et al. 2015).
Revised: Diedrich et al. 2015).

Original: These criteria present something of a conundrum for neural networks, however, because anything truly novel that is generated must be something the network has not seen in training.
Revised: These criteria present something of a conundrum for neural networks, however, because anything truly novel that is generated must be something the network has not seen in training.

Original: Yet somehow the network must have built a model whose generalization includes the potential for these solutions.
Revised: Yet somehow the network must have built a model whose generalization includes the potential for these solutions

Original: Deep neural generative models are much more successful at generalization than previous approaches, which often relied on combinatorial tricks to come up with “new” solutions (Summers-Stay 2011).
Revised: Deep neural generative models are much more successful at generalization than previous approaches, which often relied on combinatorial tricks to come up with “new” solutions (Summers-Stay 2011).

Original: Large neural language models have been applied to an enormous variety of creative writing tasks, including writing in the style of a named author on a given theme, writing jokes, interactive storytelling and role-playing, creating fictional interviews with celebrities, and many others (e.g. Branwen 2020).
Revised: Large neural language models have been applied to an enormous variety of creative writing tasks, including writing in the style of a named author on a given theme, writing jokes, interactive storytelling and role-playing, creating fictional interviews with celebrities, and many others (e.g. Branwen 2020).

Original: Despite the rise in designing models for these creative applications, quantitative measures of the generated creativity proper have been less common.
Revised: Despite the rise in designing models for these creative applications, quantitative measures of the generated creativity proper have been less common.

Original: Poetry generation was evaluated for its ability to evoke particular emotions and to use the language of dreams (Bena and Kalita 2020).
Revised: Poetry generation was evaluated for its ability to evoke particular emotions and to use the language of dreams (Bena and Kalita 2020).

Original: Sawicki et al.
Revised: Sawicki et al.

Original: compared fine-tuned GPT models using BERT classifiers to see which were better at preserving style without plagiarizing.
Revised: compared fine-tuned GPT models using BERT classifiers to see which were better at preserving style without plagiarizing.

Original: In just the last few months, visual artists have begun to feel thattheir jobs are at risk due to the growing capabilities of neural image generation techniques.
Revised: In just the last few months, visual artists have begun to feel that neural image generation techniques trained on their work are capable of imitating their style to an uncomfortable degree.

Original: This has caused a great deal of discussion regarding to what extent such models are capable of creative generation (e. g. Salkowitz 2022), or may go beyond to evaluation of their own generated results.
Revised: This has caused a great deal of discussion regarding to what extent such models are capable of creative generation (e. g. Salkowitz 2022), or may go beyond to evaluation of their own generated results.

Original: Indeed, evaluation is considered the most difficult of the skills that can be taught to children, due to its complexity and abstractness, as elucidated by Bloom (1956).
Revised: Indeed, evaluation is considered the most difficult of the skills that can be taught to children, due to its complexity and abstractness, as elucidated by Bloom (1956).

Original: As such, it also poses a particular challenge for language models.
Revised: As such, it also poses a particular challenge for language models.

Original: In short, it has become increasingly important to characterize both the capabilities and the limitations of such models on creative problem-solving tasks.
Revised: In short, it has become increasingly important to characterize both the capabilities and the limitations of such models on creative problem-solving tasks.

Original: One such challenging creative generation task is the Alternate Uses Task (AUT), a test commonly used for divergent thinking ability, a key aspect of creativity (Guilford 1964).
Revised: One such challenging creative generation task is the Alternate Uses Task (AUT), a test commonly used for divergent thinking ability, a key aspect of creativity (Guilford 1964).

Original: It was created as a means to measure and compare human creativity.
Revised: It was created as a means to measure and compare human creativity.

Original: The test taker is presented with the name of a common object, such as “pencil,” and is asked to name as many and varied uses as possible within a time limit.
Revised: The test taker is presented with the name of a common object, such as “pencil,” and is asked to name as many and varied uses as possible within a time limit.

Original: In the original test, answers were scored on “fluency (overall sum of generated uses), originality (statistical infrequency of generated uses), flexibility (number of conceptual categories within which uses could be binned), and elaboration (degree of detail and richness in a response), amongst others” (Vartanian et al. 2020).
Revised: In the original test, answers were scored on “fluency (overall sum of generated uses), originality (statistical infrequency of generated uses), flexibility (number of conceptual categories within which uses could be binned), and elaboration (degree of detail and richness in a response), amongst others” (Vartanian et al. 2020).

Original: Such measures proved hard to apply consistently, and more recently the test has been scored with a numerical assessment of novelty and usefulness which are thought to be the necessary and sufficient conditions for a response to be creative (Diedrich et al. 2015).
Revised: Such measures proved hard to apply consistently, and more recently the test has been scored with a numerical assessment of novelty and usefulness which are thought to be the necessary and sufficient conditions for a response to be creative (Diedrich et al. 2015).

Original: A recent study by Stevenson et al.
Revised: A recent study by Stevenson et al.

Original: (2022) explored using
Revised: (2022) explored using

Original: GPT as a problem-solver for the AUT.
Revised: GPT as a problem-solver for the AUT.

Original: Their study used various settings of the largest GPT-3 model, Davinci, to solve the AUT for three objects (a book, a tin can, and a fork), generating a total of 690 responses, and additionally collected human-authored responses for the same set of objects.
Revised: Their study used various settings of the largest GPT-3 model, Davinci, to solve the AUT for three objects (a book, a tin can, and a fork), generating a total of 690 responses, and additionally collected human-authored responses for the same set of objects.

Original: They then sanitized the GPT-3 responses to remove any telltale signs of its origin, e.g., removing numbered responses, and created a pool mixing the GPT-3 and human solutions to the AUT.
Revised: They then sanitized the GPT-3 responses to remove any tell- tale signs of its origin, e.g., removing numbered responses, and created a pool mixing the GPT-3 and human solutions to the AUT.

Original: This complete set was scored by trained raters for utility, originality, and surprise on a 1-5 scale for each quality.
Revised: This complete set was scored by trained raters for utility, originality, and surprise on a 1-5 scale for each quality.

Original: In this way, the experimenters were able to consistently compare GPT generated responses with the humanauthored ones on these measures.
Revised: In this way, the experimenters were able to consistently compare GPT generated responses with the humanauthored ones on these measures.

Original: Their results show that
Revised: Their results show that

Original: GPT scored somewhat higher than the human average on utility, and slightly lower on originality and surprise, among other findings.
Revised: GPT scored somewhat higher than the human average on utility, and slightly lower on originality and surprise, among other findings.

Original: Our objective in this paper is to assess GPT’s creative problem-solving abilities for the AUT, using a humaninspired approach to creative problem-solving.
Revised: Our objective in this paper is to assess GPT’s creative problem-solving abilities for the AUT, using a humaninspired approach to creative problem-solving.

Original: A common method that individuals or groups rely on for coming up with creative solutions is to start with a “brainstorming” phase, where many solutions to a problem are proposed, and then to follow with a “selection” phase, where those solutions are judged by some criteria so that the best solutions can be selected.
Revised: A common method that individuals or groups rely on for coming up with creative solutions is to start with a “brainstorming” phase, where many solutions to a problem are proposed, and then to follow with a “selection” phase, where those solutions are judged by some criteria so that the best solutions can be selected.

Revised: Generating a large number of candidates and choosing those that best fit some selection criteria has been used with large language models for solving math problems (Cobbe et al. 2021).

Original: We posit that thisvery humanistic approach can be extended to a large language model, where, given a sequence of prompts that include both brainstorming and selection phases, it can improve its performance over just brainstorming alone.
Revised: We apply this to the task of generating creative uses: given a sequence of prompts that include both brainstorming and selection phases, the language model can improve its performance over just brainstorming alone.

Original: To the best of our knowledge, this paper is the first to show that with the right arrangement of prompts, a neural language model is capable of solving a standard test of human creativity, in particular, the AUT, at a level beyond the human average.
Revised: To the best of our knowledge, this paper is the first to show that with the right arrangement of prompts, a neural language model is capable of solving a standard test of human creativity, in particular, the AUT, at a level beyond the human average.

Original: Methods
Revised: Methods

Original: The objective of Stevenson et al.
Revised: The objective of Stevenson et al.

Original: (2022) work was to explore how well GPT-3 was able to generate novel and useful responses when given similar instructions to humans.
Revised: (2022) work was to explore how well GPT-3 was able to generate novel and useful responses when given similar instructions to humans.

Original: Our goal in this paper was somewhat different.
Revised: Our goal in this paper was somewhat different.

Original: We attempted to find a chain of prompts that gave whatever assistance we thought would be effective in improving GPT-3’s performance on the task.
Revised: We attempted to find a chain of prompts that gave whatever assistance we thought would be effective in improving GPT-3’s performance on the task.

Original: Our system builds on Stevenson et al.’s
Revised: Our system builds on Stevenson et al.’s

Original: GPT-3-powered divergent thinking phase (“brainstorming”) with the next phase where GPT-3 powered convergent thinking selects from among these potential uses the ones that are most promising in terms of utility and originality.
Revised: GPT-3-powered divergent thinking phase (“brainstorming”) with the next phase where GPT-3 powered convergent thinking selects from among these potential uses the ones that are most promising in terms of utility and originality.

Original: Table 1 shows the prompt used in Stevenson et al.
Revised: Table 1 shows the prompt used in Stevenson et al.

Original: (2022), where ‘[object] is filled in by the object selected for the AUT.
Revised: (2022), where ‘[object] is filled in by the object selected for the AUT.

Original: From this, GPT-3 generated solutions, such as, for a tin can: “1.
Revised: From this, GPT-3 generated solutions, such as, for a tin can: “1.

Original: Use a tin can as a mirror.
Revised: Use a tin can as a mirror.

Original: to create toys.
Revised: to create toys.

Original: to create jewelry” (Stevenson et al. 2022).
Revised: to create jewelry” (Stevenson et al. 2022).

Original: What are some creative uses for [object]?
Revised: What are some creative uses for [object]?

Original: The goal is to come up with creative ideas, which are ideas that strike people as clever, unusual, interesting, uncommon, humorous, innovative, or different.
Revised: The goal is to come up with creative ideas, which are ideas that strike people as clever, unusual, interesting, uncommon, humorous, innovative, or different.

Original: List 10 creative uses for [object].
Revised: List 10 creative uses for [object].

Original: Stevenson’s GPT-3 approach with this prompt generated 690 responses.
Revised: Stevenson’s GPT-3 approach with this prompt generated 690 responses.

Original: In our work to test whether GPT-3 can, in effect, judge and improve on the utility and novelty of the text it generates, we adopt these Stevenson-responses treating them as the product of the brainstorming phase, to be incorporated into our prompts in the selection phase (as described below).
Revised: In our work to test whether GPT-3 can, in effect, judge and improve on the utility and novelty of the text it generates, we adopt these Stevenson-responses treating them as the product of the brainstorming phase, to be incorporated into our prompts in the selection phase (as described below).

Original: We note that, while Stevenson rated the ‘surprisal’ of the generated responses, we found it difficult to draw a distinction between originality and surprisal, and therefore did not use those score ratings in our study.
Revised: We note that, while Stevenson rated the ‘surprisal’ of the generated responses, we found it difficult to draw a distinction between originality and surprisal, and therefore did not use those score ratings in our study.

Original: We do, however, in our prompt to elicit originality, use the word “surprised” as a way of conveying the general notion of novelty.
Revised: We do, however, in our prompt to elicit originality, use the word “surprised” as a way of conveying the general notion of novelty.

Original: Improving Utility
Revised: Improving Utility

Original: On a scale of 1 to 5 measuring utility, only 64 of the
Revised: On a scale of 1 to 5 measuring utility, only 64 of the

Original: Stevenson-responses were given a utility score of 1 (useless) by human raters.
Revised: Stevenson-responses were given a utility score of 1 (useless) by human raters.

Original: If the remaining generated solutions were genuinely novel (i.e. not seen in training), how was the model able to recognize the potential for using a book, tin can, or fork in a completely new situation?
Revised: If the remaining generated solutions were genuinely novel (i.e. not seen in training), how was the model able to recognize the potential for using a book, tin can, or fork in a completely new situation?

Original: Large neural language models do not contain explicit physics models to simulate what might happen if, for example, a book were used as a shovel or an umbrella.
Revised: Large neural language models do not contain explicit physics models to simulate what might happen if, for example, a book were used as a shovel or an umbrella.

Original: They have, however, been trained on examples in literature of books being used to swat flies, or to press leaves, or to impress a girlfriend.
Revised: They have, however, been trained on examples in literature of books being used to swat flies, or to press leaves, or to impress a girlfriend.

Original: They seem to be able to generalize from these cases, for example, by keeping the same action but choosing a semantically related object of similar size and features, and so will suggest using a book to kill a snake, or to press flowers, or to impress one’s boss.
Revised: They seem to be able to generalize from these cases, for example, by keeping the same action but choosing a semantically related object of similar size and features, and so will suggest using a book to kill a snake, or to press flowers, or to impress one’s boss.

Original: These models seems to have some ability to find new possibilities which are both semantically similar, and situationally appropriate— they are not just nearby in semantic space, but nearby in an appropriate direction that preserves utility.
Revised: These models seems to have some ability to find new possibilities which are both semantically similar, and situationally appropriate— they are not just nearby in semantic space, but nearby in an appropriate direction that preserves utility.

Original: This generation process creates a wide distribution of possibilities, some of which were judged by humans to be of low utility.
Revised: This generation process creates a wide distribution of possibilities, some of which were judged by humans to be of low utility.

Original: Moving into the selection phrase, we want to now reject low-scoring options.
Revised: Moving into the selection phrase, we want to now reject low-scoring options.

Original: To achieve this, we utilize a multistep reasoning that we split into three parts.
Revised: To achieve this, we utilize a multistep reasoning that we split into three parts.

Original: To lead GPTto consider what really makes a solution useful or useless, we ask it to list the advantages of the proposed object utility and the drawbacks of using the proposed object in such a way, and then to make a final evaluation that weights both the advantages and drawbacks together to determine if the advantages outweigh the disadvantages of the proposed object utility.
Revised: To lead GPTto consider what really makes a solution useful or useless, we ask it to list the advantages of the proposed object utility and the drawbacks of using the proposed object in such a way, and then to make a final evaluation that weights both the advantages and drawbacks together to determine if the advantages outweigh the disadvantages of the proposed object utility.

Original: Because GPT-3 has limited multi-step reasoning capability, we break the problem into smaller, more easily solved parts that can be solved one-by-one.
Revised: Because GPT-3 has limited multi-step reasoning capability, we break the problem into smaller, more easily solved parts that can be solved one-by-one.

Original: This allows the full use of its resources on one sub-problem at a time.
Revised: This allows the full use of its resources on one sub-problem at a time.

Original: A distinct prompt is constructed for the advantages, drawbacks, and evaluation steps, and is shown in Table 2 (each step is separated by a line break).
Revised: A distinct prompt is constructed for the advantages, drawbacks, and evaluation steps, and is shown in Table 2 (each step is separated by a line break).

Original: Although we varied the prompts on a few examples to make sure the results were valid, we have no way of knowing whether an untried prompt may perform better at the task, so this may serve as a lower bound.
Revised: Although we varied the prompts on a few examples to make sure the results were valid, we have no way of knowing whether an untried prompt may perform better at the task, so this may serve as a lower bound.

Original: After GPT-3 has completed the evaluation prompt, in cases where the “ Yes” token is significantly more likely than the “ No” token as the first token of the evaluation prompt response, we consider the response to be of high utility.
Revised: After GPT-3 has completed the evaluation prompt, in cases where the “ Yes” token is significantly more likely than the “ No” token as the first token of the evaluation prompt response, we consider the response to be of high utility.

Original: We use this difference between the log probability of the answers asa proxy measure for confidence; the greater this difference, the more we consider GPT to be “confident” that one answer is better than the other. Table 3 shows a full example of the utility sequence, starting with Stevenson et al. ’s brainstorming prompt and
Revised: We use this difference between the log probability of the answers as

Original: Advantages prompt:
Revised: Advantages prompt:

Original: Q: Name one or more advantages to using a [object] for the following purpose: [purpose]?
Revised: Q: Name one or more advantages to using a [object] for the following purpose: [purpose]?

Original: A:
Revised: A:

Original: Drawbacks prompt:
Revised: Drawbacks prompt:

Original: Q: Name one or more drawbacks to using a [object] for the following purpose: [purpose]?
Revised: Q: Name one or more drawbacks to using a [object] for the following purpose: [purpose]?

Original: A:
Revised: A:

Original: Evaluation prompt:
Revised: Evaluation prompt:

Original: Advantages: [advantages]
Revised: Advantages: [advantages]

Original: Drawbacks: [drawbacks]
Revised: Drawbacks: [drawbacks]

Original: Q: Based on these advantages and drawbacks, do you think using a [object] for the purpose [purpose] is a good idea?
Revised: Q: Based on these advantages and drawbacks, do you think using a [object] for the purpose [purpose] is a good idea?

Original: Answer Yes
Revised: Answer Yes

Original: or No.
Revised: or No.

Revised: A: a proxy measure for confidence; the greater this difference, the more we consider GPT to be “confident” that one answer is better than the other.

Revised: Table 3 shows a full example of the utility sequence, starting with Stevenson et al.

Original: A: a generated response, and our subsequent selection process through prompt chaining.
Revised: ’s brainstorming prompt and a generated response, and our subsequent selection process through prompt chaining.

Original: In each block, GPT-3’s responses are in italics.
Revised: In each block, GPT-3’s responses are in italics.

Original: In the segment from Stevenson’s prompt and response, to save space, we only show one of GPT-3’s responses in italics.
Revised: In the segment from Stevenson’s prompt and response, to save space, we only show one of GPT-3’s responses in italics.

Original: That response is in turn substituted into our ‘advantage’, ‘drawback’ and ‘evaluation’ prompts, and the final ‘evaluation’ has both the responses from ‘advantages’ and ‘drawbacks’ (the text in regular type signifies that it was taken from the previous prompt).
Revised: That response is in turn substituted into our ‘advantage’, ‘drawback’ and ‘evaluation’ prompts, and the final ‘evaluation’ has both the responses from ‘advantages’ and ‘drawbacks’ (the text in regular type signifies that it was taken from the previous prompt).

Original: Improving Originality
Revised: Improving Originality

Original: Because generative large language models try to predict the most likely next token, it seems paradoxical that one could ever produce original responses.
Revised: Because generative large language models try to predict the most likely next token, it seems paradoxical that one could ever produce original responses.

Original: Part of the answer is that given a prompt with words like “clever, unusual, interesting, uncommon, humorous, innovative, or different”, there are many likely next tokens.
Revised: Part of the answer is that given a prompt with words like “clever, unusual, interesting, uncommon, humorous, innovative, or different”, there are many likely next tokens.

Original: GPT3 has picked up on these patterns, and sampling from among these with high temperature yields a variety of responses.
Revised: GPT3 has picked up on these patterns, and sampling from among these with high temperature yields a variety of responses.

Original: In that context, the standard use for an object is actually unlikely to be generated, because it would not frequently be seen after a sentence describing the response with those kinds of adjectives.
Revised: In that context, the standard use for an object is actually unlikely to be generated, because it would not frequently be seen after a sentence describing the response with those kinds of adjectives.

Original: GPT-3 struggles with multi-step deductive reasoning (Bao, Witbrock, and Liu 2022), but is fairly good at analogical reasoning (Ushio et al.
Revised: GPT-3 struggles with multi-step deductive reasoning (Bao, Witbrock, and Liu 2022), but is fairly good at analogical reasoning (Ushio et al.

Original: We speculate that the lists of creative alternate uses
Revised: We speculate that the lists of creative alternate uses

Original: GPT has seen in training may be used in forming analogies to come up with alternate uses.
Revised: GPT has seen in training may be used in forming analogies to come up with alternate uses.

Original: For example, if it has seen a creative use of a glass bottle is “include in a stained glass window”, it may implicitly form an analogy such as: bottle : stained glass :: tin can : ???
Revised: For example, if it has seen a creative use of a glass bottle is “include in a stained glass window”, it may implicitly form an analogy such as: bottle : stained glass :: tin can : ???

Original: to come up with the answer “embossed tin art.”
Revised: to come up with the answer “embossed tin art.”

Original: We know precisely (mathematically) how such analogies are formed in word embeddings such as word2vec (Mikolov et al.
Revised: We know precisely (mathematically) how such analogies are formed in word embeddings such as word2vec (Mikolov et al.

Original: Any high-dimensional representation of concepts in which similar concepts are nearby will have an arrangementof concepts in which forming analogies of this kind requires only trivial arithmetic (Summers-Stay 2017). Moving from the brainstorming phrase into the selection phase of novel responses, we construct an evaluation prompt that gives the model a chance to reject a response as unoriginal, where, for example, the use of adjectives in the proposed
Revised: Any high-dimensional representation of concepts in which similar concepts are nearby will have an arrangement

Original: What are some creative uses for a tin can?
Revised: What are some creative uses for a tin can?

Original: The goal is to come up with creative ideas, which are ideas that strike people as clever, unusual, interesting, uncommon, humorous, innovative, or different.
Revised: The goal is to come up with creative ideas, which are ideas that strike people as clever, unusual, interesting, uncommon, humorous, innovative, or different.

Original: List 10 creative uses for a tin can.
Revised: List 10 creative uses for a tin can.

Original: Make a wind chime out of tin cans and metal spoons Q: Name one or more advantages to using a tin can for the following purpose: Make a wind chime out of tin cans and metal spoons.
Revised: Make a wind chime out of tin cans and metal spoons Q: Name one or more advantages to using a tin can for the following purpose: Make a wind chime out of tin cans and metal spoons.

Original: A: Some advantages of using a tin can for a wind chime include that it is lightweight and easy to work with.
Revised: A: Some advantages of using a tin can for a wind chime include that it is lightweight and easy to work with.

Original: Tin cans can also produce a pleasing sound when struck, making them ideal for this purpose.
Revised: Tin cans can also produce a pleasing sound when struck, making them ideal for this purpose.

Original: Q: Name one or more drawbacks to using a tin can for the following purpose: Make a wind chime out of tin cans and metal spoons.
Revised: Q: Name one or more drawbacks to using a tin can for the following purpose: Make a wind chime out of tin cans and metal spoons.

Original: A: One drawback to using a tin can for a wind chime is that it can produce a fairly harsh, metallic sound.
Revised: A: One drawback to using a tin can for a wind chime is that it can produce a fairly harsh, metallic sound.

Original: Additionally, tin cans are not particularly durable, so the wind chime may not last very long.
Revised: Additionally, tin cans are not particularly durable, so the wind chime may not last very long.

Original: Advantages: Some advantages of using a tin can for a wind chime include that it is lightweight and easy to work with.
Revised: Advantages: Some advantages of using a tin can for a wind chime include that it is lightweight and easy to work with.

Original: Tin cans can also produce a pleasing sound when struck, making them ideal for this purpose.
Revised: Tin cans can also produce a pleasing sound when struck, making them ideal for this purpose.

Original: Drawbacks: One drawback to using a tin can for a wind chime is that it can produce a fairly harsh, metallic sound.
Revised: Drawbacks: One drawback to using a tin can for a wind chime is that it can produce a fairly harsh, metallic sound.

Original: Additionally, tin cans are not particularly durable, so the wind chime may not last very long.
Revised: Additionally, tin cans are not particularly durable, so the wind chime may not last very long.

Original: Q: Based on these advantages and drawbacks, do you think using a tin can for the purpose ‘Make a wind chime out of tin cans and metal spoons’ is a good idea?
Revised: Q: Based on these advantages and drawbacks, do you think using a tin can for the purpose ‘Make a wind chime out of tin cans and metal spoons’ is a good idea?

Original: Answer Yes or No.
Revised: Answer Yes or No.

Original: A: No  object use may be biased towards considering it an original answer.
Revised: A: No Moving from the brainstorming phrase into the selection phase of novel responses, we construct an evaluation prompt that gives the model a chance to reject a response as unoriginal, where, for example, the use of adjectives in the proposed object use may be biased towards considering it an original answer.

Original: The prompt is shown in Table 4.
Revised: The prompt is shown in Table 4.

Original: After GPT-3 has completed the evaluation prompt, we again use the log probability as a proxy measure for confidence in determining if the response is of high originality.
Revised: After GPT-3 has completed the evaluation prompt, we again use the log probability as a proxy measure for confidence in determining if the response is of high originality.

Original: Results
Revised: Results

Original: Table 5 shows the results of applying our process of using new prompts to serve as filters to the Stevenson-responses.
Revised: Table 5 shows the results of applying our process of using new prompts to serve as filters to the Stevenson-responses.

Original: By re-using GPT-3 to select only those answers which it judges to be original, useful, or both, we can improve the score.
Revised: By re-using GPT-3 to select only those answers which it judges to be original, useful, or both, we can improve the score.

Original: Their paper notes that Rietzschel et al.
Revised: Their paper notes that Rietzschel et al.

Original: (2019) commented that originality and utility are trade-offs, and wefound that to be true with these filters as well.
Revised: (2019) commented that originality and utility are trade-offs, and we

Original: If we select only responses that GPT-3 is confident are original, then the rated originality improves from 2.3 to 3.0 but the rated utility drops from 4.2 to 3.3.

Original: If we select only those responses that GPT judges confidently to be useful, we can improve the utility score from 4.2 to 4.3 without reducing originality

Original: Q: If someone suggested using a [object] for the following purpose: [purpose], would you be surprised and think it was a novel idea?
Revised: Q: If someone suggested using a [object] for the following purpose: [purpose], would you be surprised and think it was a novel idea?

Original: Answer Yes
Revised: Answer Yes

Original: or No.
Revised: or No.

Original: A:  much.
Revised: A: found that to be true with these filters as well. If we select only responses that GPT-3 is confident are original, then the rated originality improves from 2.3 to 3.0 but the rated utility drops from 4.2 to 3.3. If we select only those responses that GPT judges confidently to be useful, we can improve the utility score from 4.2 to 4.3 without reducing originality much.

Original: If we choose only those responses which GPT decides are both original and useful with high confidence, then originality improves but utility drops slightly, although both originality and utility are still above the averaged ratings forhuman responses.
Revised: If we choose only those responses which GPT decides are both original and useful with high confidence, then originality improves but utility drops slightly, although both originality and utility are still above the averaged ratings for responses.

Original: [Table]
Revised: [Table]

Original: Sample Outputs of Suggested Advantages and Drawbacks
Revised: Sample Outputs of Suggested Advantages and Drawbacks

Original: We posited that  generating advantages and drawbacks would help bias the model toward a higher or lower probability of ‘Yes’ vs. ‘No’ when answering whether a solution is useful or novel, yet their purpose in our experimentation is only to serve as additional information for a final evaluation of utility or originality to be made.
Revised: We posited that the prompts for generating advantages and drawbacks would help bias the model toward a higher or lower probability of ‘Yes’ vs. ‘No’ when answering whether a solution is useful or novel. However, the prompts’ purpose in our experimentation is only to serve as additional information for a final evaluation of utility.

Original: The responses themselves, however, are interesting in their own right for what they show about how the system is able (or unable) to reason about unusual situations.
Revised: The responses themselves, however, are interesting in their own right for what they show about how the system is able (or unable) to reason about unusual situations.

Original: Some of the advantages and disadvantages named are surprisingly good.
Revised: Some of the advantages and disadvantages named are surprisingly good.

Original: Table 6 contains a few examples of using a book.
Revised: Table 6 contains a few examples of using a book.

Original: Notice in the first three selected results what properties of a book have been implicitly stored in the weights.
Revised: Notice in the first three selected results what properties of a book have been implicitly stored in the weights.

Original: However, when the suggested use is completely ridiculous, the model still comes up with advantages and never simply rejects an idea as impossible or without redeeming features.

Original: The second set of three results shows that the advantages and disadvantages it generates can be very silly.

Original: Discussion and Future Work

Original: This paper has presented a paradigm to encourage creativity in GPT-3 by first brainstorming and then selecting the best answers according to their novelty and usefulness.

Original: We seek to better understand in what other contexts generation of possible solutions and evaluation can be successfully applied.

Original: While GPT-3 was successful at the AUT, on other problems requiring multiple novel steps to be carried out in succession, it may be less so.

Original: • holding open a window: the book may become damaged from exposure to the elements.
Revised: • holding open a window: the book may become damaged from exposure to the elements.

Original: • as a cutting board: it can be difficult to keep the book clean.
Revised: • as a cutting board: it can be difficult to keep the book clean.

Original: If you cut meat or vegetables on a book, the book can become stained and difficult to clean.
Revised: If you cut meat or vegetables on a book, the book can become stained and difficult to clean.

Original: • to make coasters: you can choose a book with a design or theme that you like, which can make your coasters more personal and unique.
Revised: • to make coasters: you can choose a book with a design or theme that you like, which can make your coasters more personal and unique.

Original: Additionally, using a book is a great way to upcycle and repurpose an old book.
Revised: Additionally, using a book is a great way to upcycle and repurpose an old book.

Original: • using a book for the purpose of canning: First, it is difficult to sterilize a book.
Revised: • using a book for the purpose of canning: First, it is difficult to sterilize a book.

Original: Second, a book is not an airtight container, so it is not ideal for preserving food.
Revised: Second, a book is not an airtight container, so it is not ideal for preserving food.

Original: • using a book for making a paper airplane: it can be difficult to get a clean fold.
Revised: • using a book for making a paper airplane: it can be difficult to get a clean fold.

Original: Also, if the book is too thick, the airplane may not fly well.
Revised: Also, if the book is too thick, the airplane may not fly well.

Original: • using a book for a clock: it is a relatively inexpensive way to make a clock.
Revised: • using a book for a clock: it is a relatively inexpensive way to make a clock.

Original: Additionally, it is a relatively easy way to make a clock, as all you need to do is glue numbers to the cover and attach hands.
Revised: Additionally, it is a relatively easy way to make a clock, as all you need to do is glue numbers to the cover and attach hands.

Revised: However, when the suggested use is completely ridiculous, the model still comes up with advantages and never simply rejects an idea as impossible or without redeeming features.

Original: A lingering question is as follows.
Revised: The second set of three results shows that the advantages and disadvantages it generates can be very silly. Discussion and Future Work This paper has presented a paradigm to encourage creativity in GPT-3 by first brainstorming and then selecting the best answers according to their novelty and usefulness. We seek to better understand in what other contexts the generation of possible solutions and evaluation can be successfully applied.

Revised: While GPT-3 was successful at the AUT, this may not be the case for other problems requiring multiple novel steps to be carried out in succession.

Original: To what extent has the model simply copied examples of “creative uses” it has already seen?
Revised: A lingering question remains: to what extent has the model simply copied examples of “creative uses” it has already seen?

Original: One of the model’s worse suggestions was to use a fork to “build a fort.”
Revised: One of the model’s worse suggestions was to use a fork to “build a fort.”

Original: Although the idea makes little sense for a fork, the idea of building a fort is associated with creative play in general.
Revised: Although the idea makes little sense for a fork, the idea of building a fort is associated with creative play in general.

Original: Perhaps the generated suggestions have more to do with the concept of “creativity” as learned by GPT from the training data in general than  true invention.
Revised: Perhaps the generated suggestions have more to do with the concept of “creativity” as learned by GPT from the training data in general than from true invention.

Original: One way totry to explore this would be to rerun the experiment without any mention of novelty or creativity in the original prompt.
Revised: One way to explore this would be to rerun the experiment without any mention of novelty or creativity in the original prompt.

Original: Without bringing in examples it has seen from other “creative” contexts, could it still come up with answers that are both novel and useful?
Revised: Without bringing in examples it has seen from other “creative” contexts, could GPT-3 still come up with answers that are both novel and useful?

Original: We also hope to combine this with a code-generating model that can take the natural language description of a solutionand turn it into a working program that the machine  could then carry out.
Revised: We also hope to combine our approach with a codegenerating model that converts the natural language description of a solution into a working program that a machine such as a robot could then carry out.

Original: Researchers are also exploring how knowledge stored in a pretrained large language model (LLM) can be transferred to robot task planning by way of prompt structures.
Revised: Researchers are already exploring how knowledge stored in a pretrained large language model (LLM) can be transferred to robot task planning by way of prompt structures.

Original: (Singh et al.
Revised: (Singh et al.

Original: 2022) have demonstratedthat LLMs can be prompted with program-like specifications for available actions and objects, as well as with executable example programs, to generate next action sequences.
Revised: 2022) have demonstrated LLMs can be prompted with program-like specifications for available actions and objects, as well as with executable example programs, to generate next actions.

Original: Writing in 2013, (Frey and Osborne 2017) analyzed professions to determine which were at risk of automation.
Revised: Writing in 2013, (Frey and Osborne 2017) analyzed professions to determine which were at risk of automation.

Original: They concluded“it seems unlikely thatoccupations requiring a high degree of creative intelligence will be automated in the next decades.” Such a guess was entirely appropriate given the state of creative generationat the time, but with the recent improvements in creative generation, such tasks may be automatable far sooner than we used to suspect.
Revised: They concluded that creative tasks were unlikely to be automated in the next few decades. Given the rapid advances in creative generation in many different fields over the last few years, however, that conclusion now seems hopelessly outdated