Title: A test of stochastic parroting in a generalisation task: predicting the characters in TV series
Venue: NeurIPS 2024
Accepted: no
Keywords: stochastic parrot

pca

nlp

large language model
Abstract: There are two broad, opposing views of the recent developments in large language models (LLMs). The first of these uses the term "stochastic parrots" from Emily Bender et al ("On the dangers of stochastic parrots: Can language models be too big?" 2021) to emphasise that because LLMs are simply a method for creating a probability distribution over sequences of words, they can be viewed as simply parroting information in the training data. The second view, "Sparks of AGI" from Sebastien Bubeck et al ("Sparks of artificial general intelligence: Early experiments with gpt-4" 2023), posits that the unprecedented scale of computation in the newest generation of LLMs is leading to what its proponents call "an early (yet still incomplete) version of an artificial general intelligence (AGI) system". In this article, we propose a method for making predictions purely from the representation of data inside the LLM. Specifically, we create a logistic regression model, using the principal components of a LLM model embedding as features, in order to predict an output variable. The task we use to illustrate our method is predicting the characters in TV series, based on their lines in the show. We show that our method can, for example, distinguish Penny and Sheldon in the Big Bang Theory with an AUC performance of 0.79. Logistic regression models for other characters in Big Bang Theory have lower values of AUC (ranging from 0.59 to 0.79), with the most significant distinguishing factors between characters relating to the number and nature of comments they make about women. The characters in the TV-series Friends are more difficult to distinguish using this method (AUCs range from 0.61 to 0.66). We find that the accuracy of our logistic regression on a linear feature space is slightly (around 3 percentage points) lower than GPT-4, which is in turn around 5 percentage points below that of a human expert. We discuss how the method we propose could be used to help researchers be more specific in the claims they make about large language models.

Abstract

There are two broad, opposing views of the recent developments in large language models (LLMs). The first of these uses the term "stochastic parrots" from Emily Bender et al [3] to emphasise that because LLMs are simply a method for creating a probability distribution over sequences of words, they can be viewed as simply parroting information in the training data. The second view, "Sparks of AGI" from Sebastien Bubeck et al [6], posits that the unprecedented scale of computation in the newest generation of LLMs is leading to what its proponents call "an early (yet still incomplete) version of an artificial general intelligence (AGI) system". In this article, we propose a method for making predictions purely from the representation of data inside the LLM. Specifically, we create a logistic regression model, using the principal components of a LLM model embedding as features, in order to predict an output variable. The task we use to illustrate our method is predicting the characters in TV series, based on their lines in the show. We show that our method can, for example, distinguish Penny and Sheldon in the Big Bang Theory with an AUC performance of 0.79. Logistic regression models for other characters in Big Bang Theory have lower values of AUC (ranging from 0.59 to 0.79), with the most significant distinguishing factors between characters relating to the number and nature of comments they make about women. The characters in the TV-series Friends are more difficult to distinguish using this method (AUCs range from 0.61 to 0.66). We find that the accuracy of our logistic regression on a linear feature space is slightly lower than GPT-4, which is in turn at a level comparable to two human experts. We discuss how the method we propose could be used to help researchers be more specific in the claims they make about large language models.

1 Introduction

Large language models (LLMs) are neural networks trained on a large text corpus to predict the next word, phrase or paragraph in that dataset [25]. As the number of network parameters and the size of the corpus increases, the ability of this network to write convincing-sounding texts improves [15]. As a result, an increasing number of compelling LLM applications, from CHAT-GPT to Copilot, have been developed. Recently, Bubek et al. argued that "beyond its mastery of language, GPT-4 can solve novel and difficult tasks that span mathematics, coding, vision, medicine, law, psychology and more, without needing any special prompting" [6]. For these authors, this ability to generalise revealed

"Sparks of AGI", going on to state that they believed "that [GPT-4] could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system."

The stochastic parrots paradigm critiques such claims by pointing out that large language models 34

simply predict the next word, sentence or paragraph, and it is humans who attribute understanding to 35

its output [3]. LLMs simply replicate examples (i.e. parrot text) from a massive corpus of data [7]. 36

The stochastic parrots view provides an epistemic critique of claims, such as "Sparks of AGI", about 37 artificial general intelligence. For example, in the context of the benchmark tests (such as those later 38 carried out by [6]), Raj et al. (2021) write, "the reality of [benchmark] development, use and adoption 39 indicates a construct validity issue, where the involved benchmarks - due to their instantiation 40 in particular data, metrics and practice - cannot possibly capture anything representative of the 41 claims to general applicability being made about them." In other words, the very notion of generality, 42 sought to be proven in "Sparks of AGI", cannot be captured by benchmark problems [6]. This 43 critique is fundamental: it doesn't matter how many specific tasks a model completes, there is no 44 convergence towards generality. Even setting these epistemic problems aside, the stochastic parrots 45 view also has practical implications for how we evaluate LLM performance. For example, Lewis 46 and Mitchell (2024) manipulate benchmark tasks to construct 'counterfactual' tasks, by for example 47 adding information that solves the task but LLM's neglect this information, because they are parroting 48 answers to similar, previously trained-on examples [18]. 49

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In spite of the limitation of benchmarks, the fact remains that LLMs do perform well over a wide range of tasks, with little or no additional training data. It is the question of understanding how such performance might arise which we address in this paper. Instead of proposing new benchmarks, we focus on comparing how LLMs perform to simpler, well-understood statistical methods on a novel task. An approach like ours has previously been persued medical imaging - where a systematic review showed that logistic regression on selected features performed (on average) just as well as complicated machine learning approaches [8] - and with respect to conflict prediction - logistic regression perform just as well (as is easier to interpret) than more complex machine learning models [16].

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For many general tasks, a relatively straightforward method of making predictions is to use linear or logistic regression on the leading principal components of a data set. One example is using principle components of 'likes' of Facebook users to predict the answers people gave to big-five personality tests [32, 19, 17]. Konsinski et al. (2016) first performed PCA or Latent Dirichlet Allocation (LDA) on the matrix of likes and Facebook users, and then used the leading components of the PCA (or clusters of LDA) in a regression model to predict the user's answers in personality tests [17]. This allowed the authors to study how the accuracy of predictions increased with the number of dimensions of the Facebook likes. The method is linear in the PCA space and has the advantage that the results can be interpreted qualitatively. For example, young and female users could be predicted as liking "humorous and juvenile" (author's choice of words) statements such as, "I finally stop laughing . . . look back over at you and start all over again" [17].

The above method is potentially interesting in the context of stochastic parrots, because it allows us to, so to speak, look inside the parrot's brain. Large language models encode information using vector semantics: words and sentences are represented as vectors [14, 24, 20], referred to as embeddings. Words that occur in similar contexts tend to have similar meanings, therefore, they will have a similar vector [25]. The vectors are generally based on a co-occurrence matrix, a way of representing how often words co-occur. An alternative to using the term-document matrix to represent words as vectors of document counts, is to use the term-term matrix . If we then take every occurrence of each word and count the context words around it, we get a word-word co-occurrence matrix [14]. Embeddings can be obtained with transformers models [27, 9, 11, 13, 31, 30], which were initially developed for machine translation in 2017 [27, 28].

We can use principal components of the embeddings of a language model, with respect to a specific problem, in order to both understand what information is used in solving the task and to test the degree to which performance on that task is achieved from the representation of the data or from some other unknown mechanism. To make these statements concrete, we now outline what we do in this article. We address the task of predicting which character said which specific lines of dialogue in two US TV series: Big Bang Theory and Friends. This task is reminiscent of the personality research discussed above in that the characters in the show have very stereotypical personalities: can we predict character personalities from their line in the show? Such problems are of specific interest for this article, for three reasons (1) an increasing number of applications of AI involve supposed personality tests and analyses [10]; (2) such tests raise ethical issues about both reliability and applications [29, 1]; (3) they are sometimes used to imply that machines can understand us better than we understand ourselves [32]. The character personality test is an example of generalisation in the sense that, while large language models might have been fed data from these series, they haven't

We proceed as follows. We first detail the method of and logistic regression on the principal 94 components of the embeddings. We then analyse which PCA components are most predictive of 95 statements by the characters, how the number components affects accuracy and differences between 96 the TV shows. Finally, we compare performance of our simpler model to GPT-4 [22] and one human 97 expert, with extensive experience of the two TV shows. 98

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2 Methods

2.1 Embeddings and PCA

The dataset is the transcript of the first 10 seasons of the TV-series The Big Bang Theory 1 and 10 seasons of the TV-serie Friends 2 in English. We cleaned the dataset, by only keeping the main characters and their respective dialogue lines. This gives 44 , 966 dialogue lines for the TV series The Big Bang Theory and 51 , 615 dialogue lines for the TV series Friends. We then transformed these dialogue lines into a vector, i.e. we create embeddings using the python library SentenceTransformer and the model 'all-MiniLM-L6-v2' [26]. Each dialogue line then has a specific embedding, a vector of dimension 384. For comparison, the small text embedding of OpenAi, 'text-embedding-3-small', gives 1,536 output dimension [21, 5].

We then performed a principal component analysis (PCA) on the embeddings (for more details of the method we follow see [12]). Principal Component Analysis(PCA) determines the directions that maximize the variation in the data. The PCA is a procedure that takes dataset with several variables, to a smaller dataset with new variables (the principal components) that will be a linear combination of the former variables. Each dimension in this space corresponds to a feature that will be explicitly defined later. To ensure a representative view of the dataset, we need to standardize it so that no single variable disproportionately influences the analysis, by removing the mean then divide by the standard deviation. Then, we calculate the covariance matrix. A covariance matrix is a square matrix that shows the covariance between pairs of variables in the dataset. The diagonal of the matrix gives the variance of the variables and the other terms give the covariance between the pair of variables. The covariance measures of how much two random variables vary together, by estimating the linearity between them. From the covariance matrix we deduce the eigenvectors and eigenvalues, by doing an eigenvalue decomposition of the covariance matrix C . We find the eigenvector by solving ( C λId ) x = 0 , where x is the eigenvector associated with the eigenvalue λ The eigenvalue gives the magnitude (or accounted variance) of the data along the new feature dimension. The eigenvector gives the direction of the data along the new feature dimension, and forms the linear combination for a principal component. The eigenvalues are in descending order and as explained in [12], they 'maximize the explained variances on each dimension'. We refer to the the coefficients of the leading eigenvector as the first principal component (PCA1), the second eigenvector as PCA2 and so on. We reduce the 384 dimension of each embeddings to a dimension space of 300. All calculations were performed in Sklearn [23] and full code is available here 3 .

An important aspect of our approach is gaining a qualitative understanding of how the principal components reflect the meaning of the dialogue lines. Each PCA corresponds to one eigenvector and consequently to one dimension from which we are able investigate which kind of phrases tied to that dimension. To help us make this analysis we used two-dimensional visualisation of the data. First we implemented a 10-means cluster on two principal components at a time, starting with the leading components (i.e PCA1 and PCA2). We colour each cluster and then assign the phrase nearest of the center as the cluster name (see figure 1). We also looked at the most extreme dialogue line in each PCA, by printing out the sentences with the highest values and the smallest values. From these we assigned a qualitative interpretation of the "meaning" of the leading PCAs. In the annex, we report the tenth highest values and the tenth smallest values.

2.2 Character prediction

In order to predict which dialogue line comes from which character, we use a logistic regression on the PCAs of the dialogue lines of the characters. We follow the notation from [12] and let u j,i be the

j-th the coefficient of the principal component of the i-th dialogue line. First, we normalise all the 143 coefficients u j,i of the principal components by taking away the mean and dividing by the standard 144 deviation, so each component has mean zero and standard deviation of one. We then performed a 145 binomial logistic regression - e.g. does the dialogue line belong to Penny or Sheldon ? - based on 146 a linear prediction of the dialogue line i : 147

allowing to measure (using regression coefficients { β 0 , ..., β n } ) how the the explanatory variables u 1 ,i , ..., u n,i , impact the prediction. The fitted logistic regression is model is given by

where β 0 determines the intercept (i.e. it is the outcome when all the other predictors variables are equal to zero). Each coefficient β i estimates the additional effect of adding the corresponding variable to the model prediction.

The sign of the coefficient indicates the influence of the specific principal component on the probability it is a particular character. If the sign is positive then it is more likely to be that character (Penny in the example above) if the dialogue line has larger and more positive values of that component. Conversely, if the sign is negative that means it is less likely to be that character if the dialogue line has larger and more positive values of that component. The larger the magnitude of the coefficient, the more important the predictor variable is in making the prediction.

For each TV series, we proceed to a logistic regression with 300 first PCAs, for each possible pair of characters. We obtain a predictor function and evaluate the absolute value of each regression coefficient. We obtain the magnitude of each coefficient and therefore assess which coefficients have the most importance in the logistic regression. Afterwards we take the ten regression coefficients with the largest aboslute value and plot them (see figure 3 and 8). From this analyse, we deduce which dimensions that have an impact on the character's prediction. To evaluate performance we calculate the AUC (Area Under The Curve) ROC (Receiver Operating Characteristics) curve to evaluate as a function of the dimensions.

2.3 Comparing to GPT4 and human expert

In order to test our method against a large language model we queried GPT4 with the following system prompt:

"You are expert on the TV series The Big Bang Theory. You are now being challenged to identify characters from the series. Try your best to do well. If you can beat another human expert

there is a prize."

and a query that asked

"Tell me who was most likely out of Leonard and Sheldon

(from the series Big Bang Theory) to have said the following line of dialogue: [DIALOGUE LINE].

Now state the most likely character as a single word, either Leonard and Sheldon.

anything else."

Do not write

Character and TV series names were adjusted appropriately for each test. We tested four pairs (Penny/Sheldon, Leonard/Sheldon, Phoebe/Ross, Phoebe/Chandler). We first repeated the

above procedure 100 times, 50 times for each character, to test the accuracy of the classification (i.e.

proportion of correct answers).

We also provided the same dialogue lines to two motivated human experts (who had watched both series in their entirety two times, most recently within the last year) and expressed a determination to beat GPT4. Both participants were relatives of the co-authors of this article. The same dialogue lines on which GPT4 was tested, were presented in a random order in the spreadsheet file. The subjects were asked to guess the name of the character for each dialogue line, and write it into the spreadsheet.

3 Results

3.1 Qualitative analysis of the principal components

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We started by plotting the embedded dialogue lines 'Big Bang Theory' in terms of the six most 185 important principal components, in order to visualise the most distinguishing features of the dialogue. 186 The first two of these (PCA1 and PCA2) are shown in figure 1aa. The nearest neighbour clustering 187 then allows us to see where different dialogue lines are found in these dimensions. We can see that 188 larger negative values of PCA1 corresponds to very short phrases (for example 'Uh' in the pink 189

(a) Projection of PCA1 and PCA2

(b) Projection of PCA3 and PCA4

Average phrase

Uh.

Oooh.

I might just take you up on that.

Screw him. You're fine.

6

kesnel.

h hat

Sheldon, why are you doing this?

PCA2:

PCA1: Short phrase to phrase that include “Sheldon *

Average phrase

What are youtalkingabout?

ndously

m

about relationship to

They wouldn't let me in. Oh my Howie.

And you?

As usual, nice talking to you Raj

(leaves.)

Phrase

PCA4:

PCA3: Phrase that question a premise to phrase with a first person future action

Average phrase

Oh, no, no, no, crystals don't work.

Great, you do that.

Still going to introduce him?

ayitroubecathingyour

We? What is this?

Who's there?

apology to

PCA6: Phrase

PCA5: Discussion with often a negation to short question about a woman

(c) Projection of PCA5 and PCA6

cluster in the top left of the figure) and larger positive values of PCA1 correspond to phrases about 190 Sheldon (for example 'Sheldon, what do you expect us to do?' in the green cluster in the top right 191 of the figure). The qualitative analysis of PCA1 confirmed this pattern, with 'Yeah' being the most 192 extreme negative value and 'You know, I was thinking. Without Sheldon, most of us would have 193 never met, but Penny would still live across from him.' being the extreme positive value (see Annex 194 6 for a list of the ten most extreme positive and negative values of PCA1 and the other principal 195 components). 196

Following the same approach for PCA2, we found that the negative values are associated with long 197 phrases about a female characters an positive values with phrases about Sheldon. The most extreme 198 negative value is 'Well, there was the time I had my tonsils out, and I shared a room with a little 199 Vietnamese girl. She didn't make it through the night, but up till then, it was kind of fun.' and the 200 most extreme positive value is 'Leonard, Sheldon.'(see annex 6). The cluster values in figure 1a also 201 show the same pattern: with 'Indian princess who befriends a monkey who was mocked by all other 202 monkeys because he was different. For some reason I related to it quite strongly' in the orange cluster 203

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(a) The Big Bang Theory

(b) Friends

0.80

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0.70

0.65

0.60

0.55

Sheldon vs Penny

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Number of PCA

0.80

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0.70

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Phoebe vs Ross

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0OE

Number of PCA

at the bottom of figure 1a and 'Sheldon, why are you doing this?' in the light green cluster at the top of the same figure.

A similar approach can be used to interpret figure 1b and c. PCA 3 ranges from phrase that questions a premise ('Really? I didn't know that.') to phrases with a first person future action ('Aw, sweetie, I'm comfortable around you, too.'). PCA 4 ranges from a phrase about relationship ('Really? That seems rather short sighted, coming from someone who is generally considered altogether unlikable. Why don't you take some time to reconsider?') to a phrase related to eating out ('Excellent! What are you planning to wear?'). The fifth dimension is phrase with often a negation or counterargument (like 'Oh no, no, no, crystals don't work', which is green in figure 1) to a short question about a woman (like 'She knows you. She's tense. We all are. Buy a basket!', which is red in the same figure). Finally, PCA 6 ranges from an apology (e.g. 'I wish you weren't wearing flip-flops. It's dangerous to drive in flip-flops') to a phrase with affirmative statement( e.g. 'Still going to introduce him?'). This final interpretation is even clearer when we look at the extreme negative value ( 'Relax, it wasn't your fault.') and extreme positive value ('Sure. I'd like to meet her.'). Overall, in The Big Bang Theory the distinguishing characteristics of the principal components often relate to the characters views of women. For Friends, there are also clear semantic differences in the sentences, although these appear to be less gender stereotyped. We give a full analysis of the leading six components in annex 5.3.

When we plot the average position of the characters in the space of the first two components, the differences are very small in comparison to the variation (figure 5 in annex 6). For example, while there is a distance of 0.33 between Leonard and Amy on the PCA 1 axis, the standard deviation of the values for the Leonard and Amy on that axis are 3.62 and 3.58, respectively. This observations indicates that it is impossible to distinguish the characters in terms of just a single dimension. We do note, though, that Friends characters are even closer together than The Big Bang Theory characters (the PCA1 distance between Chandler and Rachel is 0.15 and between Chandler and Joey is 0.11, while the standard deviations of Chandler, Rachel and Joey are respectively 3.62, 4.03 and 3.78). The biggest difference we observed is between Penny and Sheldon.

3.2 Character prediction

While a small number of principal component dimensions is not sufficient to tell the characters apart, can we use more of the dimensions to make the distinction? To test this we performed binomial logistic regression on pairs of characters as a function of the number of principal components we included in the model. The AUC values in figure 2a show a steady improvement in the predictions up to around 50 principal components for Big Bang Theory, after which only slight increases in performance are obtained. Sheldon and Penny were easier to distinguish using this method than Sheldon and Leonard. Figure 2b, shows that Friends characters were much more difficult to distinguish using this method.

If we view the principal component analysis as an attempt to capture the character's personality by 238 their dialogue lines (as in the analysis by [32]) then we can say that the TV characters personality 239 have a dimension of somewhere between 50 and 100. Each new dimension gives a small extra insight 240

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P(Penny|Penny or Sheldon)

PCA19

PCA2

PCA15

PCA4

PCA3

PCA29

PCA24

PCA1

PCA7

PCA21

P(Bernadette|BernadetteorSheldon)

PCA19

PCA7

PCA24

PCA5

PCA3

PCA15

PCA21

PCA40

PCA4

PCA37

0.4

P(Sheldon|Sheldon or Raj)

PCA19

PCA24

PCA7

PCA3

PCA27

PCA5

PCA43

PCA6

PCA15

PCA48

P(Penny|Penny or Raj)

PCA2

PCA4

PCA1

PCA15

PCA39

PCA8

PCA19

PCA29

PCA16

PCA10

P(Sheldon|Sheldon or Leonard)

PCA19

PCA2

PCA24

PCA15

PCA7

PCA37

PCA4

PCA3

PCA13

PCA1

P(Sheldon|Sheldon orHoward)

PCA19

PCA24

PCA15

PCA7

PCA27

PCA5

PCA35

PCA29

PCA31

PCA48

- 0.2

P(Amy|Amy or Sheldon)

PCA2

PCA24

PCA19

PCA3

PCA7

PCA10

PCA4

PCA31

PCA13

PCA12

P(Amy|Amy or Penny)

PCA29

PCA18

PCA15

PCA1

PCA32

PCA27

PCA37

PCA2

PCA19

PCA26

P(Amy|Amy or Raj)

PCA10

PCA33

PCA27

PCA2

PCA18

PCA14

PCA32

PCA12

PCA38

PCA13

P(Leonard|Leonard orRaj)

PCA2

PCA1

PCA11

PCA12

PCA10

PCA5

PCA13

PCA18

PCA62

PCA182

P(Bernadette|Bernadette orLeonard)

PCA5

PCA12

PCA2

PCA40

PCA17

PCA7

PCA13

PCA16

PCA108

PCA66

-0.0

P(Penny|Penny or Leonard)

PCA18

PCA29

PCA3

PCA4

PCA12

PCA32

PCA22

PCA17

PCA62

PCA39

P(Raj|Raj or Howard)

PCA131

PCA57

PCA11

PCA39

PCA38

PCA17

PCA15

PCA183

PCA100

PCA107

P(Bernadette|Bernadette orHoward)

PCA54

PCA16

PCA19

PCA22

PCA4

PCA69

PCA40

PCA7

PCA105

PCA5

P(Leonard|Leonard or Howard)

PCA2

PCA46

PCA4

PCA22

PCA13

PCA17

PCA41

PCA27

PCA5

PCA12

- -0.2

P(Bernadette|BernadetteorRaj)

PCA17

PCA16

PCA11

PCA39

PCA185

PCA82

PCA7

PCA141

PCA136

PCA19

P(Amy|Amy or Howard)

PCA33

PCA10

PCA27

PCA17

PCA3

PCA29

PCA42

PCA68

PCA34

PCA12

P(Amy|Amy or Leonard)

PCA12

PCA1

PCA10

PCA33

PCA37

PCA14

PCA66

PCA15

PCA294

PCA118

P(Amy|AmyorBernadette)

PCA5

PCA17

PCA1

PCA42

PCA10

PCA7

PCA22

PCA19

PCA37

PCA95

P(Penny|Penny or Howard)

PCA2

PCA4

PCA17

PCA3

PCA19

PCA1

PCA39

PCA18

PCA16

PCA26

-0.4

P(Penny|PennyorBernadette)

PCA2

PCA17

PCA29

PCA18

PCA5

PCA4

PCA7

PCA15

PCA25

PCA22

into the character differences. Since Friends characters are more difficult to predict from what they say, we can conclude that Friends characters are less stereotyped than characters in The Big Bang Theory.

We can investigate which PCA dimensions best distinguish characters by looking at the coefficients of the binary regression. Figure 3 shows the ten most important components (determined by the magnitude of the absolute value of the coefficients in the regression) for distinguishing the characters dialogue lines in The Big Bang Theory. Each row represents a character pair, with the PCAs ordered from left to right according to the magnitude of the coefficients. The first column corresponds to the coefficient with the largest magnitude in the linear predictor function, the second column corresponds to the second coefficient with the second largest magnitude, and so on.

As an example, the first row is the character prediction for the couple 'Penny and Sheldon' should be read as considering the probability the dialogue line is by Penny, i.e. P ( Penny = 1 | the line is said by Penny or Sheldon ) . The first cell entry, PCA19, is the coefficient in the linear predictor function with the largest absolute value. Performing a qualitative analysis on PCA19 (see annex 6) we find that negative coefficients correspond to lines about food and positive coefficients correspond to lines about comics. In this case, the coefficient of the PCA19 is negative, implying that if a dialogue line is about meal or food, it is more likely to be spoken by Penny than Sheldon.

The most common occurring component in figure 3 is exactly this PCA 19 (food vs. comics) which has 12 occurrences. PCA 2, which is long phrases about a female character versus phrases with one name has 11 occurrences. PCA 7 has 11 occurrences and ranges from phrases with yes/no to question about the current situation. The next most common occurring components are PCA4 (10 occurrences) which ranges from an apology to phrase with affirmative statement; PCA15 (10 occurrances) ranging from long phrases about a woman to short phrases about houses; PCA17 (9 occurrences) range from short phrases about travel to long food related phrases: PCA5 (9 occurrences) which range from long dialogue lines that express an opinion to short questions about a female character.

Figure 4 shows the relationship between the characters in terms of PCA19 (which distinguishes dialogue lines about meal/food related from those about comics). The graph shows the magnitude of

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0.17→

0.14

Raj

0.15

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Penny

0.18

0.34

→Howard

0.29

Sheldon-

0.33

→

Leonard

the coefficient and the direction of the arrow indicates that the coefficient is positive. For example, for P ( Penny | the line is said by Penny or Sheldon ) the regression coefficient for the PCA19 is negative, reflecting the fact that Penny talks more about food and Sheldon that talks more about comics, so the arrow points from Sheldon to Penny. Similarly, we see that Bernadette talks more about food than Raj, Howard Sheldon and Amy and thus the arrows point toward her. And Raj talks more about comics than Penny, Bernadette and even Sheldon, so the arrows point out from him in the figure. In the case of the TV series Friends, the magnitude of the regression coefficients are smaller than those for The Big Bang Theory and a more varied number of components are represented (see annex 5.3).

While the method for constructing figure 4 can give an indication of how the components distinguish the characters, we should bear in mind that in a regression of hundreds of variables (on which this graph is based) the relationships established are not always straightforward. For example, in the figure, we see that the respective models predict that Howard talks more about comics than Sheldon, who talks more about comics than Penny, and Penny talks more about comics than Howard. This inconsistency is likely due to other principal components distinguishing Penny and Howard better than PCA19, and PCA19 acting as a counterbalance, to these additional components. A full analysis of these relationships is beyond the scope of the current article.

3.3 Comparing to GPT4 and human expert

Initial prompting of GPT4 revealed that it has knowledge of the two TV series in its training data. GPT4 replied that it "can provide information about the show, its characters, plot points, cultural impact, and more". It was also able to provide motivation for its answers. For example, when we asked if this dialogue line 'Okay, sweetie, I don't know if we're gonna have cookies, or he's just gonna say hi, or really what's gonna happen, so just let me talk, and we'll. . . ', it correctly answered 'Penny'. Then, when asked, it to explain why it draws conclusions about the characters, it cited criteria "Context of Character Behavior", "Speech Patterns" and "Interaction Dynamics".

For the set of 100 dialogue lines, a direct prompt to GPT4 (see methods for details) was correct for Penny versus Sheldon on 81 occasions, for Sheldon versus Leonard on 71 occasions, for Phoebe versus Ross on 66 occasions, and for Phoebe versus Chandler on 65 occasions. For these same test examples, the first human expert was correct on 71, 76, 67, and 59 occasions, respectively. The second human expert was correct on 74, 72, 70, and 73 occasions. For comparison, the accuracy (percentage correct over all sentences) for the 300 dimensional PCA model was 72.8%, 68.1%, 59.7% and 60.6% respectively. The standard error for a proportion of 70% is 0 . 7 · 0 . 3 · 100 ≈ 4 . 5 %, suggesting a comparable level of performance between the human experts and GPT4, and a slightly lower level of performance for the 300 dimensional PCA model.

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4 Conclusion

Our qualitative analysis highlights how, when interpreted by a human, the principal components of the embeddings reflect the meaning of the dialogue lines of TV series. Many of dimensions contributing to the prediction are related to female characters. This can be attributed to the fact that the TV series portrays very stereotypical characters, with the main protagonists portrayed as geeks, embodying various clichés associated with them. A number of previous studies have identified gender and racial stereotyping within the way models represent data [4, 2, 29], we have shown that these dimensions are also important in the predictions these models make. Friends, in which the characters might be considered to have smaller stereotyped (within-group) differences, was more difficult to predict using this method.

We have shown that given the principal components of the dialogue in a TV series, we are able to predict the characters personality using logistic regression, to a level of performance slightly below that of GPT4. We needed 50-100 dimensions in the logistic regression to predict a dialogue line in the TV series. This might be said to support the idea of a language model more like a stochastic parrot than a spark of AI, in the sense that a large part of the predictive skill of the model can be obtained by adding up the components of the word embeddings and providing an appropriate prediction. Indeed, we have used a much smaller embedding vector (384 dimensions) that GPT4 (several thousand dimensions) to achieve somewhat comparable results.

That said, there remain two things which GPT4 does which our model does not. Firstly, our analysis starts from the sentence embeddings. Taking these embeddings as given ignores the complex process by which these are generated through training in the first place [9, 30]. Secondly, we had to specify the problem we wanted to solve as a logistic regression problem and train on previous data. GPT4, on the other hand, requires no additional training step and, from the given prompt, can identify the requested character. In light of these limitations, we see our work as highlighting the need to be more specific about claims related to sparks of AI [22]. We have shown that prediction part of the question of identifying TV character personality is (to some degree) obtainable from linear models, the question then is where the supposed spark lies? Is it in the creation of embeddings or is it in GPT4's ability to identify the prediction problem from the input provided by the user? We would suggest that further dissections of how these methods work, like we have done here for the prediction stage, can shed more light on these questions.

Our study is limited to a qualitative study of two very specific datasets. The contribution is primarily 332 methodological. We propose an alternative to benchmark testing for understanding why a machine 333 learning method works in the way it does, by comparing it to a method based on linear predictions. 334 As such, it is a qualitative contribution to a larger debate around how to evaluate LLMs, rather than a 335 quantitative demonstration of model performance. 336

337

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5 Annex1 : Supplementary material 415

5.1 Average Position for each main character of the two Tv series 416

(a) in The Big Bang Theory

(b) in Friends

Average position for each character

0.4

0.2

0.4

0.6

0.2

0.0

8

g

0

8

Penny

sLeonard

00

Ralo

<sheldon"

00

0

。

38

$9

88

- 1H , on ueaA, apnjpus eu aseyd wo d

0.4

0.2

0.2

0.6

0.4

0.0

Phoebe

Rachel

Bo

g

00

0.6

1.0

0.5

0.0

0.5

1.0

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2.0

1.0

6

-0.5

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0.5

1.0

1.5

PCA1: Short phrase to phrase that include “Sheldon *

PCA1: From phrase that include a name to * Hey “

2.0

5.2 Accuracy curves the two Tv serie 417

(a) The Big Bang Theory

(b) Friends

0.80

0.75

0.70

0.65

0.60

0.55

Sheldon vs Penny

0

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Number of PCA

0.80

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Phoebe vs Ross

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Number of PCA

5.3 Friends Analysis

418

For Friends, we analyse similarly the 6 first dimensions as seen in Figure 7. The PCA1 is interpreted 419 as phrase that include a name to 'Hey'. This is illustrate with the figure 7a by the dark blue cluster in 420 the left with the average phrase 'Ms. Monroe... Oh there you go', for the negative larger values of the 421 PCA1, and by the red cluster on the top right with average phrase 'Hey' for the positive larger values 422 of the PCA1. The qualitative analysis, in annex 7, gives as the most extreme negative value of the 423 PCA1 the phrase 'Yeah. It's just gonna be too hard. Y'know? I mean, it's Ross. How can I watch 424 him get married? Y'know it's just, it's for the best, y'know it is, it's... Y'know, plus, somebody's got 425 to stay here with Phoebe! Y'know she's gonna be pretty big by then, and she needs someone to help 426 her tie her shoes; drive her to the hospital in case she goes into labour.'. The most extreme positive 427 value of the PCA1 is 'Hey'. The qualitative confirm our earlier statement about the interpretation of 428 the PCA1. 429

The PCA2 is phrase that include 'yeah' to 'Hi'. The negative values of the PCA2 can be found on the 430

figure 7a, for example from the light green cluster of at the bottom left, with average phrase 'Um, 431

Average position for each character

(a) Projection of PCA1 and PCA2

(b) Projection of PCA3 and PCA4

Average phrase

How about loe.. Pepponi?

Ms. Monroe... Oh there you go.

-H- 0uea, pnpu eu

along,huh?You didn't believe me.1

That is a slap in the face.

Here come some more.

Arrrghh!!

No but, no but.

Hm-hmm!

Um, yeah.

AaH

From phraset

PCA2:

10

S-

0

5

10

PCA1: From phrase that include a name to * Hey “

Average phrase

What was that?

That tape was never meant to be seen

10

Did you like leam about her family?

private.

What a sweet story.

It's throwing and catching!

Allright.AllrightPhoebe willet

youhave him,but you oweme; you oweme

big!

didn't lose.

Oh, gosh, you got some on your shirt.

Oh hey. Id shake your hand but uh; I'm

PCA4:

PCA3: From phrase that include a name

to phrase that express the willinaness to help and support someone

Average phrase

So, um, have you told your parents?

I'm sorry, what?

Maybe that's the problem.

I know, I'm her!

Faded salmon color.

Ok.behome rightafterwork.Ok

by Emma-Wemma-Demma, 1 love you - wovyou

dovyou..

Umm.listenRoss dureally think

this moving in with Rachel is a good

idea?

10

0

PCA5: From phrase about character relationships to phrase that include agreement

(c) Projection of PCA5 and PCA6

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yeah.', and the positive value are on the top red cluster with average phrase 'Hey'. It is confirm from the qualitative analysis in annex 7, give the most extreme negative value 'Yeah, fair enough.' and the most extreme positive value 'Hey! Hi!'.

The PCA3 is phrase that express the willingness to help and support someone to phrase that include a name.The projected values of the PCA3 are on the figure 7b, the negative values are on the left of the graph, for example, the orange cluster with average phrase 'Did you like learn about her family?'. In regards of the positive values, they are on the right of the graph, for example the light green cluster with average phrase 'Okay! Okay, you're yelling again! See that?'. The qualitative analysis, see annex 7, shows the most extreme negative value of the PCA3 is 'Phoebe?! Wait a-but-but she just, she said that Joey was her backup.' and the most extreme positive value is ' Hi! I'm back. Yeah, that sounds great. Okay. Well, we'll do it then. Okay, bye-bye.'

The PCA4 interpretation is about phrase that include 'what' or 'oh my God', for example in the figure 7b in the dark purple cluster in the bottom left with average phrase 'What was that?', to phrase about relationship and with the name, for example in the figure 7b with the dark green cluster with average phrase 'Oh hey, I'd shake your hand but uh: I'm really into the game. Plus, I think it'd be better for my ego if we didn't stand rigt to each other.'. The qualitative analysis, in annex 7, confirm our statement with the following most extreme negative value 'What?! What is it?!. and the most extreme positive value 'Well it's okay. Chandler is talking to her.'

PCA5 is phrase about character relationship to phrase that include agreement. As seen in the figure 7c, the negative value of PCA5 are represented on the graph on the left, for example with the light blue cluster with average phrase 'So, um, have you told your parents?'. The positive value of PCA5 are on the right of the figure 7c, as we can pick out from the dark purple cluster with average phrase 'No, but Ross. We are never gonna happen, OK. Accept that.'. The qualitative analysis verify our interpretation, in annex 7, we see that the most extreme negative value is the phrase 'But, also, what happened between you and your Mom?. and the most extreme positive value is 'Yeah! That would be great!'.

We interpret the PCA6 as phrase that question a name to phrase about marriage and proposal. The PCA6 projection is illustrate in the figure 7c, with negative values as the bottom, with for example the cluster dark orange with average phrase 'Yeah, Chandler why don't you take a walk? This doesn't concern you.'. The positive value of the PCA6 are in the top of the graph, for example dark green cluster with average phrase 'Okay, come on, I can't get married until I get something old, something new, something borrowed, and something blue'. Our statement confirmed by the qualitative analysis, in annex 7, with the most extreme negative value is the phrase 'Wait a minute. What's his name?' and the most extreme positive value is the phrase 'Yes! We're getting married?!'.

P(JoeylJoey or Monica)

PCA18

PCA30

PCA13

PCA17

PCA23

PCA14

PCA20

PCA7

PCA81

PCA50

0.2

P(RachellRachel or Joey)

PCA18

PCA63

PCA20

PCA23

PCA9

PCA73

PCA7

PCA39

PCA51

PCA6

PCA17

PCA9

PCA44

PCA3

PCA63

PCA15

PCA39

PCA8

PCA64

PCA12

P(joeyljoey or Phoebe)

PCA18

PCA23

PCA30

PCA9

PCA81

PCA46

PCA51

PCA162

PCA38

PCA223

- 0.1

P(Ross|Ross or Monica)

PCA13

PCA44

PCA19

PCA18

PCA8

PCA1

PCA12

PCA29

PCA4

PCA7

P(Monica|Monica orPhoebe)

PCA9

PCA13

PCA7

PCA35

PCA38

PCA28

PCA51

PCA17

PCA29

PCA102

P(Ross|Ross or Phoebe)

PCA12

PCA58

PCA18

PCA105

PCA181

PCA96

PCA64

PCA247

PCA28

PCA9

P(Monica|Monica orChandler)

PCA18

PCA2

PCA7

PCA39

PCA16

PCA38

PCA35

PCA23

PCA60

PCA11

-0.0

PCA23

PCA16

PCA11

PCA9

PCA28

PCA181

PCA2

PCA53

PCA77

PCA121

P(Rachel|Rachel orPhoebe)

PCA7

PCA17

PCA96

PCA3

PCA16

PCA63

PCA78

PCA39

PCA26

PCA105

P(Ross|Ross or Joey)

PCA20

PCA44

PCA64

PCA2

PCA30

PCA46

PCA18

PCA12

PCA19

PCA5

-0.1

PCA5

PCA2

PCA30

PCA18

PCA16

PCA53

PCA20

PCA121

PCA46

PCA162

PCA16

PCA44

PCA41

PCA72

PCA84

PCA11

PCA69

PCA85

PCA4

PCA1

PCA16

PCA9

PCA39

PCA17

PCA11

PCA3

PCA43

PCA15

PCA6

PCA7

0.2

PCA17

PCA9

PCA31

PCA38

PCA4

PCA6

PCA50

PCA13

PCA250

PCA51

In the case of the TV series Friends, in the figure 8, the first column are the most significant regression 466 coefficient for each pair. We can notice that the most extreme negative value is in the first row 467 and belongs to the regression coefficient of the character's dialogue lines prediction between Joey 468 and Monica. The probability is as follow, P ( Joey = 1 | the line is said by Joey or Monica ) = p and 469 P ( Monica = 0 | the line is said by Joey or Monica )) = 1 -p . The corresponding dimension of the 470 first coefficient is the PCA 18, it depicts phrase from 'Oh no' to phrase that include 'yeah' or 471 'look' (see qualitative analysis in annex ?? . In other words, a phrase that include 'Oh no' is more 472 likely from Joey. The most extreme positive value in this first column appears in the last row, 473 corresponding to the regression coefficients for predicting dialogue lines between the pair 'Rachel 474 and Monica'. The probability is such that P ( Rachel = 1 | the line is said by Rachel or Monica ) = p 475 and P ( Monica = 0 | the line is said by Rachel or Monica ) = 1 -p . The coefficient correspond to the 476 dimension PCA17: from phrase that include 'Joey' to phrase that include 'Ross'. We can deduce that, 477 if a phrase include 'Ross' it is more likely from Rachel. 478

For Friends, we also count the occurrences of each PCA from the figure 8, and then interpret them. 479 We recapitulate the information in the table 1. Contrary to The Big Bang Theory the phrases in 480 Friends are much shorter, more exclamatory, and there are less obvious topic like food or comics. 481

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In the TV series Friends, we note fewer instances of the main principal component analysis. For instance, in The Big Bang Theory, PCA19 occurs most frequently, appearing 12 times. However, in Friends, PCA9 and PCA18 are the most common dimensions, each occurring 8 times. If we count the number of different PCA in figure 3 for The Big Bang Theory we obtain 59, and 56 different PCA for Friends in the figure 8. The number of dimension is similar in both case, but we can pick out that the magnitude of the coefficient is slightly higher in The Big Bang Theory than in Friends. Since the TV serie Friends has less occurrences of the main PCAs, smaller magnitude in the regression coefficients and less AUC accuracy, therefore more dimension are needed into the dialogue line predictions. This is visible on the figure 5, where we can see that average position of the character in Friends are more closer than the average position of the character in The Big Bang Theory.

Rachel

0.16

0.13

0.16

Phoebe

In the figure 9, we show the relationship between the character of Friends for the PCA9, the dimension that have the most occurrences, it is interpret as with phrase that include 'Oh' to question

about what the people has been doing. For example, in the dialogue lines prediction

1

|

the line is said by Rachel or Ross

)

P

(

Rachel

=

, the regression coefficient is positive, then if it is a question

6 502

515

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6.1.2 Highest coefficient

BERNADETTE : You know, I was thinking. Without Sheldon, most of us would have never met, but Penny would still live across from him.

AMY : Which couldn't have happened if you didn't live across the hall from her, which couldn't have happened without Sheldon. Same goes with you guys. If Leonard wasn't with Penny, she never would have set you up.

PENNY : Oh, my God, Sheldon the genius is jealous of Leonard.

HOWARD : Now, I never thought I'd say this, but I'm kind of excited to see Sheldon.

AMY : This isn't about me and Sheldon. This is about Rajesh moving in with Leonard and Penny.

RAJ : It's a human emotion, Sheldon. Everyone gets jealous. I'm jealous of Leonard and Penny and Howard and Bernadette for being in such happy relationships.

LEONARD : Oh, come on. Sheldon, have you ever once heard me say that I don't trust Penny? Sheldon? Where did he go?

PENNY : Well, yeah, he'd been living with Sheldon.

LEONARD : Really. Who do you think did that, Sheldon?

AMY : Well, I was hoping the next person I dated would be a little less like Sheldon.

6.2 PCA2

6.2.1 Lowest coefficient

AMY : Well, there was the time I had my tonsils out, and I shared a room with a little Vietnamese girl. She didn't make it through the night, but up till then, it was kind of fun.

BERNADETTE : Because it would make you seem like something she already thinks you are.

BERNADETTE : You don't think she'd actually send you something gross or dangerous, do you?

541

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557

LEONARD : Too expensive. You'd think I'd be used to women withholding their love. I mean, my mother did. I mean, no matter how hard I tried, she just didn't have any interest in me.

LEONARD : I mean, I know she's not my girlfriend or anything, but wouldn't you think she'd feel a little bad that I'm going to be gone for the whole summer?

SHELDON : Or you might think she thinks you think it's a date even though she doesn't.

LEONARD : Yeah, yeah, that's the fun part. We're also getting new curtains for my bedroom, and a dust ruffle, and a duvet, and I don't even know what a duvet is but I'm pretty sure if I did I wouldn't want one, but every time I talk to her about moving out she cries and we have sex.

AMY : Parental pressure can be daunting. I remember the battle with my mother about shaving my legs. Last year, I finally gave in and let her do it.

LEONARD : Don't you think if a woman was living with me I'd be the first one to know about it?

SHELDON : I was hoping she might listen to you about the dangers of owning unhygienic furniture.

6.2.2 Highest coefficient

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6.3 PCA3

6.3.1 Lowest coefficient

SHELDON : Really? I didn't know that.

PENNY : Did they make a movie about it?

RAJ : How did that even happen? Did they know that's what they were doing when they were doing it?

HOWARD : Yeah, I saw it on Mythbusters.

BERNADETTE : Do they have that?

SHELDON : A more plausible explanation is that his work in robotics has made an amazing leap forward.

SHELDON : Oh. Is it true they used scuba gear to create the sound of Darth Vader breathing?

HOWARD : Not exactly. They spent a ton of money developing this dandruff medication that had the side effect of horrible anal leakage.

SHELDON : It's been around for 25 years, and has been extensively corroborated by other researchers.

584

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RAJ : Did he get superpowers?

6.3.2 Highest coefficient

PENNY : Aw, sweetie, I'm comfortable around you, too.

LEONARD : Great. Just relax and enjoy. Tonight is all about you.

SHELDON : Thank you, but I'll be fine.

PENNY : Okay, well, we'll talk to you guys later. Bye. She said not to come. It's gonna be a while.

SHELDON : Fine, let's go. Thank you for letting me sleep on your couch.

SHELDON : Oh, well, you two sit down and get to know each other. I'll get your room ready.

AMY : I will. I wish you were here.

LEONARD : Let's go. Okay, you two, just, have a nice. . . whatever this is.

PENNY : All right. Well, you guys have fun. I guess I'll see you Sunday night.

LEONARD : Yeah, no, I'm fine. It's good, it's a good party, thanks for having us, it's just getting a little late so. . . .

6.4 PCA4

6.4.1 Lowest coefficient

SHELDON : Really? That seems rather short sighted, coming from someone who is generally considered altogether unlikable. Why don't you take some time to reconsider?

SHELDON : Yes, and she's not taking my feelings into account at all. Maybe it's time I teach her a lesson.

AMY : No, we're sorry. We never should have been comparing relationships in the first place.

HOWARD : Yeah, she was dating this guy, and I was kind of a jerk to her about it.

SHELDON : Yeah, but to be fair, he only said the part about him getting sick of you.

SHELDON : Oh, you're right. I could never be with a woman whose self-esteem was so low she'd be with Leonard.

SHELDON : Not true. No, look at me. I had an engagement ring to give a girl, and instead, she rejected me. And am I emotional about that? No. No, I am sitting here on a couch, talking about my favourite TV character like nothing happened. 'Cause I am just like him, all logical, all the time.

SHELDON : It hurts that you would lie to me, Amy. I thought our relationship was based on trust and a mutual admiration that skews in my favour.

PENNY : Okay, I have not tried to change Leonard. That's just what happens in relationships. Look how much Amy's changed you.

PENNY : I get that, okay? It's just, Leonard and I have been married for two years, and we're no further along than when we were dating.

6.4.2 Highest coefficient

HOWARD : In our new minivan. Hey, what's for lunch?

BERNADETTE : Where are you guys going to eat?

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PENNY : What beverage do you make for that?

SHELDON : Oh, I have quite the evening planned. Our foetus-friendly festival of fun begins with an in-depth look at the world of model trains, and then we'll kick things up a notch and explore all the different ways that you can make toast.

LEONARD : What are you drinking there? A little eggnog?

RAJ : Sounds great!

SHELDON : In here, you'll find emergency provisions. An eight-day supply of food and water, a crossbow, season two of Star Trek: The Original Series on a high-density flash drive.

AMY : I'm going to the vending machine. Do you want anything?

SHELDON : Greetings, gentlemen. How goes your little project?

6.5 PCA5

6.5.1 Lowest coefficient

BERNADETTE : Absolutely. All we need to do is spend a little time and find something you're passionate about.

PENNY : Okay, a simple yes will do.

BERNADETTE : Of course you can. But maybe a good rule would be to wait for people to bring it up.

RAJ : No, no, it's a very promising area. In a perfect world I'd spend several more years on it. But I just couldn't pass up the opportunity to work with you on your tremendously exciting and not yet conclusively disproved hypothesis.

LEONARD : Sheldon, I think this will work. Let's just try it my way.

LEONARD : If that's what you want to do, yes.

HOWARD : Yeah, this is a bad idea. We should go.

AMY : Of course. I get to be part of the first team to use radon markers to map the structures that. . .

PENNY : Yeah. And there are a few things we need to stay on top of. So we thought it would useful, and I can't believe I'm about to say this, um.

LEONARD : No, I don't want to do it. You can do it.

6.5.2 Highest coefficient

HOWARD : How was she? 656 LEONARD : When was the last time you saw her? 657 LEONARD : How's your mom holding up? 658 AMY : Oh. What was her name? 659 LEONARD : How's she doing? 660 BERNADETTE : It was your mom. 661 LEONARD : Aw. What's wrong with her? 662 HOWARD : My mom died. 663 SHELDON : What's her name? 664 HOWARD : So, what is she doing today? 665

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6.6 PCA6

6.6.1 Lowest coefficient

LEONARD : Relax, it wasn't your fault.

HOWARD : I'm sorry, too. It's all my fault.

AMY : Well, I didn't, and it's your fault.

PENNY : I'm sorry I yelled at you. It's not your fault.

LEONARD : It's not your fault.

AMY : It's not your fault.

SHELDON : It's simple biology. There's nothing I can do about it.

HOWARD : Look, I have felt terrible about this for years, and I'm glad I have the opportunity to tell you just how sorry I am.

LEONARD : This time, it's your fault.

LEONARD : Well, that's not your fault.

6.6.2 Highest coefficient

LEONARD : Sure. I'd like to meet her.

LEONARD : Will Amy be joining us for dinner?

BERNADETTE : Maybe, if she asks.

HOWARD : Sure she would. Ma, do you mind if Bernadette stays here this weekend?

LEONARD : No, no, of course not. Just have your relationship someplace else.

SHELDON : I'm going to find her and ask her to marry me. And if she says yes, we can put this behind us and resume our relationship. And if she says no, well, then she can just ponfo miran.

6.7 PCA19

6.7.1 Highest coefficient

SHELDON : Yes. Oh, I'm so excited. And I just can't hide it.

PENNY : I do, it's just he wants to go to that party at the comic book store. A lot of the guys that hang out there are kind of creepy.

LEONARD : Oh, I'm just trying to find the stupid next of kin to this stupid video store owner so I can return the DVD and see the look on Sheldon's stupid face when he sees that I didn't let this get to me.

HOWARD : Ooh, I want to go to the comic book store. (He leaves.)

PENNY : Yeah, but those tickets only get him into Comic-Con. That dress gets me into anywhere I want.

PENNY : No, come on, it's going to be fun, and you all look great, I mean, look at you, Thor, and, oh, Peter Pan, that's so cute.

BERNADETTE : Is it me, or is there something fun about watching him just float there?

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HOWARD : Come on, Sheldon, there's so few places I can wear my jester costume.

RAJ : So, listen to what he wrote. Uh, I saw you play at the comic book store. You guys rock. And then there's an animated smiley face raising the roof like this.

SHELDON : Oh no! (He is also wearing a Flash costume.)

6.7.2 Lowest coefficient

SHELDON : We can't have Thai food, we had Indian for lunch.

SHELDON : It was a Monday afternoon. You joined us for Indian food.

SHELDON : Good morning, Friend Howard. Friend Raj. I see you gentlemen are enjoying beverages. Perhaps they would taste better out of these.

RAJ : My stomach. Indian food doesn't agree with me. Ironic, isn't it?

LEONARD : Well the only way we can play teams at this point is if we cut Raj in half.

LEONARD : I've always been a little confused about this. Why don't Hindus eat beef?

RAJ : Of course, but it's all Indian food. You can't find a bagel in Mumbai to save your life. Schmear me.

SHELDON : Yeah, I actually have information about Raj that would be helpful with this discussion.

RAJ : We Indians invented them. You're welcome.

LEONARD : Here's an idea, why don't we just go out for Indian food.

6.8 PCA7

6.8.1 Highest coefficient

RAJ : He's gonna be here any second, what should we do?

PENNY : What are you guys gonna do?

LEONARD : What are we gonna do?

6.8.2 Lowest coefficient

PENNY : Oh, Sheldon, are these letters from your grandmother?

PENNY : I do, and you know, I don't think I've ever thanked you properly for helping me get it.

SHELDON : Oh, yes. In fact, I improved upon it.

SHELDON : No, of course not. No, I used trickery and deceit.

LEONARD : Yeah, no, I do, I use those. . . uh. . . just to polish up my. . . spear-fishing equipment. I spear fish. When I'm not crossbow hunting, I spear fish. Uh, Penny, this is Sheldon's twin sister, Missy. Missy, this is our neighbour Penny.

LEONARD : Yes, I've always admired that about you.

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PENNY : She was right, you know. The locus of my identity is totally exterior to me.

LEONARD : Oh, yes. Indeed, I did.

LEONARD : No, no, I'm good. If my P.E. teachers had told me this is what I was training for, I would have tried a lot harder.

RAJ : Do you kind of look like a shiny Sheldon?

6.9 PCA15

6.9.1 Highest coefficient

BERNADETTE : Yeah. You're inviting him into your home. It's intimate. It's where your underpants live.

RAJ : It's a lease.

LEONARD : What was I supposed to do? He needed a place to sleep it off.

LEONARD : Ask him for a napkin, I dare you. (There is a knock on the door.) I'll get it.

RAJ : He probably just goes to the bathroom.

HOWARD : Maybe the problem is he thinks you're available. Does he know you're dating Sheldon?

LEONARD : What if he lives in your garage?

HOWARD : How'd you get him to come to your house?

BERNADETTE : What are you going to do? Doesn't he know you have a boyfriend?

LEONARD : He's in his bedroom.

6.9.2 Lowest coefficient

LEONARD : Look, do I think that you are talented and that you are beautiful? Of course I do. But isn't Los Angeles full of actresses who are just as talented, just as beautiful? All right, look, we'll come back to that.

AMY : I do. Penny, Bernadette and I are sorry.

RAJ : Oh, yes, we've got the moon and the trees and Elizabeth McNulty, who apparently died when she was the same age I am.

SHELDON : And on a different, but not unrelated topic, based on your current efforts to buoy my spirits, do you truly believe that you were ever fit to be a cheer leader?

SHELDON : Hello, female children. Allow me to inspire you with a story about a great female scientist. Polish-born, French-educated Madame Curie. Co-discoverer of radioactivity, she was a hero of science, until her hair fell out, her vomit and stool became filled with blood, and she was poisoned to death by her own discovery. With a little hard work, I see no reason why that can't happen to any of you. Are we done? Can we go?

SHELDON : No, I don't think so. Those dolls represent three things I do not care for, clowns, children and raggediness. I think it's a lost cause.

SHELDON : Yes. I think prolonged exposure to Penny has turned her into a bit of a Gabby Gertie.

RAJ : Yes, isn't she an amazing actress.

SHELDON : Actually, I thought the first two renditions were far more compelling. Previously I felt sympathy for the Leonard character, now I just find him to be whiny and annoying.

HOWARD : She was just so sad all the time. I was the only person who could cheer her up. Well, me and Ben and Jerry.

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6.10.1 Highest coefficient

SHELDON : Penny, a moment. We just had Thai food. In that culture, the last morsel is called the krengjai piece, and it is reserved for the most important and valued member of the group.

LEONARD : Yeah, it's delicious, the sarcasm's a little stale, though. Hey, how about this? Until we figure out what to do with the ring, Penny holds on to it.

PENNY : Okay, sweetie, I don't know if we're gonna have cookies, or he's just gonna say hi, or really what's gonna happen, so just let me talk, and we'll. . .

PENNY : Fine. What do you want?

HOWARD : Okay, this one is for a Cadbury Creme Egg.

LEONARD : Ah, well, what's this? A pot of oatmeal? Or, thanks to you, what I will now call gloatmeal.

SHELDON : I'm sorry, but these are just ordinary foods with the names bent into tortured puns. The dishes themselves are in no way Halloweenie.

LEONARD : Ah, that's a good question. Apparently someone was being awfully flirty while not wearing their engagement ring, causing another someone to show up here thinking the first someone might be available.

PENNY : Okay, well, I'd offer you Halloween candy, but that's gone. So, what's up?

RAJ : Okay. Shall we? Oh, my God. It's light, it's flaky, it's buttery. You don't need to have sex with him, just eat one of these.

6.10.2 Lowest coefficient

RAJ : Then she's going to have to convince your mother to let you go into space.

HOWARD : Then get out of my house.

BERNADETTE : Yeah, if you want to go off the grid, you have to move out of your mother's house.

SHELDON : I can't believe my own mother is abandoning me.

HOWARD : I will. I'm obviously not going to live in my mother's house for the rest of my life. I'm not a child.

LEONARD : With your career?

BERNADETTE : You're a real hero, Howard.

BERNADETTE : I'm proud of her. This is a great opportunity. It's nice to see her take it seriously.

LEONARD : Also instead of just living in your mother's house, you could actually live inside her body.

LEONARD : And now you're also an astronaut.

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7 Annex 3: Dialogue example of Friends

7.1 PCA1

7.1.1 Highest coefficient

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7.1.2 Lowest coefficient

RACHEL : Yeah. It's just gonna be too hard. Y'know? I mean, it's Ross. How can I watch him get married? Y'know it's just, it's for the best, y'know it is, it's... Y'know, plus, somebody's got to stay here with Phoebe! Y'know she's gonna be pretty big by then, and she needs someone to help her tie her shoes; drive her to the hospital in case she goes into labour.

RACHEL : Ross, you know what? She may need one..We're just going to have to make our peace with that! Monica and Chandler's apartment.

JOEY : Look we've got to find her. Phoebe just called!! Rachel's coming to tell Ross she loves him!!

CHANDLER : Well, she's just so much fun with Joey, I just assumed, she'd still be living with him.

JOEY : Well, remember when they got in that big fight and broke up and we were all stuck in her with no food or anything? Well, when Ross said Rachel at the wedding, I figured it was gonna happen again, so I hid this in here.

MONICA : I can't believe this. Rachel and Joey?

RACHEL : Look Monica, getting cold feet is very common. Y'know, it's-it's just because of all the anticipation and you just have to remember that you love Chandler. And also, I ran out on a wedding. You don't get to keep the gifts.

MONICA : No, look, she's obviously unstable, okay? I mean she's thinking about running out on her wedding day. Okay, fine! But I mean, look at the position she's putting him in! What's he gonna do? Ross is gonna run over there on the wedding day and break up the marriage?! I mean, who would do that?! Okay, fine, all right, but that's y'know, it's different! Although it did involve a lot of the same people.

PHOEBE : It's so weird seeing Ross and Rachel with a baby. It's just so grown up.

7.2 PCA2

7.2.1 Highest coefficient

RACHEL : Hey! Hi! 868 ROSS : Hey! Hi! 869 PHOEBE : Hey! Hi! 870 RACHEL : Hey! We're here! 871 RACHEL : Hi!! 872 RACHEL : Hi!! 873 MONICA : Hi! 874 MONICA : Hi! 875 MONICA : Hi! 876

7.2.2 Lowest coefficient 877

RACHEL : Yeah, fair enough. 878

RACHEL : Really? You think so? 879

PHOEBE : Really? You think? 880

PHOEBE : Yeah, what's your point? 881

PHOEBE : Yeah, but not just that. 882

RACHEL : No, you're right, you are absolutely right. I mean that makes, that makes 883 everything different. 884

JOEY : No. Really? 885

ROSS : Really? Its not just frowned upon? 886

JOEY : Yeah, I wouldn't know about that. 887

CHANDLER : Yeah, you're right about that. 888

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7.3 PCA3

7.3.1 Highest coefficient

CHANDLER : Hi! I'm back. Yeah, that sounds great. Okay. Well, we'll do it then. Okay, bye-bye.

ROSS : I'll do it. Hey, whatever you need me to do, I'm your man. Whoa-oh-whoa! Are you, are you okay?

RACHEL : No, come on, I'm totally ok. I don't need you to come! I can totally handle this on my own.

ROSS : I'll help you. Yeah, I'll make up a schedule and make sure you stick to it. And plus, it'll give me something to do.

JOEY : Alright, alright. I'm around. Go ahead.

PHOEBE : Anyway, I should go. Okay, bye.

MONICA : Ok first of all...It would be great. But that's not what I'm here to talk to you about. I need to borrow some money.

MONICA : No, I'll do it. You just stick to your job.

ROSS : Oh, that'd be great! Okay, but if you do, make sure it seems like you're there to see him, okay, and you're not like doing it as a favour to me.

JOEY : Sure, yeah. I don't have time to say thank you because I really gotta go.

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7.3.2 Lowest coefficient

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7.4 PCA4

7.4.1 Highest coefficient

ROSS : Well it's okay. Chandler is talking to her.

JOEY : I said a little bit Ross. Now, how about you Chandler?

JOEY : Okay. I'm Chandler

JOEY : Hey look Ross, you need to understand something okay? I uh...I am never gonna act on this Rachel thing, okay? I-I would never do anything to jeopardize my friendship with you.

JOEY : It's okay, Ross, alright? I totally understand. Of course you're not fine. You're.. You're Ross and Rachel.

JOEY : I'm fine, I'm fine, it's just, it's just weird what's happening with her and Ross. You know, yesterday he asked me to fix him up with somebody.

RACHEL : All right. So you're telling me that there is nothing going on between you and Chandler.

ROSS : Fine, fine, Rachel your with Monica, Joey you're with me.

PHOEBE : Okay. Oh umm, Chandler, Monica is looking for you.

ROSS : Umm, okay, yeah, sure. But wh-what's wrong with Monica and Chandler?

7.4.2 Lowest coefficient

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7.5 PCA5

7.5.1 Highest coefficient

RACHEL : Yeah! That would be great! 948 MONICA : Yeah, that'd be great! Thank you! 949 JOEY : Yeah! Yeah! That would be very helpful! Yeah. 950 CHANDLER : All right, ready? 951 ROSS : All right, ready? 952 CHANDLER : All right, ready? 953 PHOEBE : All right, ready? 954 MONICA : All right, you ready? 955 PHOEBE : Sure, yeah! 956

JOEY : Sure. Yep. 957

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7.5.2 Lowest coefficient

PHOEBE : But, also, what happened between you and your Mom?

JOEY : She was nothing compared to you.

JOEY : She was nothing compared to you.

CHANDLER : Hey that's what I tell girls about me.

JOEY : Me too. I mean I...haven't thought at all about how I put myself out there and said all that stuff and how you didn't feel the same way about me and-and how it was really awkward.

ROSS : Well, well I am married. Even though I haven't spoken to my wife since the wedding.

PHOEBE : Oh, because, you know... they don't like you.

MONICA : Well, um, because mainly, um, they don't like you. I'm sorry.

CHANDLER : Well it couldn't have been worse. A woman literally passed through me. OK, so what is it, am I hideously unattractive?

ROSS : Hey, whatever it is, I am sure it has happened to me. Y'know, actually once-once I got dumped during sex.

7.6 PCA6

7.6.1 Highest coefficient

ROSS : Yes! We're getting married?!

JOEY : No! No, and I did not ask her to marry me!

ROSS : N-no! Okay? We've been through this! We're not gonna get married just because she's pregnant, okay?

JOEY : Well all right then, I guess I shouldn't get to excited about the fact that I just kissed her!

CHANDLER : OH...MY...GAWD! I am so sorry sweetie, are you okay? You didn't tell her we were getting married, did you?

ROSS : Hey! I offered to marry her!

CHANDLER : How can I not be upset? Okay? I finally fall in love with this fantastic woman and it turns out that she wanted you first!

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7.8 PCA18

7.8.1 Highest coefficient

JOEY : Yeah, he did, look... look, it's right there on the counter! Ha-ho-ho!

CHANDLER : Okay, did you see that?! With the inappropriate and the pinching!!

CHANDLER : Okay, did you see that?! With the inappropriate and the pinching!!

JOEY : Hey! Handcuffs! And fur line, nice! I didn't know you guys had it in ya!

JOEY : Look, it was a job all right?

CHANDLER : Look! Look! Look what the... Look what... Look what the floating heads did!

ROSS : Okay, there was some staring and pointing.

MONICA : Yeah, yeah, it's interesting.. but y'know what? Just for fun, let's see what it looked like in the old spot. Alright, just to compare. Let's see. Well, it looks good there too. Let's just leave it there for a while.

JOEY : Uh, take a look at the guy's pants! I mean, I know you told us to show excitement, but don't you think he went a little overboard?

RACHEL : Yeah, he did! Oh, see, this is what I'm talking about!

7.8.2 Lowest coefficient

RACHEL : Oh no. 1042

PHOEBE : Oh no. 1043

PHOEBE : Oh no. 1044

RACHEL : Oh no. 1045

CHANDLER : Oh no. 1046

PHOEBE : Oh no. 1047

ROSS : Oh no. 1048

PHOEBE : 1049

Oh no.

PHOEBE : Oh no. 1050

ROSS : Oh no. 1051

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7.9 PCA7

1053

7.9.1 Highest coefficient

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7.9.2 Lowest coefficient

JOEY : Yeah, yeah, I met this woman.

MONICA : Yes but my mom got me this job.

PHOEBE : Yes, yes I do. God, oh it's just perfect! Wow! I bet it has a great story behind it too. Did they tell you anything? Like y'know where it was from or...

PHOEBE : No, not usually. But yeah, I could use one right now.

PHOEBE : Yeah, kinda.

MONICA : Yeah, just like the one in the poem.

CHANDLER : Yes, money well spent!

PHOEBE : No! But it's the nicest kitchen, the refrigerator told me to have a great day.

CHANDLER : Yeah, I remember.

MONICA : No. But I remember people telling me about it.

7.10 PCA17

7.10.1 Highest coefficient

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7.10.2 Lowest coefficient

MONICA : Hey, Joey, I don't think that you should leave Chandler alone. I mean it's only been two days since he broke up with Kathy. Maybe you can go fishing next week?

JOEY : Chandler, you have to start getting over her. All right, if you play, you get some fresh air, maybe it'll take your mind off Janice, and if you don't play, everyone will be mad at you 'cause the teams won't be even. Come on.

PHOEBE : Joey? How could you just let them leave?

CHANDLER : Look, Joey, Kathy is clearly not fulfilling your emotional needs. But Casey, I mean granted I only saw the back of her head, but I got this sense that she's-she's smart, and funny, and gets you.

MONICA : Wait a minute...Joey. Joey you can't ask her out, she's your roommate. It-it'll be way too complicated.

PHOEBE : Okay, but try and get Joey too.

ROSS : No Joey! Look why don't, why don't we just let her decide? Okay? Hey-hey, we'll each go out with her one more time. And-and we'll see who she likes best.

RACHEL : Yeah, Joey kinda disabled it when I moved in.

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MONICA : Joey that is horriable.

CHANDLER : No, see the thing is I want to get out of here before Joey gets all worked up and starts calling everybody bitch.

7.11 PCA16

7.11.1 Highest coefficient

JOEY : I don't know. It's-it's just...lately, I've been feeling... Okay, here's what it is...

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NeurIPS Paper Checklist

1. Claims

Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?

Answer: [Yes] .

Justification: The article follow indeed the abstract claims.

Guidelines:

The answer NA means that the abstract and introduction do not include the claims made in the paper.

The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers.

The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.

It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper.

2. Limitations

Question: Does the paper discuss the limitations of the work performed by the authors? Answer: [Yes] .

Justification: We outline the limitations clearly in the last paragraph of the conclusions.

Guidelines:

The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.

The authors are encouraged to create a separate "Limitations" section in their paper.

The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.

The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.

The authors should reflect on the factors that influence the performance of the approach. For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.

The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.

If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.

While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.

3. Theory Assumptions and Proofs

Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?

Answer: [NA] .

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Justification: There are no theoretical results or proof in this paper.

Guidelines:

The answer NA means that the paper does not include theoretical results.

All the theorems, formulas, and proofs in the paper should be numbered and crossreferenced.

All assumptions should be clearly stated or referenced in the statement of any theorems.

The proofs can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged to provide a short proof sketch to provide intuition.

Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.

Theorems and Lemmas that the proof relies upon should be properly referenced.

4. Experimental Result Reproducibility

Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?

Answer: [Yes] .

Justification: The code and data are provide. The datasets are public, and all the library used are also public.

Guidelines:

The answer NA means that the paper does not include experiments.

If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.

If the contribution is a dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.

Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution is a specific model and empirical evaluation, it may be necessary to either make it possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g., in the case of a large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.

While NeurIPS does not require releasing code, the conference does require all submissions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example

(a) If the contribution is primarily a new algorithm, the paper should make it clear how to reproduce that algorithm.

(b) If the contribution is primarily a new model architecture, the paper should describe the architecture clearly and fully.

(c) If the contribution is a new model (e.g., a large language model), then there should either be a way to access this model for reproducing the results or a way to reproduce the model (e.g., with an open-source dataset or instructions for how to construct the dataset).

(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome to describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results.

5. Open access to data and code

Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material?

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Answer: [Yes] .

Justification: Data and full code are on Github

Guidelines:

The answer NA means that paper does not include experiments requiring code.

Please see the NeurIPS code and data submission guidelines ( https://nips.cc/ public/guides/CodeSubmissionPolicy ) for more details.

While we encourage the release of code and data, we understand that this might not be possible, so 'No' is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for a new open-source benchmark).

The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurIPS code and data submission guidelines ( https: //nips.cc/public/guides/CodeSubmissionPolicy ) for more details.

The authors should provide instructions on data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc.

The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. If only a subset of experiments are reproducible, they should state which ones are omitted from the script and why.

At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).

Providing as much information as possible in supplemental material (appended to the paper) is recommended, but including URLs to data and code is permitted.

6. Experimental Setting/Details

Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?

Answer: [Yes]

Justification: The full code used are on gitub, that include all the information that we did (test split,...).

Guidelines:

The answer NA means that the paper does not include experiments.

The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them.

The full details can be provided either with the code, in appendix, or as supplemental material.

7. Experiment Statistical Significance

Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?

Answer:[Yes] .

Justification: We report standard errors for our experimental results on GPT4 and human subjects.

Guidelines:

The answer NA means that the paper does not include experiments.

The authors should answer "Yes" if the results are accompanied by error bars, confidence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper.

The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).

The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)

The assumptions made should be given (e.g., Normally distributed errors).

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It should be clear whether the error bar is the standard deviation or the standard error of the mean.

It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have a 96% CI, if the hypothesis of Normality of errors is not verified.

For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out of range (e.g. negative error rates).

If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.

8. Experiments Compute Resources

Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?

Answer: [Yes] .

Justification: There are no information about time or resources, the calculus used takes 4 seconds (to do the PCA) to a couple of minutes (to do the embeddings), and do not required lots of memory

Guidelines:

The answer NA means that the paper does not include experiments.

The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.

The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute.

The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make it into the paper).

9. Code Of Ethics

Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics https://neurips.cc/public/EthicsGuidelines ?

Answer: [Yes]

Justification: The paper conforms to the NeurIPS code of Ethics

Guidelines:

The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.

If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics.

The authors should make sure to preserve anonymity (e.g., if there is a special consideration due to laws or regulations in their jurisdiction).

10. Broader Impacts

Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?

Answer: [Yes] .

Justification: The qualitative analysis shows how gender stereotyping is a large part of how machine learning models make predictions. The article also critiques ideas around artificial general intelligence (AGI) in a way we think illuminates debate on these issues.

Guidelines:

The answer NA means that there is no societal impact of the work performed.

If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact.

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Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.

The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.

The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.

If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML).

11. Safeguards

Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?

Answer: [NA]

Justification: No such risks

Guidelines:

The answer NA means that the paper poses no such risks.

Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.

Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.

We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make a best faith effort.

12. Licenses for existing assets

Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?

Answer:[Yes]

Justification: We credits the owners of the dataset and python libraries that we used

Guidelines:

The answer NA means that the paper does not use existing assets.

The authors should cite the original paper that produced the code package or dataset.

The authors should state which version of the asset is used and, if possible, include a URL.

The name of the license (e.g., CC-BY 4.0) should be included for each asset.

For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.

If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswithcode.com/datasets has curated licenses for some datasets. Their licensing guide can help determine the license of a dataset.

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For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.

If this information is not available online, the authors are encouraged to reach out to the asset's creators.

13. New Assets

Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?

Answer: [Yes] .

Justification: The details about training, limitations are in the article, and the code is on Github.

Guidelines:

The answer NA means that the paper does not release new assets.

Researchers should communicate the details of the dataset/code/model as part of their submissions via structured templates. This includes details about training, license, limitations, etc.

The paper should discuss whether and how consent was obtained from people whose asset is used.

At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.

14. Crowdsourcing and Research with Human Subjects

Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)?

Answer: [Yes] .

Justification: We recruit two relatives to do the test. We give them instructions and spreadsheets with dialogue where they have to complete with the name of the characters

Guidelines:

The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Including this information in the supplemental material is fine, but if the main contribution of the paper involves human subjects, then as much detail as possible should be included in the main paper.

According to the NeurIPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.

15. Institutional Review Board (IRB) Approvals or Equivalent for Research with Human Subjects

Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?

Answer: [No] .

Justification: The two subjects that we asked questions were relatives that like those TV series immensely. There were no risks for them in answering the questions, therefore we do not proceed for an Ethical approval for such a small study.

Guidelines:

The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.

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We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurIPS Code of Ethics and the guidelines for their institution.

For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors present an analysis of logistic regression on sentence embeddings as a way to predict the speaker of a particular line of dialogue in the Big Bang Theory. Specifically, they fit a PCA model to embeddings obtained from a sentence transformer and then use each PCA dimension as a linear feature. The authors present some qualitative analysis of the most predictive PCA dimensions and some quantitative analysis of the classification accuracy. In addition, they present a brief analysis of the ability of GPT-4 to directly classify lines of dialogue and compare to a limited user study.\n\nSTRENGTHS:\nThe authors have identified an interesting and important debate in the AI community — more tests which help researchers discriminate between mere stochastic parroting and true generalization are certainly needed! In addition, the authors are very thorough in their description of the methods involved and their qualitative analysis of the PCA features is extensive. I also appreciate the thought the authors have given to the limitations of their study and the need for further work.\n\nWEAKNESSES:\nFirst and foremost, I feel that this paper needs to be much clearer and more focused in its research question. The introduction indicates that the objective of the study is to determine the extent to which the apparent ability of large language models like GPT-4 to generalize to novel tasks is actually attributable to their ability to parrot data from their training. However, a good part of the analysis appears dedicated to the specifics of The Big Bang Theory and the features of its dialogue. Section 3.1, for instance, extensively interrogates the PCA dimensions obtained from the sentence embeddings in a way that feels very specific to the particular dataset. Similarly, the conclusion raises claims that the ability for logistic regression to predict the speaker with reasonable accuracy is due to stereotyping in the characterization of the show. These claims are potentially warranted given the experimental evidence (though a more detailed and statistically-motivated analysis would be necessary to make such claims with certainty), but feel as though they belong in a different paper (a potentially quite interesting paper for a different venue, I should add). The connection between these results and the initial framing of LLM evaluation remain, unfortunately, somewhat murky. This is not to say there is no possible link between dialogue speaker prediction and LLM abilities! I encourage the authors to think about this problem more and articulate the specific claim they hope to interrogate.\n\nOn that note, and assuming that the main motivation is indeed to study large language models, I feel that the analysis could be strengthened. First, it would be helpful to justify some of the specific decisions made as part of evaluation. For instance, why were the Big Bang Theory and Friends selected over other possible dialogue datasets? Why was dialogue speaker prediction always studied between exactly two characters? Why were these specific characters selected? Do the characters have a similar amount of lines, or are there other statistical biases in the dataset that might affect the results?  When proposing a novel task, it’s important to make the assumptions and decisions that went into the task selection clear.\n\nWith regards to human evaluation, I encourage the authors to widen their study. That is to say, a user study which consists of only two participants (both of whom are related to one of the authors) makes it difficult to ascertain the reliability of the results. Indeed, I would suggest a study consisting of a larger number participants (ideally participants who do not have any externally motivating factors like relationships to the authors) so that a more general measure of human ability can be obtained. Further, I think it could actually be preferable for the participants to not have prior experience with the television show. This would make the test more an examination of the ability for participants to generalize their knowledge of personality traits to a novel situation instead of their ability to recall information (which is, ostensibly, closer to the desired research question in LLMs).\n\nDespite these critiques, I hope that the authors continue to refine their research question, justification, and methodology. There are interesting questions to study here!\n\nQUESTIONS:\n**Questions**\n- See above for questions on task selection\n- How does this classification task differ from those previously studied? e.g. those used in https://arxiv.org/abs/2309.07755 or https://arxiv.org/abs/2402.07470\n- Is there any effect of the prompt on the downstream results? (For GPT-4)\n- Is there any effect of random seed on the downstream results? (All models)\n\n**Notes**\n- The paper could benefit from an additional round of proofreading (e.g. “analyze” in Section 2.2 —> “analysis”, “annex” —> “appendix” throughout the paper)\n- The paper contains links to a few public GitHub repositories, which technically may violate double-blind review. The authors should be careful about including identifiable information in anonymous submissions\n\nLIMITATIONS:\nI feel that the authors have been very up front with the limitations of their work and have situated it in the context of broader impacts.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 1, "DATE": "2024-07-07"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors are focused on whether or not LLMs can be thought of as stochastic parrots or contain \"Sparks of AGI\". They look into what kind of data is recoverable from internal LLM representations. Specifically, the authors investigate to what extent the task of identifying TV personalities (e.g. Penny vs Sheldon) based on their dialogue lines is solvable using various methods. The authors compare a classifier based on PCA components extracted from existing LLM embeddings, GPT-4 zero shot performance, and human expert judgments. They find that all methods show fairly good performance, with human experts showing best results, followed by GPT-4, followed by the classifier. The authors also present a brief qualitative analysis, interpreting the more prevalent axes of variation in the embeddings identified using PCA.\n\nSTRENGTHS:\nThe authors tackle a very ambitious and important problem. The writing is clear throughout, and the authors provide extensive background for the methods they use.\n\nWEAKNESSES:\nI need to preface this by saying that I hope that my negative review does not discourage the authors from further pursuing the topic. I feel bad for having to reject this paper as it has some good ideas behind it and has an intention of researching a highly important problem. I hope that in next iterations, their work can be improved and expanded. At present, unfortunately, it does not match publication standards. I will try to explain why, and give pointers on how to potentially fix it in the future.\n\nThe biggest flaw of the paper is the experiment design. The authors never clearly define what exactly it means to be a \"stochastic parrot\" as opposed to \"general intelligence\". The authors also don't explain how their experiments would help to decide one way or another. So the results we have are impossible to interpret. It would help to go back to the original question and work through the argumentation more clearly. If the internal LLM representations have information about TV personalities, does it make them more or less of a stochastic parrot and why.\n\nOtherwise, the experiments give a very exploratory impression. For example the authors run PCA on sentence embeddings computed on their dataset and interpret the components. But it's unclear why and how this would help to answer the main question the paper attempts to answer.\n\nAdditionally, the paper's methods are extremely well-known, but unfortunately, the authors don't refer to relevant literature. The work highly overlaps with the topic of linear and nonlinear probes, as well as with the general theme of transfer learning. In essence, what the authors did can be described as adding a \"classification head\" to a pre-existing LLM. This is a very well-known technique.\n\nIf we want to gain new insights into what the models are doing, it is usually more interesting to look into the computations in intermediate layers of the model, rather than the last embedding layer. It is also often desirable to look at causal probes (rather than just a classifier).\n\nLastly, there are certain writing choices that deviate from common \"conventions\" in academic publishing. For example, oftentimes the authors go into excessive detail on well-known methods (explaining how PCA works and what a covariance matrix is). I highly suggest that the authors look at existing successful papers that use similar methods and copy their approach when it comes to decisions on what to explain in the main text, what to put into the appendix, and what to omit. The general rule of thumb is that newly introduced and important ideas should be at least briefly given in the main text, with extra details given in the appendix. Extremely well-known and established methods such as Principal Component Analysis don't need a full explanation, and a simple reference to the original source is enough.\n\nI really hope that the authors don't get discouraged and try to refine and improve their research in the future. The first starting point would be to more clearly define the problem, and to study in depth the existing literature on linear probes and probing in general, and on investigating what the internal LLM representations contain. One potential starting point is the paper \"Evaluating the World Model Implicit in a Generative Model\", Vafa et al. 2024 and related works.\n\nQUESTIONS:\nHow exactly do we go from studying internal representations to the conclusions about stochastic parroting? For example, there are Neuroscience works that show that one can recover a lot of what a person is thinking about (see the studies related to the \"Grandmother cell\" idea). A lot of visual input can be reconstructed as well. Does it make humans stochastic parrots as well?\n\nBasically, if GPT-4 can answer a given question (Penny vs Scheldon), we already know that its internal representations contain information needed to answer that. Same goes for human brains. I don't fully understand how it relates to the question of \"stochastic parrotedness\".\n\nLIMITATIONS:\nThe authors acknowledge some of the limitations of their study.", "TITLE": "Official Review", "RECOMMENDATION": 2, "REVIEWER_CONFIDENCE": 5, "SOUNDNESS_CORRECTNESS": 1, "DATE": "2024-07-11"}

comments: SUMMARY:
This paper’s main contribution is to apply a logistic regression on the principal components of the LLM embeddings for classifying TV series characters based on their dialog lines. The main finding is the logistic regression approach does worse than GPT-4 in predicting TA characters, but is comparable to human evaluations with two annotators.

STRENGTHS:
The paper focus on an interesting angle of using language model features for predicting the belongings of dialog lines of characters of TV shows.

WEAKNESSES:
The methodology of using logistic regression over PCA of language model embeddings is not novel, and there's no rigorous quantitative evaluations of the method beyond qualitative examples. The connection of the method and task to the broad discussion around "spark of AGI" and "Stochastic Parrots" is farfetched.

QUESTIONS:
Could you address the technical novelty of the method proposed?

LIMITATIONS:
The paper claims that "the contribution of the paper is primarily methodological, and their study is limited to a qualitative study of two very specific datasets." However, the method they adopt is a fundamental ML technique, which lacks novelty.
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 1
DATE: 2024-07-13

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper aims to prove LLMs work as \"stochastic parrots\" (Bender et al) rather than \"sparks of agi\" (Bubeck et al). To prove this claim, the paper presents an experiment where a task can be solved by training a linear model (logistic regression) on top of PCA of the LLM output. The authors then claim, based on the linear model experiments, that the LLM doesn't exhibit any sparks of agi due to the ability of (nearly) solving the task using linear models.\n\nSTRENGTHS:\nThe authors show a simple linear model trained on the output of an LLM for a given task is good enough to solve it, compared to using a GPT4 model, raising questions on the supposed intelligence often ascribed to the model.\n\nWEAKNESSES:\nWhile I generally agree that LLM's are closer to \"stochastic parrots\" than \"sparks of agi\", the claim that it can be proved using the proposed PCA experiments is weak to me. \n\n- Firstly, the embeddings are essentially the output of the LLM in question (all-MiniLM-L6-v2) - I would call it outputs rather than embeddings, as embeddings just indicate input word embeddings to the model, which clearly here isn't the case. \n- Secondly, the outputs itself being feature rich to be used for classification is unsurprising. It is expected the principal components of this embedding would be useful in predicting the properties of the task (as shown in the projection of PCA plots). This just shows the underlying model (SentenceBERT here) is good at extracting rich semantic and syntactic features from the input sentence (probing literature essentially proves that [1]).\n- Lastly, the experiment also shows the representations extracted from the sentence embedding model is sufficient for the task. For a harder task, if the linear probe on all-MiniLM-L6-v2 was not good with respect to GPT4, that would also not conclusively prove the ability of GPT4 is due to any sparks, rather it can be explained that GPT4's own embedding features are richer. That is, a linear probe trained on GPT4 embeddings for a harder task would also likely mimic its own performance. (this is theoretical, as neither the author or anyone other than OpenAI have access to their embeddings)\n\n[1] https://aclanthology.org/D19-1250/\n\nQUESTIONS:\nNone\n\nLIMITATIONS:\nThere are no explicit limitation section, however the last paragraph of conclusion discusses it.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2024-07-19"}