Toward Human Readable Prompt Tuning: Kubrick’s The Shining is a good movie, and a good prompt too?
Authors: Weijia Shi
Xiaochuang Han
Hila Gonen
Ari Holtzman
Yulia Tsvetkov
Luke Zettlemoyer
Venue: EMNLP 2023 Findings (2023)
Keywords: Prompt tuning
Analysis
Interpretability

Abstract: Large language models can perform downstream tasks in a zero-shot fashion, given natural language prompts that specify the desired behavior. Such prompts are typically hand engineered, but can also be learned with gradient-based methods from labeled data. However, it is underexplored what factors make the prompts effective, especially when the prompts are in natural language. In this paper, we investigate common attributes shared by effective prompts in classification problems. We first propose a human readable prompt tuning method (FluentPrompt) based on Langevin dynamics that incorporates a fluency constraint to find a distribution of effective and fluent prompts. Our analysis reveals that effective prompts are topically related to the task domain and calibrate the prior probability of output labels. Based on these findings, we also propose a method for generating prompts using only unlabeled data, outperforming strong baselines by an average of 7.0% accuracy across three tasks.

Toward Human Readable Prompt Tuning:
Kubrick's The Shining is a good movie, and a good prompt too?
Weijia Shi
Xiaochuang Han
Hila Gonen Ari Holtzman Yulia Tsvetkov Luke Zettlemoyer
Paul G. Allen School of Computer Science & Engineering.
University of Washington, Seattle, WA {swj0419, xhan77, hilagnn, ahai, yuliats, ]ecs washington.cm edu
Abstract
Pretrained LM Frozen
Large language models can perform down- stream tasks in a zero-shot fashion, given nat- ural language prompts that specify the desired behavior. Such prompts typically hand gineered, but can also be learned with gradient- based methods from labeled data. However, it is underexplored what factors make the prompts effective, especially when the prompts are in natural language. this paper, in- vestigate common attributes shared by effec- tive prompts in classification problems. We first propose human readable prompt tuning method (FL (FLUEETPRRMEE based on Langevin dynamics that incorporates fluency constraint find distribution of effective and fluent prompts. Our analysis reveals that effective prompts are topically related the task do- main and calibrate the prior probability of out- put labels. Based on these findings, we also propose a method for generating prompts using only unlabeled data, outperforming strong base- lines by an average of 7.0% accuracy across three tasks. We release code and data in 10luuuttool//
this movie was
Test example from IMDb
AutoPrompt
disfluent
screamed aggrav "creame!
FluentPrompt annnyl Kubrick's The Shining
domain
relvance
Kubrick is the director disappointing negative rating
calibration
Figure 1: Compared with previous discrete prompt tun- ing method AutoPrompt (Shin al., 2020) which gen- erates gibberish prompts, FLUENTPROMPT can identify effective and more readable prompts, useful for down- stream analyses. We find that in prompt tuning, the good prompts are topically relevant to the task do- main (e.g., mentioning a film director "Kubrick" a movie sentiment classification task), and calibrate the prior probability of output labels (e.g., including neg- ative words to balance an overly optimistic model).
1 Introduction
Large language models (LMs) can perform down- stream tasks by simply conditioning on prompt-a short sequence text specific to the task. Such nat- ural language prompts are either carefully hand en- gineered (e.g., manual prompt engineering, Kojima et al. 2022) automatically learned from labeled data (e.g., gradient-based prompt tuning, Shin et al. 2020). Despite their effectiveness, it remains clear what makes these prompts work and what attributes effective prompts share. In this paper, we aim to identify key characteristics of effective prompting, and use this knowledge to generate ef- fective and human readable prompts without any labeled data.
There are two main challenges for performing this type of analysis. First, manual prompt tuning produces a limited number of effective prompts for each task, making it difficult to infer common features of good prompts where contrast with less effective prompts is needed. On the other hand, the prompts found by gradient-based tuning meth- ods are often disfluent and unnatural, making them difficult to interpret (e.g.. AutoPrompt Figure 1).
To overeome these challenges, first proposc human readable prompt tuning method called FL FLUENNPROMPT. Inspired by prior work in con- trollable text generation (Kumar al., 2022), FLU- ENTPROMPT uses Langevin dynamics to generate a set of human readable prompts for any task. Our
"qqul contribution. Order randomly determined.

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method adds progressive noise to the tuning pro- cedure to obtain distribution of effective prompts, while also maintaining the fluency of the prompts through a perplexity constraint. As shown in Fig- ure I, compared to the baseline gibberish prompts produced by AutoPrompt, FLUENTPROMPT gen- erates prompts that are more fluent (i.e., lower per- plexity) and perform competitively. The resulting fluent prompts not only facilitate our further analy- sis, but can also lead better trust and engagement from both researchers and end users.
prompted language model (Qin and Eisner, 2021; Ding et al., 2021; Lester et al., 2021: Liu et al., 2021). Such continuous prompts are typically tuned by gradient-based methods, which are guided by the task training examples with labels. While these prompts usually improve the model per- formance, their continuous nature makes them difficult for humans to understand interpret (Khashabi et al., 2021; Hambardzumyan al., 2021).
Discrete Prompt Discrete prompts com- posed of discrete tokens from natural language vo- cabulary. Such prompts can be either written by human or searched automatically, Human-written prompts (Kojima et al., 2022; Wang et al., 2022; Sanh et al., 2021; Su et al., 2022) typically con- sist of meaningful texts such as task descriptions (Schick and Schütze, 2021) or instructions (e.g.. "let's think step by step", Kojima et al. 2022), which only human readable but also align with our understanding of tasks. In In-context demonstration examples can also be considered as
Afte obtaining a broad set of effective and human-readable prompts, we analyze the factors that contribute to the effectiveness of prompts. Specifically, we show that effective prompts are both (I) topically related to the task domain and (2) more calibrated to the prior probability of output labels. Specifically, calibration measures how balanced the L.''s prior probability of output labels (i.e., the absence of a specific example) is. Based on our findings, we propose novel method UNSUPERVISED FLUENTPROMPT, for au- tomatically searching for effective prompts using only unlabeled data. UNSUPERVISED FLUENT- PROMPT optimizes the prompts for both better cal- ibration and better domain relevance. Our exper- imental results show that UNSUPERVISED FLU- ENTPROMPT outperforms strong zero-shot base- line (Holtzman et al., 2021) by 7.0% in accuracy. We summarize our contributions as follows:
prompts (Brown et al., 2020; Liu et al., 2022) but are not the focus of this work.
Prior work has also focused on searching dis- crete prompts automatically. One method is gradient-based similar the continuous prompt setup but with projections to a discrete vocabulary (Shin et al., 2020). The drawback of this method is that the resulting prompts are usually disfluent and difficult to read. Other work searching for discrete prompts include edit-based enumeration (Prasad et al., 2022), reinforcement learning (Deng et al., 2022), and large language model continua- tion and filtering (Zhou et al., 2022). The goal for these prompt tuning methods is mainly to achieve competitive task performance without modifying language model parameters.
We introduce FLUENTPROMPT, human- readable prompt tuning method that can gener- ate a broad set of effective and fluent prompts ($3). This method not only serves as the foun- dation for our analysis, but also helps bridge the gap between manual prompt engineering and gradient-basee prompt tuning.
We analyze the factors that contribute to the effectiveness of prompts and show that topic relatedness and calibration of the prompts are key to their success ($4).
The purpose of our work is to analyze what as- pects of the tuned natural language prompts make them effective for zero-shot inference of language models. To facilitate such amalysis, need prompt readability in
prompts and also large search space as in gradient-based discrete prompt tuning. FLUENTPROMPT bridges the gap and provides a distribution of effective and human- readable prompts.
Inspired by our findings, we introduce new method for discovering effective prompts without the need for labeled data ($5).
2 Related Work
2.1 Prompt Tuning
2.2 Analyses of Prompts
Continuous Prompt Continuous prompts are continuous vectors inserted to the task input for
A growing body of literature tries to understand the mechanisms behind prompts via various per-

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spectives. For example, prompts in the form of in-context examples are analyzed under per-
label word. ! Specifically, the probability of a label y given an input æ and template is estimated by:
turbations w.r. order, label, editing, etc. (Lu et al., 2022; Min et al., 2022; Chen et al., 2022). Human-written instructions (Mishra et al., 2021) have also been studied and show weak sensitivity to semantic-changing perturbations (Webson and Pavlick, 2021). Gonen et al. (2022) use paraphras- set of human-writteni
exp logito (v(y) x
po(v(y) x.,) Ey exp logito (o(y') x
Lester al. (2021) add a sequence of M soft embeddings ég:M (simplified as é; O:M refers the positional subscript for the sequence from po- sition 0 to M - 1) in front of the input. There- fore, the probability of the label is computed by Po(u(y) é, æ, t), where è is embeddings that by- pass the word embedding layer of the LM 0 and is learned based set of training data. These learned embeddings sometimes referred to soft prompts, and the learning of such prompts as soft prompt tuning. For example, stochastic gra- dient descent (SGD) used as an optimizer, the soft prompt é is updated as
ing and back-translation prompts and find on these natural prompts there is a correlation between lower perplexity and better resulting performance.
Our work focuses natural language prompts derived from gradient-bar based prompt tuning. Khashabi et al. (2021) tune continuous prompts and show that effective continuous prompts may transfer poorly to their nearest discrete prompts. In contrast, we perform prompt tuning in the discrete space directly with FLUEENPROMPT, demonstrat- ing the feasibility of searching for readable prompts using gradient- based method. This approach gives more faithful understanding of the factors that contribute to the effectiveness of natural language prompts.
ē' =
- v/Ve(
log (og))()()) ēi-1 "0, t))
(2)
where i is the timestep superscript, referring to i-th optimization step, and vj is the learning rate.
3.2 Method
3 FLUENTPROMPT
3.2.1
Discrete prompt tuning with Langevin dynamics
We introduce FLUENTPROMPT, prompt tuning method that generates group of highly effective and humanereadable prompts. Our approach uti- lizes Langevin dynamics to incorporate fluency constraints into the prompt tuning process, mak- ing it novel application of controllable text gen- eration and constrained sampling within the field of discrete prompt tuning. With hemaabreadable prompts, aim to explore the relationship be- tween the features of the prompts and their perfor- mance.
There are two challenges for the above soft prompt tuning. First, the resulting embeddings cannot be mapped to the natural language vocabulary. Khashabi et al. (2021) show that naively mapping an effective soft prompt to its nearest tokens sig- nificantly drops the performance. Second, we only obtain a single embedding instead of range of embeddings with varying levels of performance. This makes it difficult to analyze the characteristics of the prompts and compare their effectiveness in specific tasks.
3.1 Background: continuous prompt tuning Given an input example æ with an output label y E Y, we can prompt an autoregressive language model with parameters 0 as follows. We refor- mulate the task as language modeling problem by inserting task-specific template t to æ, and defining verbalizer mapping from a label y to label word (i.e, token in the LM's vocabulary that semantically implies the label). For example. to determine the sentiment of "I like the movie". we can pass like the movie. It was [MASK]" to the LM and inspect the probability of "good" as a
Following Kumar et al. (2022), we use Langevin dynamics to sample discrete prompts that lead to a better performing model in the task. Overall, the method is similar to SGD but adds a progressive Gaussian noise to the embeddings, with the scale decreasing over time. Additionally, each opti- mization step, the updated embedding is projected to the nearest embedding in the LM vocabulary.
è' = Projele (é)
E 1)+ 21
'Table 7 shows the exact templates and verbalizers used throughout this work.

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where:
exp(hamm-1 Bm)
E is an energy function (lower is better),
pe(ém @0:m)
(4)
E(e 1) po é:-1_,_00 ,x,t).
Le,eE exp(ha,m-1 ev)
where we equivalently compute the logits for each embedding's corresponded vocabulary and take the softmax." Subsequently, the sequence probability is pa (e. Im pe(êm ē0:m).
is Gaussian noise, (O,TT)
3 is the variance of the noise following geo- metric progression, Sstart 33 Bend + O.
We define a prompt fluency loss as the neg- ative log-likelihood of the prompt embeddings, log lo (o(eo:))) Along with the task labeling loss ($3.2.1). we modify our energy function as:
E is the embedding table (layer) of the LM one embedding for each token in the vocabu- lary.
Proje is projection operation finding nearest neighbor for each soft embed- ding the LM's vocabulary, (e) argmin áll2).
E {(e:M) = Ausk log pa (v(() ē0:as: 2,
Auunnc log pa (@o.M)
(5)
where Atask Amuency 1. Through the whole FLUENTPROMPT tuning procedure, the language model parameters @ are fixed while the embeddings 60.M are tuned.
Without the progressive noise in Langevin dy- namics, our prompt search procedure is gradient- based and shares a similar intuition with Auto- Prompt (Shin et al., 2020). Both methods use the gradient of the loss w.r.t. the embeddings, though AutoPrompt applies greedy substitution whereas we use projected gradient descent, aligning with soft prompt tuning and enabling the subsequent prompt sampling. AutoPrompt also incorporates verbalizer word selection, which is not a focus of the analysis in this work. We use our gradient- based, discrete prompt tuning method without Langevin dynamics baseline, referred to as AutoPromptsaD
3.3 Experimental Setup
Target tasks We evaluate performance on two sentiment analysis tasks: Amazon Polar- ity (McAuley and Leskovee, 2013) and SST- 2 (Socher al., 2013), and one topic classi- fication task: AGNEWS (Zhang et al., 2015). These tasks were selected since vanilla soft prompt tuning (Lester et al., 2021) substantially im- proves model performance. In contrast, tasks like RTE (Dagan et al., 2005) are more difficult; soft prompt tuming did not yield a significant improve- ment (57.4% accuracy from prompt tuning com- pared with 52.1% from random guess) in our pilot study, and therefore did not pursue further anal- ysis using FLUENTPROMPT. The verbalizer words and templates used for each task are listed in Ta- ble
3.2.2 Fluency constraint
Sampling from projected Langevin dynamics en- sures that the tuned prompt contains natural lan- guage tokens. However, with no extra constraints, they can form a disfluent sentence.
We explicitly incorporate fluency objective to the Langevin energy function. This objective re- sembles the regular perplexity loss, but the labels (next token in the prompt) are not ground-truth. In- sequence
Model We optimize prompts for GPT-2 large (774M parameters, Radford et al. 2019) using FLU- ENTPROMPT. We use a batch size of 16 and train for 5,000 steps with AdamW optimizer (Loshchilov and Hutter, 2018). We select the best prompt based on the validation performance. For our method FLUENTPROMPT, we use a step size E (0.3, 1.0, 3.0, 10.0), Satr = 1.0, Aend = 0.0001, Auucncy E (0.003, 0.01, 0.03, 0.1, 0.3). We search for both 5-token prompts (M 5) and 10- token prompts (M = 10) and use 10 random seeds
stead, we measure an embedding- probability according to Kumar et al. (2022). For simplicity, below we drop the timestep superscript on the prompt embeddings and only keep the posi- tional subscript.
The first step is to obtain the probability of gen- erating the embedding position m (i.e., ēm) based on the previous m 1 embeddings (i.e..
e0.m). We extract the last hidden state from the LM (i.c., output embedding) at position m 1:
Thhis is equivalently computing the logits since e, and the projected e.. from the last optimization step are both in the embedding table.
hg = h. Then the probability is:

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Prompt
Acc.
PPL
1f E (0.3, 1.0, 3.0, 10.0) and 10 random seeds. AutoPrompIscn does not have perplexity con- straint over its prompt tuning process (Afuuecy = 0). For FLUENTPROMPT, we apply an optimal per- plexity constraint at Aftuency = 0.003, 0.003, 0.01 for SST-2, Amazon, and AGNEWS, respectively. We observe that on Amazon, FLUENTPROMPT achieves both better average prompt accuracy and better maximum accuracy. On SST-2 and AGNEWS, FLUENTPROMPT also achieves better average accuracy, while having nearly as high maximum accuracy as AutoPromplsca> For all three tasks, FLUENTPROMPT leads to prompts with a significantly lower perplexity (p < 0.0001 in t-tests). Without sacrificing performance, prompts with lower perplexity are preferred for their potentially better readability for downstream analyses.
SST-2
Empty Prompt
66.5
AutoPrompiscn
Compl disgustingAAAA Rated jer
87.6
10
FLUENTPROMPE
Kubrick, "The Shining Paramount, "The Shining
87.5
13.1
86.8
12.2
Kubrickt's "The Man
86.3
9.3
disappointing.. Inin"
84.4
4.1
AMAZON
Empty Prompt AutoPrompiscn Reviewed experien audition lashesrible
75.8
82.2
10
ILUENTPROMPT
scathing.Aulo An'a"
83.1
5.1
upset.ln'n"
82.6
3.67
cigars: 'unan
82.4
20.9
mascarakn'nin
82.2
47.1
AGNEWS
Empty Prompt AutoPromplyca
49.7
69.3
>
10
FLUENTPROMPT
4 What Makes Good Prompts?
netfix//eenrennttiixx netflix AnimeMoviegenrelnn
71.1
281.0
70.1
1925.0
In this section, we analyze common attributes of the effective tuned prompts. Specifically, we study the 10-token prompts found by FLUENT SST-2, Amazon and AGNEWS.
Synopsishnln The story
69.2
9.6
pmwiki.php/mainne phw.iippphmmainnSuueeeerooo
65.0
2.4
Table Accuracy (Ace.) and Perplexity (PPL) of prompts. Both FLUENTPROMPT and AutoPrompisan use M=5 tunable tokens. FLUENTPROMPT shows com- parable performance the AutoPrompssao but with significantly lower perplexity. Prompts discovered by FLUENTPRoMPT show domain relevance and potential calibration for model outputs.
4.1 Effective prompts calibrate the output distribution over label words
Language models are known to be biased towards label words that are common in its pretraining dis- tribution (Holtzman al., 2021; Zhao et al., 2021). In this section, we aim to investigate whether ef- fective prompts found by prompt tuning implic- itly adjust for the bias (calibration). To measure this bias, we follow Holtzman et al. (2021) to use task-specific domain string d as the test input and compute the entropy of the labels. Table lists the fask-specific domain d for each dataset. As the task-specific domain strings do not imply any label information (i.e., label-neutrall we expect the out- put of the language model to be uniform over the label words when only conditioned on the domain string. The entropy of the label words is computed as follows:
for each hyperparameter setup. Additionally, we perform experiments with Bstart Bend 0 (i.e, no progressive noise) and Aftuency (i.e, no flu- ency constraint) as ablations FLUENTPROMPT purposed for analysis.
3.4 Results
Table 1 shows example prompts found by AutoPromptsGD and FLUENTPROMPT, along with their associating accuracy and perplexity. We ad- ditional show the accuracy of an empty prompt (i.e., è null). We see that FLUENTPROMPT per- forms comparably to AutoPrompiscn and signifi- cantly better than the empty prompt. In terms of readability, FLUENTPROMPT generates more flu- ent prompts than AutoPrompiscD- In Table 2, we quantitatively compare AutoPromptsep and FLUENTPROMPT.. each task, we use each method to generate 40 prompts with a length M = 10, under 4 step sizes
H(v) = E loy)(
Σ po((()) ē, d, logro(v(s)) @ē t)
DEY
(6)
For
Higher entropy of the label word prediction implies a more balanced (calibrated) label words distribu-

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SST-2
Amazon
AGNEWS
log Acc
log
Max
log PPL
AutoPromphzap FLUENTPROMPT 87.54
84,9 .99
90.48
13,89
83.36
86.95
13.33
69.74
80.50
15.68
90.14
9.27
85.20
88.20
10.20
73.34
79.50
10.93
Table 2: Prompl effectiveness and perplexity of sutoPrompiscn and FLUEENT Each model derives 40 prompts with a length of 10. AutoPrompisad does not have the progressive noise & and the perplexity constraint (ABueccy 0). PLUMPTPROMPT applies the perplexity constraint with Aluency = 0.003, 0.003, 0.01 for SST-2, Amazon, and AGNEWS, respectively. The prompts found by FLUENTPROMEN are overall more effective and have significantly lower perplexity, indicating better readability.
S51-2
Amszon
AGNEWS
30
Prompet (acr> 80.0%1
Prompe (Atr=-B0.00m 00.0%
Prompt tacc= 70.03
Tmetty Promott (ic 575)
Tmety Fromot 75 315) Human Frompt (3.c=73.7%)
Enppy Hompt (0c0e43
Human Frompt (acc-35.99) (5.5
Human Prompt laci 48, 7541
20-
10-
1.0
00
0.25
1.00
1.25
teopy words
Entrogy
Eitropy Wonds
(a)
(c)
Figure 2: Frequency of prompts (y-axis) at different entropy levels (X-axis). We compare effective prompts with the empty and human-written prompt.
Task
Domain String d
lower accuracy.
SST-2
This movie review
To further explore the relation between the task performance and calibration, we compute corre- lation between the task accuracy and the label word entropy all prompts obtained by FLUENT- PROMPT and report Spearman's 's rank correlation. From Figure 3, we observe that the label word en- tropy exhibits significant positive correlations with the task accuracy (all p < 0.0001). The Spearman's coefficients +0.61, +0.75 and +0.43 for SST-2, Amazon and AGNEWS, respectively.
Amazon AGNEWS
This is Amazon product review
This is news
Table 3: Tasks and their task-specific domain strings. The task-specific domain strings do not imply any label information.
tion. When the label word probabilities uni- form, the entropy reaches its maximum at log([Y]). As listed in Table 1, some effective prompts found by FLUENTPROMPT for sentiment analy- sis contain negative sentiment words (e.g., "disap- pointing" and "complained" in prompts for SST-2 ), which may implicitly reduce the probabilty of posi- tive labels and calibrate the label word distribution. To validate this hypothesis, we filter a set of effec- tive prompts by FLUENTPROMPT and compute the entropy of the label predictions conditioned on the concatenation of the prompt and the task-specific domain string. Figure 2 shows the density plot com- paring the label word entropy of effective prompts. with empty and human-written prompts taken from Bach et al. (2022). We observe that the entropy of effective prompts has a higher mode than the entropy of empty and human-written written prompts with
4.2
Effective prompts are topically related the task domain
Qualitative Analysis As shown in Table most of the effective prompts obtained by FLUENT- PROMPT contain domain-related words. For ex- ample, the prompt Kubrick, "The Shining in SST- contains a movie director name and movie title, relevant to the domain of movie reviews. Similarly, the prompts mascarawiv and cigarsvivv found for the Amazon datasct contain product names relevant to the domain of product reviews. Additionally, AGNEWS is news topic classifica- tion task. Some of the effective prompts in AG- NEWS contain topic classffication-rlaatee words such as "genre", while others contain URLs that

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SST-2
Amazzo
11.00
0.80
0.15
0.85
Scounees
0.82
0.88
0.80
0.60
0.75
0.5
0.6
0.7
0.3
0.9
1.0
0.2
0.4
0.5
0.3
1.0
0.
0.6
0.8
1.0
122
1.4
1.6
Brkropy of Label Words
Entropy Lobel Werds
Enkrasy of Label Marrss
(a)
(h)
(c)
Figure 3: Correlation between task performance and label word entropy. Spearman rank correlation coefficients for SST-2, Amazon and AGNEWS +0.42, +0.76 and +0.57. All p-values smaller than 0.0001.
link to websites such as netflix and pmwiki.* The target pages of these URLs also contain topic classificatio--rllteed information, such as the prompt pmwiki/pmwiki, http:////wwiiiiiihph/aaaaureeeer which links to wiki page containing the following information: "Genre: Action Adventure Comedy Commercials""
Task
Domain Words
SST-2 Amazon
movie, film, cinima, director, positive, negative book, amazon, product, furniture, positive, neg- ative
AGNEWS
topic, category, politics, sports, business, tech- nology
Table 4: Domain words for each task.
Quantitative Analysis Based on our qualitative analysis, we hypothesize that effective prompts are topically related to the task domain. To validate this hypothesis, we compare domain word frequency in effective prompts and random sentences. First, we select a set of domain words for each task (see Table 4), which consist of the task label words (e.g., "positive" and "negative" for SST-2) and com- mon words in the task domain (e.g.. "movie" and "film" for the movie domain of SST-2). Since our prompts are very short (10 tokens), we augment each prompt with its continuation generated by GPT-3 (Brown et al., 2020), based on the assump- tion that the continuation by the large LM follows the same domain as the prompt. For each prompt, sample distinct continuations from GPT-3 us- ing nucleus sampling p 0.95 at length of 100 tokens. We compare the top 10 effective prompts with 10 random sentences from PILE (Gao al., 2020) augmented by the same continuations. We then count the domain words in the concatenation of the prompt and its continuation.
SST-2
Amazon
AGNEWS
Acc.
Freq.
Acc.
Freq.
Acc.
Freq.
Effective Random
89.4
23,4
86.5
5.8
77.6
3.7
67.2
1.3
74.2
2.2
49.3
0.8
Table 5: Average domain words frequency (Freq.) and average accuracy (Acc.) for effective prompts and ran- dom sentences. Effective prompts and their continua- tion contain substantially more domain words than random sentences and their continuation. The p- values from the paired t-test for SST-2, Amazon, and AGNEWS were 0.004, 0.003, and 0.0002, respectively.
significantly higher than that of random sentences with p-values of 0.004, 0.003, and 0.0002 for SST 2, Amazon, and AGNEWS, respectively. Both our qualitative and quantitative analysis provide strong evidence that effective prompts obtained by prompt tuning are topically related the task's domain.
5 UNSUPERVISED FLUENTPROMPT
Table lists the average accuracy and the num- ber of domain words in the effective and the ran- dom sentences with their continuations. The ac- curacy of effective prompts is higher than that of random sentences on all three datasets. Moreover, the domain words frequency of effective prompts is
Our analysis in Section 3 shows that effective prompts exhibit calibration and have high domain relevance the task. Since these two features are both highly indicative and do not require ground- truth labels for computation, we propose UNSU- PERVISED PRUPN,, method for auto- matically identifying effective prompts without la- beled data. The key idea is to optimize the prompts
wwww.netflix.com www..pmwiki.org

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for improved calibration and domain relevance. In the following sections, we will detail the methodol- ogy of UNSUPERVISED FLUENTPROMPT ($5.1), describe the experimental setup ($5.2), and present the results ($5.3).
SST-2
Amazon
AGNEWS
Unsupervised Method
Emipy
66.5
75.8
49.7
PMInc
85.6
76.2
64.1
UNSUP F.P.
88.2
85.3
68.0
Table 6: Accuracy of different unsupervised prompting methods the three datasets. UNSUP. F.P. refers our UNSUPERVISED FLUENTPROMPT.
5.1 Method
Calibration loss In Section 4.1, we find a strong positive correlation between the degree of calibra- tion and performance of the prompts. We there- fore explicitly optimize the prompt towards greater calibration (i.e., maximizing the entropy of label words). Ideally, we need large set of label-neutral domain strings to prevent the model from learn- ing noises in the procedure. Since these domain strings are not always easy to obtain, we use the training inputs of the task (without ground-truv truth labels), and expect that the aggregation of them should be label-neutral. Therefore, we define calibration loss based on the entropy of the label words distribution:
5.2 Experimental Setup
Inheriting the notations of FLUENTPROMPT, we
consider the following hyperparamcters: 7/ É (1.0,
3.0), Saat = 1.0, Bend = 0.0001, Adomain E (0.
0.0003, 0.001, 0.003, 0.01, 0.05, 0.2, 0.5), M =
10. We use five random seeds for each setup and
report the average performance.
5.3 Results
In Table 6, we compare the performance of our pro- posed method, UNSUPERVISED FLUENTPROMPT, with the empty prompt and the strong unsupervised baseline PMI calibration PMIpc (Holtzman et al., 2021) on three datasets. Our results show that UN- SUPERVISED FLUENTPROMPT consistently out- performs PMIDC with an average improvement of 7.0% across the datasets. This demonstrates that
Centropy (ē) = EyyEY [log E (x) èē x)]
Intuitively, the calibration loss encourages the prompt to help the model generate more balanced predictions at dataset (macro) level rather than instance (micro) level.
Domain relevance loss In Section 4.2, find that effective prompts are overall more related to the task domain. To explicitly make the prompt rel- evant to the domain, we extend the existing fluency (perplexity) loss from Section 3.2.2, modeling the perplexity of both the prepending prompt and the input example:
the incorporated calibration and domain informa- tion are helpful to finding effective prompts.
6 Conclusion
In this paper, we investigate the factors that con- tribute to the effectiveness of prompts. To facilitate (8) this study, we develop human-readable prompt tuning method FL.EETT GPT-2 large model to generate effective and read- able prompts. Our analysis reveals that effective prompts are topically related to the task domain and calibrate the prior probability of label words.
Ldomain (é) -- log pe (2O.M)
(7)
Σ pogpo(a; è, xx))
and apply it to the
Elog Clogpo(ty ē, x, bcy)
Intuitively, (o 2) log would mea- sure the pointwise mutual information between the task data æ and the tuned prompt ē, with the part log (x()) not involved in the prompt optimization. Overall, our unsupervised energy function E is updated to:
A Although the prompts generated by FLUENT- PROMPT are effective and readable, they still carry limited semantic meanings. For instance, we did not find any prompts directly indicating the task definition or instructions. One potential reason is that the GPT-2 large model is not instruction- tuned. Future work can apply FLUENTPROMPT to an instruction-tume model to see instruction prompts can be discovered.
E(eu:as) Ceatibrationn-enttee
(10)
Adomain Edomain
(11)
where Agatibration Astomaii =

---

Limitations
nual Meeting of the Association for Computational System Demonstrations, pages 93-104,
Linguistics:
The FLUENTPROMPT approach is versatile method for optimizing human-readable prompts in both classification and generation tasks. How- ever, our investigations into calibration are specific to classification tasks. It would be intriguing to explore the characteristies of effective prompts in generation tasks in future studies. It is worth not- ing that FLUENTPROMPT employs Langevin dy- namics incorporate perplexity constraints during training, making it directly applicable autoregres- sive models and not to masked language models or encoder-decoder models.
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neclakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners. Advances in neural information processing systems, 33: 1877-1901.
Yanda Chen, Chen Zhao, Zhou Yu, Kathleen McKeown, and He Hc. 2022. On the relation between sensitivity and accuracy in in-context learning. arXiv preprint arXiv:2209.0766
Ido Dagan, Oren Glickman, and Bernardo Magnini. 2005. The pascal recognising textual entailment chal- lenge. In Machine learning challenges workshop, pages 177-190. Springer.
Due to resource limitations, we applied FLUENT- PROMPT to GPT-2 large. Our current GPU was not sufficiently efficient to handle larger models within our budget. It is important note that our focus was not solely on performance, but rather on ana- lyzing the properties of effective prompts. In the future, it would be valuable to extend our method to diffcrent-sized language models and explore alter- native constrained sampling techniques to identify fluent and effective prompts for various types of language models.
Mingkai Deng, Jianyu Wang. Cheng- Leong-Ping Hsich, Yihan Wang, Han Guo, Tianmin Shu, Meng Song. Eric P Xing, and Zhiting Hu. 2022. Riprompt: Optimizing discrete text prompts with reinforcement learning. arXiv preprial arXiv:2205. 12548.
Ning Ding, Shengding Hu. Weilin Zhao, Yulin Chen, Zhiyuan Liu, Hai-Tao Zheng, and Maosong Sun. 2021. Openprompt: An open-source framework for prompt-learning. CoRR, abs/2111.01998.
Leo Gao. Stella Biderman, Sid Black, Laurence Gold- ing. Travis Hoppe, Charles Foster, Jason Phang, Ho- race He, Anish Thite, Noa Nabeshima, al. 2020. The pile: An 800gb dataset of diverse text for lan- guage modeling. arXiv preprint arXiv:2101.0000
Acknowledgements
We gratefully acknowledge support from NSF CA- REER Grant No. ITS2142732 This material is funded in part by the DARPA Grant under Contract No. HR001 10C01224 This research is supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Re- search Projects Activity (IARPA), via the HIATUS Program contract 2022-22072200004. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of ODNI, IARPA, or the U.S. Gov- ernment. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein.
Hila Gonen, Srini Iyer, Terra Blevins, Noah A Smith, and Luke Zettlemoyer. 2022. Demystifying prompts in language models via perplexity estimation. arXiv preprint arXiv:2212.040.7.
Karen Hambardzumyan, Hrant Khachatrian, and Jonathan May. 2021. WARP: Word-level Adversarial ReProgramming. In Proceedings of the 59th Annual Meeting of the Association for Computational Lin- guistics and the 11th International Joint Conference Natural Language Processing (Volume 1: Long Papers), pages 4921-4933, Online, Association for Computational Linguistics.
Ari Holtzman, Peter West, Vered Shwartz, Yejin Choi, and Luke Zettlemoyer. 2021. Surface form com- petition: Why the highest probability answer isn't always right. In Proceedings of the 2021 Conference Empirical Methods in Natural Language Process- ing, pages 7038-7051, Online and Punta Cana, Do- minican Republic. Association for Computational Linguistics.
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A Verbalizer and templates
Table shows an example input, template and the verbalizer used for each task.
Albert Webson and Ellie Pavlick. 2021. Do prompt- based models really understand the meaning of their prompts? arXiv preprint arXiv:2109.014 102.07247..
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Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, and Jimmy Ba. 2022. Large language models are buman-level prompt engineers. arXiv preprint arXiv:2211.01911

---

Task
Template
Label words through verbalizer
SST-2
Illuminating overly talky documentary. It was
positive, negative positive, negative politics, sports, business, technology
Amazon AGNEWS
Terrible service. It was
Economic growth in Japan slows down as the country experiences. is about
Table 7: The template, example (colored black) and verbalizer used for each dataset.


Reviews:
[{"review_id": "hrA83v3Db5", "reviewer": "Reviewer_HPcZ", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 3, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Due to the number of reviews we got for this work, we have to prioritize some common questions raised in all reviews and make a general response to all reviewers. However, we will be super happy to discuss and follow up here if you think any of your crucial concerns are not addressed. Thank you so much! We additionally thank your suggestions on presentation improvements.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "The approach is both straightforward and efficient, effectively addressing the issue of prompt interpretability which holds substantial practical significance. \n\nThe paper not only analyzes the elements contributing to prompt effectiveness but also demonstrates significant improvements in experimental results.", "reasons_to_reject": "1. The absence of a complexity analysis is notable. High computational complexity could significantly hinder practical applicability. \n\n2. Moreover, the reliance on a single language model as the backbone is limiting, potentially confining the proposed model's effectiveness solely to GPT-2 large, reducing the persuasiveness of experimental outcomes. \n\n3. Additionally, the limited baseline, restricted to AutoPrompt, further weakens the paper's credibility.\n\n4. No code and data provided， lacking of reproducibility of experimental results.\n\nWith these concerns in mind, I kindly suggest revisiting and addressing these issues comprehensively to enhance the overall quality and rigor of the paper before reconsidering it for acceptance. Your efforts in refining these aspects would undoubtedly contribute to a more robust and impactful submission.", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper solves the problem about prompt interpretability, and introduce FLUENTPROMPTF, a human-readable prompt tuning method that can generate a broad set of effective and fluent prompts, bridging the gap between manual prompt engineering and gradient-based prompt tuning. Besides, they analyze the factors that contribute to the effectiveness of prompts and show that topic relatedness and calibration of the prompts are key to their success.", "excitement": 3, "reproducibility": 2, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "Automatic Chain of Thought Prompting in Large Language Models \n\nLarge Language Models Are Human-Level Prompt Engineers\n", "Typos_Grammar_Style_and_Presentation_Improvements": "line 028: \"langauge\" -> \"language\"\nline 271-272: \"langauge\" -> \"language\""}, "extra_text": {}}, {"review_id": "v9px7hCzdG", "reviewer": "Reviewer_RhgP", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 2, "soundness": 2, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Due to the number of reviews we got for this work, we have to prioritize some common questions raised in all reviews and make a general response to all reviewers. However, we will be super happy to discuss and follow up here if you think any of your crucial concerns are not addressed. Thank you so much! We additionally thank your suggestions on presentation improvements.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "- The author conducted experimental analysis on \"What makes good prompts.\"\n\n- An enhanced method is proposed for automatically identifying effective prompts without labels.", "reasons_to_reject": "- The implementation is based on GPT-2, which is not currently the state-of-the-art method, thereby diminishing the contribution of the work.\n- This work lacks ablation analysis to analysis sereval crucial hyper-parameters, such as the lambda.\n- The results of UNSUPERVISED FLUENTPROMPT are presented without substantial discussion.\n", "questions_to_authors": "", "paper_topic_and_main_contributions": "This study presents a human readable prompt tuning approach for LLMs, aiming to generate prompts that are both effective and fluent.\n", "excitement": 3, "reproducibility": 2, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": " The formatting in the appendix appears to have some issues.\n"}, "extra_text": {}}, {"review_id": "P6udPfn3rf", "reviewer": "Reviewer_Uwpm", "summary": "", "questions": "A. I am unsure if I have correctly understood Table 5, which displays accuracies for effective prompts that are higher than Table 2. If Table 5 shows effective prompts + GPT-3 continuations (resulting in a 100 token prompt), then does this imply that augmenting prompts with LLMs leads to improved performance over your FluentPrompt method? ", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Due to the number of reviews we got for this work, we have to prioritize some common questions raised in all reviews and make a general response to all reviewers. However, we will be super happy to discuss and follow up here if you think any of your crucial concerns are not addressed. Thank you so much! We additionally thank your suggestions on presentation improvements.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "Good insights: Section 4 (What makes good prompts?) offers greater understanding of the properties of good prompts (topic relevance + label word calibration). This can aid the NLP community in understanding why prompt tuning is effective. \n\nNovel methods: the paper proposes two methods (supervised and unsupervised) for prompt tuning that outperform baselines. In addition to accuracy increase, the human-fluency achieved by these generated prompts can aid researchers in interpreting their prompts and lead to greater awareness of potential model/dataset issues (e.g. uncovering label word bias).\n\n", "reasons_to_reject": "1.Proposed method is not entirely successful: as noted by the authors, despite achieving lower perplexity than baselines, the 'human-readable' prompts remain somewhat cryptic and limited in semantic meaning. \n\n2.Lack of comprehensiveness: the FluentPrompt method proposed is only compared to Empty Prompt and AutoPrompt baselines. While the authors' focus was on discrete prompt tuning, I would have liked to have seen the performance of continuous soft prompt tuning on these tasks, so as to demonstrate whether there is a tradeoff between readability (achieved via discrete prompts) and performance. \n\n\n\n", "questions_to_authors": "A. I am unsure if I have correctly understood Table 5, which displays accuracies for effective prompts that are higher than Table 2. If Table 5 shows effective prompts + GPT-3 continuations (resulting in a 100 token prompt), then does this imply that augmenting prompts with LLMs leads to improved performance over your FluentPrompt method? ", "paper_topic_and_main_contributions": "This paper explores methods of automatic prompt generation using gradient-based prompt tuning. It focuses on discrete rather than continuous prompt tuning, with a focus on human readability as well as improved accuracy. Its main contributions are: \n\n1. A prompt tuning method that generates human readable prompts\n2. An unsupervised method of generating prompts that does not require labelled data\n3. Analysis of the properties of effective automatically generated prompts. The authors observe that good prompts tend to be 1) related in topic to the task domain and 2) calibrate model output to adjust for label bias.\n \n", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "VIj1Fo9em2", "reviewer": "Reviewer_oTX2", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 2, "soundness": 5, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Due to the number of reviews we got for this work, we have to prioritize some common questions raised in all reviews and make a general response to all reviewers. However, we will be super happy to discuss and follow up here if you think any of your crucial concerns are not addressed. Thank you so much! We additionally thank your suggestions on presentation improvements.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "The strengths of this paper include the proposed FLUENTPROMPT method for generating effective and fluent prompts, the analysis of the factors that contribute to the effectiveness of prompts, and the proposed method for generating prompts using only unlabeled data. The paper also provides insights into the importance of topic relatedness and calibration of prompts for their success. If this paper were to be presented at the conference or accepted into Findings, the main benefits to the NLP community would be the availability of a new method for generating effective and fluent prompts, which could improve the performance of natural language processing tasks. The paper's analysis of the factors that contribute to the effectiveness of prompts could also inform future research in this area. Additionally, the proposed method for generating prompts using only unlabeled data could be particularly useful for low-resource languages or situations where labeled data is scarce.", "reasons_to_reject": "One potential weakness of this paper could be the limited scope of the analysis, as the authors only investigate the effectiveness of prompts in classification problems. Additionally, the proposed FLUENTPROMPT method may not be applicable to all natural language processing tasks, and the proposed method for generating prompts using only unlabeled data may not be as effective in certain situations. The main risk of having this paper presented at the conference or accepted into Findings could be that the proposed methods may not be as effective as expected in real-world scenarios, or that the analysis may not be generalizable to other natural language processing tasks beyond classification problems.", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper is about investigating the factors that make prompts effective in natural language processing tasks, particularly in classification problems. The paper proposes a human-readable prompt tuning method called FLUENTPROMPT, which incorporates a fluency constraint to find a distribution of effective and fluent prompts. The authors analyze the effectiveness of prompts and show that topic relatedness and calibration of the prompts are key to their success. Based on their findings, they also propose a method for generating prompts using only unlabeled data, which outperforms strong baselines by an average of 7.0% accuracy across three tasks. The main contributions of this paper are the proposed FLUENTPROMPT method for generating effective and fluent prompts, the analysis of the factors that contribute to the effectiveness of prompts, and the proposed method for generating prompts using only unlabeled data.\n", "excitement": 4, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "PcxBUduPYW", "reviewer": "Reviewer_xFvG", "summary": "", "questions": "1. Could the samples in Table 1 be cherry-picked? The authors should also provide examples of bad prompts for a better analysis.\n", "limitations": "", "rating": 4, "confidence": 3, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Due to the number of reviews we got for this work, we have to prioritize some common questions raised in all reviews and make a general response to all reviewers. However, we will be super happy to discuss and follow up here if you think any of your crucial concerns are not addressed. Thank you so much! We additionally thank your suggestions on presentation improvements.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. The paper is well-written and easy to follow.\n2. The analysis is throughout and well performed.\n3. The work addresses an important and interesting topic for the NLP community.", "reasons_to_reject": "1. The experimental setup regards only one model (GPT-2). More models should be tested to verify the proposed solution's performance and generalizability.\n\nMinor points:\n\n2. Although the authors mention in the \"Limitation\" section why they did not adopt larger models than GPT-2, they should also describe the hardware employed for the experimental phase.\n3. The authors should provide statistics of the benchmarked datasets.", "questions_to_authors": "1. Could the samples in Table 1 be cherry-picked? The authors should also provide examples of bad prompts for a better analysis.\n", "paper_topic_and_main_contributions": "The authors address the problem of prompt tuning, proposing FluentPrompt to generate human-readable prompts in natural language. They experiment with three datasets concerning sentiment aisles and topic classification, demonstrating that their proposed method outperforms the previous solution and generates more readable prompts. Furthermore, the authors perform multiple studies about prompts' calibration and topic-relatedness.", "excitement": 4, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {"Typos_Grammar_Style_and_Presentation_Improvements": "1. L028: \"langauge\" -> \"language\"\n2. L271-272: \"langauge\" -> \"language\"\n3. L369: \"What makes good prompts?\" -> should be capitalized as follow: \"What Makes Good Prompts?\"\n4. L395-396: \"the label words distribution.\" -> \"label word distribution.\"\n5. Caption of Table 5: \"subsantially\" -> \"substantially\"\n6. L489: \"FluentPrompt a method\" -> add a comma as follows: \"FluentPrompt, a method\"\n7. L495: \"and present\" -> add a comma as follows: \", and present\"\n8. L517: \"In Section 4.2\" -> add a comma as follows: \"In Section 4.2,\""}}, {"review_id": "R2tb7jPKWY", "reviewer": "Reviewer_mZkP", "summary": "", "questions": "1.How are the domain words for each task obtained?\n2.How many prompts are generated to obtain the analysis results shown in figure 2 and figure 3? How do you get various prompt for one task? By using different hyper-parameter?", "limitations": "", "rating": 3, "confidence": 3, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Due to the number of reviews we got for this work, we have to prioritize some common questions raised in all reviews and make a general response to all reviewers. However, we will be super happy to discuss and follow up here if you think any of your crucial concerns are not addressed. Thank you so much! We additionally thank your suggestions on presentation improvements.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1.This manuscript presents a new approach, FLUENTPROMPT, for tuning human-readable prompts in large language models. The method incorporates a fluency constraint and Langevin dynamics to generate effective and comprehensible prompts.\n2.The manuscript offers a thorough analysis and exploration of the factors that contribute to prompt effectiveness, such as topical relevance to the task domain and calibration of the prior probability of output labels.\n3.The manuscript proposes an unsupervised prompt tuning technique that optimizes prompts for better calibration and domain relevance using only unlabeled data. Empirical findings demonstrate that UNSUPERVISED FLUENT PROMPT outperforms robust zero-shot baselines in terms of accuracy.", "reasons_to_reject": "1.The manuscript lacks sufficient motivation for the substitution of SGD with Langevin dynamics. It is unclear why introducing additional Gaussian noise would aid in this task.\n2.The analysis of the common attributes of effective tuned prompts is somewhat flawed. Specifically, in quantitative analysis, GPT-3 is utilized to continue the prompt, and the word frequency is calculated based on the continuation. However, there is no assurance that the continuation follows the same distribution as the prompt. In reality, it follows the distribution of the GPT-3 pretraining corpus.\n3.The ablation study of the domain loss and entropy loss in the proposed UNSUPERVISED FLUENTPROMPT is insufficient. Readers have no insight into which one contributes the most to the performance.\n4.The authors only compare the performance of the proposed method with AutoPrompt. I believe that more baseline methods should be included to gain a better understanding of the effectiveness of the proposal. For instance, the following works also focus on discrete prompts: \na)RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning \nb)Black-box tuning for language-model-as-a-service \nc)Grips: Gradient-free, edit-based instruction search for prompting large language models.", "questions_to_authors": "1.How are the domain words for each task obtained?\n2.How many prompts are generated to obtain the analysis results shown in figure 2 and figure 3? How do you get various prompt for one task? By using different hyper-parameter?", "paper_topic_and_main_contributions": "This manuscript concentrates on the prompt design within the prompt learning paradigm, aiming to enhance the interpretability and effectiveness of the prompt. The authors replace the stochastic gradient descent utilized in previous prompting optimization methods with Langevin dynamics and introduce fluency regulation during prompt training. They conduct experiments utilizing three datasets, namely Amazon Polarity, SST-2, and AGNEWS. The authors also analyze the shared characteristics of an effective prompt and examine the factors that contribute to prompt efficacy.", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "N/A", "Typos_Grammar_Style_and_Presentation_Improvements": "N/A"}, "extra_text": {}}, {"review_id": "3YCSoNQ0cm", "reviewer": "Reviewer_ceBW", "summary": "", "questions": "* Can you include a short description of what Langevin Dynamics is? I had to Google it. You shouldn't assume your reader has even a vague knowledge of what that is. You don't need to go into details (I was perfectly able to follow the paper without knowing anything about Langevin, so you could honestly cut this connection entirely). But if you are going to bring it up, you should describe what it is.\n* Sorry if this is a stupid question--but I struggled to follow why you define your loss over the embeddings (probability of the embeddings) rather than just over the tokens themselves, as is more standard? There must be some reason for this (perhaps related to the langevin dynamics?) but it was not clear to me. I think providing some better intuition for why your loss is defined the way it is would be important.\n* I was confused initially in section 3.2 because I thought you were describing an unsupervised model (as this was emphasized in your intro). But actually 3.2 describes a supervised model, and its only at the end of the paper that you back off to the unsupervised one. Organizationally, the presentation could be improved to prevent this confusion. It would help to give more details about the data you are using and the loss in section 3.2, and make explicit that you will describe the unsupervised version later.\n* The unsupervised results are (perhaps too!) good. IIUC that method actually performs about the same as the supervised one. It makes me suspicious that the method is actually behaving like autoprompt, rather than like fluentprompt. Can we see some example prompts discovered by the unsupervised method, and verify that they are indeed more interpretable? Or is it finding pathological prompts that are just high-performing? \n", "limitations": "", "rating": 4, "confidence": 3, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Due to the number of reviews we got for this work, we have to prioritize some common questions raised in all reviews and make a general response to all reviewers. However, we will be super happy to discuss and follow up here if you think any of your crucial concerns are not addressed. Thank you so much! We additionally thank your suggestions on presentation improvements.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "* Interesting methodological contribution for automatic prompt tuning\n* Nice analysis on prompts in general which could generate some compelling follow up work\n* Lots of experiments, its a hefty paper\n", "reasons_to_reject": "* Nothing really. I found the description of the method hard to follow and some clarifications would be nice. See Questions section below.", "questions_to_authors": "* Can you include a short description of what Langevin Dynamics is? I had to Google it. You shouldn't assume your reader has even a vague knowledge of what that is. You don't need to go into details (I was perfectly able to follow the paper without knowing anything about Langevin, so you could honestly cut this connection entirely). But if you are going to bring it up, you should describe what it is.\n* Sorry if this is a stupid question--but I struggled to follow why you define your loss over the embeddings (probability of the embeddings) rather than just over the tokens themselves, as is more standard? There must be some reason for this (perhaps related to the langevin dynamics?) but it was not clear to me. I think providing some better intuition for why your loss is defined the way it is would be important.\n* I was confused initially in section 3.2 because I thought you were describing an unsupervised model (as this was emphasized in your intro). But actually 3.2 describes a supervised model, and its only at the end of the paper that you back off to the unsupervised one. Organizationally, the presentation could be improved to prevent this confusion. It would help to give more details about the data you are using and the loss in section 3.2, and make explicit that you will describe the unsupervised version later.\n* The unsupervised results are (perhaps too!) good. IIUC that method actually performs about the same as the supervised one. It makes me suspicious that the method is actually behaving like autoprompt, rather than like fluentprompt. Can we see some example prompts discovered by the unsupervised method, and verify that they are indeed more interpretable? Or is it finding pathological prompts that are just high-performing? \n", "paper_topic_and_main_contributions": "This paper describes a new method for automatic prompt-tuning which produces more interpretable prompts than existing methods. Specifically, the authors describe a gradient-based method for finding the best prompt for a task, but their method includes a fluency objective that constrains the model to prefer token sequences with low perplexity. The method thus produces prompts that acheive similar performance to those found using unconstrained automatic prompt tuning methods, but has the added benefit that the discovered prompts are (somewhat) interpretable to humans. The authors then use the method to perform some analysis on prompts, and which aspects of prompts produce good results. They reveal some interesting and intuitive insights. E.g., they show that topic keywords in the prompt help to guide the model, and that words are often included in the prompt which prime the model to have the right distribution over labels (e.g., negative sentiment words to counteract what might otherwise be a bias toward affirmative answers).\n\nThis is a nice paper overall. The method is nice and I really appreciate the analysis the authors perform. There is a lot of material and it holds together nicely. I have no major concerns, just a few questions about clarity.", "excitement": 4, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: The question paper rotates around the automatic prompt generation theme for NLP tasks. The authors delve deep into the mechanics of prompt tuning, emphasizing human readability and the efficiency of the prompts. Their primary contributions include introducing the FluentPrompt method, analyzing factors making prompts effective, and an unsupervised approach to generating prompts without relying on labeled data. While there is consensus on the paper's merits, some reviewers have raised concerns about certain study factors.

**Reason To Accept**
- Originality: The paper offers the NLP community fresh insights through several points, which detail the attributes of a good prompt. The characteristics emphasized—topic relevance and calibration for label bias help understand why prompt tuning is practical.
- Methodological Contributions: The proposed FluentPrompt technique is an innovative solution incorporating fluency constraints to generate effective prompts. Along with an unsupervised approach, this method consistently outperformed baselines across experiments.
- Analysis: Reviewers praised the exploration of factors contributing to prompt efficacy. The emphasis on topic relevance and calibration aids researchers in understanding the nuances of effective, prompt design.
- Potential for Follow-up Research: The analyses presented, especially concerning prompts' attributes, are anticipated to inspire subsequent studies in the domain.
**Reason To Reject**
- Limitations: Despite its potential, the FluentPrompt method is not perfect. The so-called 'human-readable' prompts sometimes need to be clarified, raising concerns about their true interpretability.
Scope of Analysis: Several reviewers felt the analysis need to be narrower in focus. The effectiveness of prompts is primarily studied for classification problems, which might only generalize to some NLP tasks.
- Comparison with Baselines: The paper mainly contrasts FluentPrompt with only a few baselines, such as Empty Prompt and AutoPrompt. Some reviewers wanted a broader comparison, including methods like continuous soft prompt tuning and other discrete prompt techniques.
- Exps Limitations: GPT-2 for experiments raises concerns about the findings' applicability across different models. The studys generalizability remains in question, as no other models were considered.
$FFinal Verdict:

While the paper introduces some innovative ideas and methods in the domain of prompt generation and tuning, certain reservations exist about its breadth and depth of analysis. Addressing these concerns might enhance its credibility and value to the NLP community.




**Overall Summary**
While the paper introduces some innovative ideas and methods in the domain of prompt generation and tuning, certain reservations exist about its breadth and depth of analysis. Addressing these concerns might enhance its credibility and value to the NLP community.
Decision: Accept-Findings