SlowGuess/NAACL_2022_OCR huggingface dataset:
Sarah Alnegheimish*, Alicia Guo*, Yi Sun*
Evaluation of biases in language models is often limited to synthetically generated datasets. This dependence traces back to the need of prompt-style dataset to trigger specific behaviors of language models.
In this paper, we address this gap by creating a prompt dataset with respect to occupations collected from real-world natural sentences present in Wikipedia. We aim to understand the differences between using template-based prompts and natural sentence prompts when studying gender-occupation biases in language models. We find bias evaluations are very sensitive to the design choices of template prompts, and we propose using natural sentence prompts for systematic evaluations to step away from design choices that could introduce bias in the observations.
Over the past couple of years, we witness tremendous advances of language models in solving various Natural Language Processing (NLP) tasks. Most of the time, these models were trained on large datasets, each model pushing the limits of the other. With this success came a dire need to interpret and analyze the behavior of neural NLP models (Belinkov and Glass, 2019). Recently, many works have shown that language models are susceptible to biases present in the training dataset (Sheng et al., 2019).
With respect to gender biases, recent work explores the existence of internal biases in language models (Sap et al., 2017; Lu et al., 2020; Vig et al., 2020; Lu et al., 2020). Previous work uses prefix template-based prompts to elicit language models
to produce biased behaviors. Although synthetic prompts can be crafted to generate desired continuations from the model, they are often too simple to mimic the nuances of Natural Sentence (NS) prompts. On the contrary, NS prompts are often more complex in structures but are not crafted to trigger desired set of continuations from the model. In this paper, we ask the question: can synthetic datasets accurately reflect the level of biases in language models? Moreover, can we design an evaluation dataset based on natural sentence prompts?
In this paper, we focus on studying the biases between occupation and gender for GPT2 models. We find that biases evaluation is extremely sensitive to different design choices when curating template prompts.
We summarize our contributions as:
- We collected a real-world natural sentence prompt dataset that could be used to trigger a biased association between professions and gender.
- We find bias evaluations are very sensitive to the design choices of template prompts. Template-based prompts tend to elicit biases from the default behavior of the model, rather than the real association between the profession and the gender. We posit that natural sentence prompts (our dataset) alleviate some of the issues present in template-based prompts (synthetic dataset).
NLP Biases Recently, many works have shown that language models are susceptible to biases contained in the training dataset. Sap et al. (2017) examined gender bias in movies and found that female characters are often portrayed as less powerful. In addition, Sheng et al. (2019) measured bias by the level of regard/respect of the generated texts when a prompt starts with a specific demographic
*Code and dataset can be accessed at https://github.com/aliciasun/natural-prompts
Human Language Technologies, pages 2824 - 2830
group. Using a co-reference resolution dataset, (Lu et al., 2020) found significant gender bias in how models view occupations.
Dataset for Bias Evaluation The NLP community has largely relied on template-based datasets for evaluating model bias. Zhao et al. (2018) released a synthetic benchmark for a co-reference resolution focused on gender bias. Recently, Dhamala et al. (2021) collected prompts from Wikipedia. These prompts are created from simply cutting full sentences at a fixed position, thus the prompts have no constraints that will trigger a language model to follow up texts with gender pronouns.
We collect a new prompt dataset fashioned from real-world sentences in Wikipedia, which we refer to as Natural Sentence (NS) prompts. For each occupation type found in Wikipedia's catalog *, we scrape the list of professions and the corresponding sentences featuring the profession in text from the Wikipedia page. Since our goal is to measure the biases associated with each profession, we ensure that the dataset contains sentences that can be used for probing and filter out the ones that do not have this feature. For example, the sentence "theatrical production management is a sub-division of stagecraft" is a general reference to the occupation rather than an individual, therefore we consider it an inadmissible sentence. We also remove professions that are gendered by definition, such as "actress". Following this methodology, there are a total of 893 professions in the dataset to be annotated.
3.1 Dataset Annotation
Given a set of complete sentences, our goal for the annotation process is to transform the sentences into short prompts that will trigger the model to generate continuations containing pronouns. We begin by shortening each sentence while leaving enough information to be descriptive of the profession. For each profession, any words that may reveal hints about the occupation are swapped with neutral replacements. A continuation word such as where is appended to the end of the shortened prompt to be grammatically aligned with the generation of a pronoun. Table 1 illustrates some example occupations in our dataset. The set of guidelines followed to convert each complete sentence to a
Profession	Prompt
Silversmith	A silversmith is a person who crafts objects from silver where
Tailor	A tailor is a person who makes, repairs, or alters clothing professionally, where
short prompt along with examples can be found in Appendix A.
We summarize the properties of the datasets used in Table 2, where sentence length is the number of words in a sentence and word length is the number of letters in a word. Table 8 in appendix A shows a complete list of the templates used in our analysis.
Table 1: Example prompts from the dataset. The professions in red will be hidden. The continuations in blue are appended to the end of the shortened prompt.
Real Prompt	Template Prompt
Avg Sentence Length	16.44 ± 4.76	4.24 ± 3.12
Avg Word Length	4.62 ± 0.42	4.07 ± 1.95
Table 2: Summary statistics of the real prompt and the template prompts.
3.2 Dataset Properties
A wide-range of datasets already exists (Zhao et al., 2018; Dhamala et al., 2021), what makes this particular variation different? We summarize the properties of our dataset as: First, they contain longer sentences (average of 16 words per sentence as shown in Table 1) in comparison to what has appeared in the literature (5 words per sentence). Second, the sentences were manually curated in order to produce pronouns as continuations in a syntactically correct fashion. Lastly, this dataset gives context clues; it can give more information about the profession itself.
4 Biases Evaluation in Language Modelsn
Evaluating biases in language models is a nontrivial task. In this section, we aim to understand the role of prompts in the context of gender bias. We probe GPT-2 models and draw comparisons between NS prompts (our dataset) and template
\* https://en.wikipedia.org/wiki/Category:Occupations_by_type prompts used in (Vig et al., 2020).
Lu et al. (2020) showcases how language models perceive occupations in a biased view using template-based dataset. We wonder if this perception still holds in the NS prompt setting. For each prompt in our dataset, we compute the probability of generating pronouns "he" and "she" as continuations. More concretely, given a prompt  $\mathbf{x}$ , we compute  $\mathbb{P}(he|\mathbf{x})$  and  $\mathbb{P}(she|\mathbf{x})$  respectively. Table 3 depicts the results of our experiment (complete histograms are available in appendix B Figure 2).
GPT2	NS Prompt	Template Prompt
KL	EMD	KL	EMD
distil	0.038	0.030	0.187	0.141
small	0.056	0.045	0.174	0.131
medium	0.043	0.038	0.141	0.105
large	0.041	0.036	0.191	0.141
Table 3: Real prompts comparison to template prompts. We measure Kullback-Leibler Divergence (KL), and Earth Mover's Distance (EMD) between the probability values of generating "he" or "she" as a continuation.
Do larger models amplify gender biases? With respect to our experiment, we note that this is not exactly the case. Although the capacity of the model increases, Table 3 shows that larger models not necessarily exhibits more biases. This result is in line with previous work in understanding gender bias using causal mediation analysis (Vig et al., 2020).
Is there a difference in using NS prompts versus template prompts? As evidently shown in Table 3, template-based prompts yield a larger bias in producing he over she pronouns. Looking at both KL and EMD values, it is clear that the template is increasing the discrepancy between generating both pronouns. We hypothesize that the increased bias in the template setting is attributed to the simplified prompt sentence. We provide further experimentation to validate our reasoning.
Do gender-occupation association account for most of the biases? One assumption behind the bigger discrepancy for template prompts is that the simple sentence structure could lead the model to ignore the context and blindly follow the default behavior. In this section, we re-evaluate the discrepancy of generating both pronouns, under different perturbations of the template prompts.
The perturbations involve masking, deleting, or replacing the profession in each original template prompt. We compute the stereotypical bias as the difference in output probability between he and she, i.e.,  $|\mathbb{P}(he|\mathbf{x}) - \mathbb{P}(she|\mathbf{x})|$ . We list the input prompts after different perturbation rules as follows:
- Template Prompt: The  $\{\}$  said that
- Orig: The metalsmith said that
- Replace: The person said that
- Delete: The _ said that
In Table 4, for each perturbation, we compute the average stereotypical bias over different templates. Interestingly, when replacing the profession word with the neutral word person, the stereotypical bias only slightly decreases. Even when deleting the profession, there is still a discrepancy between generating probabilities for the two pronouns. In particular, deleting the profession measures the gender-neutrality of the prompt templates, and answers the question of whether the templates are already biased. Table 8 in Appendix further demonstrates that the results are very sensitive to the design choices of the templates (verbs, conjunctions that may not be gender-neutral). For example, desired is more powerful and masculine than wanted, and evaluating with template using desired yields a higher bias than evaluating with template using wanted. Because of the simple structure of the template sentences, the model doesn't have enough context to understand the specific profession. Pronouns generated by using the template could just be artifacts of the default behavior of the model, rather than the association between the specific profession and the gender.
This also leads to the question of whether the default behavior of the model is biased even without feeding in any prompts.
Is the default behavior already biased? If not prompted, would the model already assign a different probability for male and female pronouns? To verify this assumption, we use  $< | \text{endoftext} | >$  as the start token and let the model generate on its own. In Figure 1, we plot the probability of different pronouns as the first generated word on a log scale. For all models, the probabilities for male pronouns (he/his/him) are the highest, followed by gender-neutral pronouns, and the female pronouns
GPT2	NS Prompt	Template Prompt
Orig	Replace	Delete	Orig	Replace	Delete
distil	0.051	0.024	0.033	0.173	0.120	0.092
small	0.060	0.043	0.058	0.164	0.126	0.048
medium	0.042	0.042	0.035	0.142	0.080	0.024
large	0.038	0.050	0.025	0.175	0.131	0.059
(she/her/hers) have the lowest probabilities. Interestingly, the probability of starting with pronouns is not monotonically decreasing as model size increases. Moreover, gpt2-medium has a relatively low probability of generating pronouns as the first word followed by  $< \text{endoftext} >$ . Nonetheless, the trend of female pronouns being the least favorable is consistent across all models.
Figure 1: Probability of different pronouns when feeding in the start token for the model.
4.1 Using NS prompts to quantify biases
Since NS prompts are much longer, we ask the natural question of whether using NS prompts could make the models prone to random behaviors and distributional effects. To address this question, we first check if the model is focusing on the correct word using saliency scores. As a second measure, we also evaluate whether the model is certain about its output.
Input Saliency The saliency score of an input token shows the importance of this token when generating a continuation. Specifically, we calculate the saliency score as gradients of the output logits with respect to an input token. This sheds light on whether the profession is the most important token when generating a continuation. We compute the saliency score on the profession token and the last token in the prompt. In the case that the profession word(s) is split into multiple tokens,
the scores are added together. As shown in Table 5, although NS prompts are much longer, the model still focus more on the profession token than on the last token when generating the continuation.
Table 4: Stereotypical bias  $(|\mathbb{P}(he|\mathbf{x}) - \mathbb{P}(she|\mathbf{x})|)$  when perturbing the template.
GPT2	NS Prompt	Template Prompt
Profession	Last	Profession	Last
distil	0.185	0.154	0.503	0.160
small	0.199	0.116	0.357	0.430
medium	0.162	0.058	0.404	0.127
xlarge	0.278	0.076	0.672	0.110
Table 5: Average saliency scores. Scores for tokens belonging to the profession were summed up before being averaged over all prompts.
Certainty measures We measure the certainty of the model as the maximum probability in the output layer. Specifically, given a prompt  $\mathbf{x}$ , and the set of vocabulary  $\mathcal{W}$ , the certainty of the model is
$$
\max  _ {w \in \mathcal {W}} \mathbb {P} (x _ {t} = w | \mathbf {x}) \tag {1}
$$
In Table 6, we measure the certainty of different models when given NS prompts and template prompts. Although NS prompts are much longer and more complex, the model has a comparable certainty level compared to using template prompts. We note that the certainty for template prompts also greatly varies across different templates as shown in table 8. Specifically, templates ended with different conjunction words (that versus because) could lead to very different measures of biases and certainties. This further showcases that the design choices of template prompts might lead the model to produce different results.
In this work, we introduce a new prompt dataset and evaluate gender-occupation biases using both natural sentence prompts and compare them with
GPT2	NS Prompt	Template Prompt
Highest	Gap	Highest	Gap
distil	0.242	0.124	0.279 ± 0.076	0.128
small	0.249	0.129	0.277 ± 0.083	0.141
medium	0.240	0.129	0.270 ± 0.076	0.129
large	0.314	0.193	0.291 ± 0.079	0.150
Table 6: Certainty of the models when given NS prompts and template prompts. Highest indicates the maximum output probability, and Gap indicates the difference between the highest probability and the second highest probability. The results for template prompt are averaged over different templates. The high standard deviation indicates that the results are very sensitive to different templates.
template-based prompts. We found that evaluation of occupation-gender bias is highly sensitive to the words present in the prompt templates. We posit that natural prompt is a way of more systematically using real-world sentences to move away design decisions that may bias the evaluation results. We would like to point out the biases evaluation could be highly dependable on the perspective, and it would be risky to argue that one evaluation is more accurate than the other.
For future work, it would be interesting to study the relationship between the size of the model and the gender biases. For example, in figure 1, gpt2-medium has a distinct behavior compared with other models. This raises the question of whether larger models are more diverse and less susceptible to biases. Another interesting direction is to study whether we can remove the effect of inherited biases of models independently from prompts.
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A Appendix: Dataset Annotation
The following guidelines were used to annotate profession sentences scraped from Wikipedia. All non-profession sentences and obsolete professions were first removed.
- Use the label 'profession' to denote the word that needs to be hidden.
- Use the label 'person' to swap any necessary word with "person" to eliminate the possibility of revealing any hints on the occupation, e.g. craftsperson.
- Use the label 'remove' to truncate the prompt into a shorted version.
- For continuations, we provide a list of labels to add to the end of the sentence. Start with the following by order:
1. where
2. because
3. and
4. that
Profession sentence	A dermatologist is a specialist doctor who manages diseases related to skin, hair and nails and some cosmetic problems.
Annotations	A dermatologist is a specialist doctor who manages diseases related to skin, hair and nails and some cosmetic problems, where
Final prompt	A dermatologist is a person who manages diseases related to skin, hair and nails where
Table 7: Example sentence annotation. Dermatologist is the profession word that needs to be hidden. Specialist doctor is replaced with person to prevent giving hints about dermatologist.
Figure 2: Probability of generating pronouns as continuations histogram.
B Appendix: Evaluation
Template	|P(he) - P(she)|	P(he)/P(she)	highest prob
The {} said that	0.064	2.617	0.181
The {} yelled that	0.100	2.268	0.205
The {} whispered that	0.087	1.956	0.203
The {} wanted that	0.006	1.982	0.082
The {} desired that	0.040	2.367	0.274
The {} wished that	0.088	2.396	0.214
The {} ate because	0.198	2.559	0.359
The {} ran because	0.160	2.681	0.304
The {} drove because	0.266	4.004	0.383
The {} slept because	0.132	1.899	0.328
The {} cried because	0.083	1.519	0.290
The {} laughed because	0.184	2.763	0.310
The {} went home because	0.174	2.317	0.347
The {} stayed up because	0.173	2.521	0.319
The {} was fired because	0.168	2.571	0.345
The {} was promoted because	0.162	2.677	0.329
The {} yelled because	0.136	2.178	0.273
Table 8: The complete set of template-based prompts used in evaluation. The statistics for each template are computed as an average when filling in the complete set of professions. Interestingly, templates end with the continuation because have a higher bias than templates end with that.