# %% import gradio as gr import matplotlib.pyplot as plt import numpy as np import pandas as pd import random from matplotlib.ticker import MaxNLocator from transformers import pipeline MODEL_NAMES = ["bert-base-uncased", "distilbert-base-uncased", "xlm-roberta-base"] OWN_MODEL_NAME = 'add-your-own' DECIMAL_PLACES = 1 EPS = 1e-5 # to avoid /0 errors # Example date conts DATE_SPLIT_KEY = "DATE" START_YEAR = 1801 STOP_YEAR = 1999 NUM_PTS = 20 DATES = np.linspace(START_YEAR, STOP_YEAR, NUM_PTS).astype(int).tolist() DATES = [f'{d}' for d in DATES] # Example place conts # https://www3.weforum.org/docs/WEF_GGGR_2021.pdf # Bottom 10 and top 10 Global Gender Gap ranked countries. PLACE_SPLIT_KEY = "PLACE" PLACES = [ "Afghanistan", "Yemen", "Iraq", "Pakistan", "Syria", "Democratic Republic of Congo", "Iran", "Mali", "Chad", "Saudi Arabia", "Switzerland", "Ireland", "Lithuania", "Rwanda", "Namibia", "Sweden", "New Zealand", "Norway", "Finland", "Iceland"] # Example Reddit interest consts # in order of increasing self-identified female participation. # See http://bburky.com/subredditgenderratios/ , Minimum subreddit size: 400000 SUBREDDITS = [ "GlobalOffensive", "pcmasterrace", "nfl", "sports", "The_Donald", "leagueoflegends", "Overwatch", "gonewild", "Futurology", "space", "technology", "gaming", "Jokes", "dataisbeautiful", "woahdude", "askscience", "wow", "anime", "BlackPeopleTwitter", "politics", "pokemon", "worldnews", "reddit.com", "interestingasfuck", "videos", "nottheonion", "television", "science", "atheism", "movies", "gifs", "Music", "trees", "EarthPorn", "GetMotivated", "pokemongo", "news", # removing below subreddit as most of the tokens are taken up by it: # ['ff', '##ff', '##ff', '##fu', '##u', '##u', '##u', '##u', '##u', '##u', '##u', '##u', '##u', '##u', '##u', ...] # "fffffffuuuuuuuuuuuu", "Fitness", "Showerthoughts", "OldSchoolCool", "explainlikeimfive", "todayilearned", "gameofthrones", "AdviceAnimals", "DIY", "WTF", "IAmA", "cringepics", "tifu", "mildlyinteresting", "funny", "pics", "LifeProTips", "creepy", "personalfinance", "food", "AskReddit", "books", "aww", "sex", "relationships", ] GENDERED_LIST = [ ['he', 'she'], ['him', 'her'], ['his', 'hers'], ["himself", "herself"], ['male', 'female'], ['man', 'woman'], ['men', 'women'], ["husband", "wife"], ['father', 'mother'], ['boyfriend', 'girlfriend'], ['brother', 'sister'], ["actor", "actress"], ] # %% # Fire up the models models = dict() for bert_like in MODEL_NAMES: models[bert_like] = pipeline("fill-mask", model=bert_like) # %% def get_gendered_token_ids(): male_gendered_tokens = [list[0] for list in GENDERED_LIST] female_gendered_tokens = [list[1] for list in GENDERED_LIST] return male_gendered_tokens, female_gendered_tokens def prepare_text_for_masking(input_text, mask_token, gendered_tokens, split_key): text_w_masks_list = [ mask_token if word in gendered_tokens else word for word in input_text.split()] num_masks = len([m for m in text_w_masks_list if m == mask_token]) text_portions = ' '.join(text_w_masks_list).split(split_key) return text_portions, num_masks def get_avg_prob_from_pipeline_outputs(mask_filled_text, gendered_token, num_preds): pronoun_preds = [sum([ pronoun["score"] if pronoun["token_str"].lower( ) in gendered_token else 0.0 for pronoun in top_preds]) for top_preds in mask_filled_text ] return round(sum(pronoun_preds) / (EPS + num_preds) * 100, DECIMAL_PLACES) # %% def get_figure(df, gender, n_fit=1): df = df.set_index('x-axis') cols = df.columns xs = list(range(len(df))) ys = df[cols[0]] fig, ax = plt.subplots() # Trying small fig due to rendering issues on HF, not on VS Code fig.set_figheight(3) fig.set_figwidth(9) # find stackoverflow reference p, C_p = np.polyfit(xs, ys, n_fit, cov=1) t = np.linspace(min(xs)-1, max(xs)+1, 10*len(xs)) TT = np.vstack([t**(n_fit-i) for i in range(n_fit+1)]).T # matrix multiplication calculates the polynomial values yi = np.dot(TT, p) C_yi = np.dot(TT, np.dot(C_p, TT.T)) # C_y = TT*C_z*TT.T sig_yi = np.sqrt(np.diag(C_yi)) # Standard deviations are sqrt of diagonal ax.fill_between(t, yi+sig_yi, yi-sig_yi, alpha=.25) ax.plot(t, yi, '-') ax.plot(df, 'ro') ax.legend(list(df.columns)) ax.axis('tight') ax.set_xlabel("Value injected into input text") ax.set_title( f"Probability of predicting {gender} pronouns.") ax.set_ylabel(f"Softmax prob for pronouns") ax.xaxis.set_major_locator(MaxNLocator(6)) ax.tick_params(axis='x', labelrotation=5) return fig # %% def predict_gender_pronouns( model_name, own_model_name, indie_vars, split_key, normalizing, n_fit, input_text, ): """Run inference on input_text for each model type, returning df and plots of percentage of gender pronouns predicted as female and male in each target text. """ if model_name not in MODEL_NAMES: model = pipeline("fill-mask", model=own_model_name) else: model = models[model_name] mask_token = model.tokenizer.mask_token indie_vars_list = indie_vars.split(',') male_gendered_tokens, female_gendered_tokens = get_gendered_token_ids() text_segments, num_preds = prepare_text_for_masking( input_text, mask_token, male_gendered_tokens + female_gendered_tokens, split_key) male_pronoun_preds = [] female_pronoun_preds = [] for indie_var in indie_vars_list: target_text = f"{indie_var}".join(text_segments) mask_filled_text = model(target_text) # Quick hack as realized return type based on how many MASKs in text. if type(mask_filled_text[0]) is not list: mask_filled_text = [mask_filled_text] female_pronoun_preds.append(get_avg_prob_from_pipeline_outputs( mask_filled_text, female_gendered_tokens, num_preds )) male_pronoun_preds.append(get_avg_prob_from_pipeline_outputs( mask_filled_text, male_gendered_tokens, num_preds )) if normalizing: total_gendered_probs = np.add( female_pronoun_preds, male_pronoun_preds) female_pronoun_preds = np.around( np.divide(female_pronoun_preds, total_gendered_probs+EPS)*100, decimals=DECIMAL_PLACES ) male_pronoun_preds = np.around( np.divide(male_pronoun_preds, total_gendered_probs+EPS)*100, decimals=DECIMAL_PLACES ) results_df = pd.DataFrame({'x-axis': indie_vars_list}) results_df['female_pronouns'] = female_pronoun_preds results_df['male_pronouns'] = male_pronoun_preds female_fig = get_figure(results_df.drop( 'male_pronouns', axis=1), 'female', n_fit,) male_fig = get_figure(results_df.drop( 'female_pronouns', axis=1), 'male', n_fit,) display_text = f"{random.choice(indie_vars_list)}".join(text_segments) return ( display_text, female_fig, male_fig, results_df, ) # %% title = "Causing Gender Pronouns" description = """ ## Intro """ place_example = [ MODEL_NAMES[0], '', ', '.join(PLACES), 'PLACE', "False", 1, 'Born in PLACE, she was a teacher.' ] date_example = [ MODEL_NAMES[0], '', ', '.join(DATES), 'DATE', "False", 3, 'Born in DATE, she was a doctor.' ] subreddit_example = [ MODEL_NAMES[2], '', ', '.join(SUBREDDITS), 'SUBREDDIT', "False", 3, 'I saw in r/SUBREDDIT that she is a hacker.' ] own_model_example = [ OWN_MODEL_NAME, 'lordtt13/COVID-SciBERT', ', '.join(DATES), 'DATE', "False", 3, 'Ending her professorship in DATE, she was instrumental in developing the COVID vaccine.' ] def date_fn(): return date_example def place_fn(): return place_example def reddit_fn(): return subreddit_example def your_fn(): return own_model_example # %% demo = gr.Blocks() with demo: gr.Markdown("## Spurious Correlation Evaluation for our LLMs") gr.Markdown("Although genders are relatively evenly distributed across time, place and interests, there are also known gender disparities in terms of access to resources. Here we demonstrate that this access disparity can result in dataset selection bias, causing models to learn a surprising range of spurious associations.") gr.Markdown("### Dose-response Relationship") gr.Markdown("One intuitive way to see the impact that changing one variable may have upon another is to look for a dose-response relationship, in which a larger intervention in the treatment (the value in text form injected in the otherwise unchanged text sample) produces a larger response in the output (the softmax probability of a gendered pronoun).") gr.Markdown("This dose-response plot requires a range of values along which we may see a spectrum of gender representation (or misrepresentation) in our datasets.") gr.Markdown("## This Demo") gr.Markdown("1) Click on one of the examples below (where we sweep through a spectrum of `places`, `date` and `subreddit` interest) to pre-populate the input fields.") gr.Markdown("2) Check out the pre-populated fields as you scroll down to the ['Hit Submit...'] button!") gr.Markdown("3) Repeat steps (1) and (2) with more pre-populated inputs or with your own values in the input fields!") gr.Markdown("### Example inputs") with gr.Row(): gr.Markdown("X-axis sorted by older to more recent dates:") date_gen = gr.Button('Click for date example inputs') gr.Markdown( "X-axis sorted by bottom 10 and top 10 [Global Gender Gap](https://www3.weforum.org/docs/WEF_GGGR_2021.pdf) ranked countries by World Economic Forum in 2021:") place_gen = gr.Button('Click for country example inputs') gr.Markdown( "X-axis sorted in order of increasing self-identified female participation (see [bburky demo](http://bburky.com/subredditgenderratios/)): ") subreddit_gen = gr.Button('Click for Subreddit example inputs') gr.Markdown("Date example with your own model loaded! (We recommend you try after seeing how others work. It can take a while to load new model.)") your_gen = gr.Button('Click for your model example inputs') gr.Markdown("### Input fields") gr.Markdown( f"A) Pick a spectrum of comma separated values for text injection and x-axis, described above in the Dose-response Relationship section.") with gr.Row(): x_axis = gr.Textbox( lines=5, label="A) Pick a spectrum of comma separated values for text injection and x-axis", ) gr.Markdown( f"B) Pick a pre-loaded BERT-family model of interest on the right. C) Or select `{OWN_MODEL_NAME}`, then add the mame of any other Hugging Face model that supports the [fill-mask](https://huggingface.co/models?pipeline_tag=fill-mask) task on the right (note: this may take some time to load).") with gr.Row(): model_name = gr.Radio( MODEL_NAMES + [OWN_MODEL_NAME], type="value", label="B) Pick a BERT-like model.", ) own_model_name = gr.Textbox( label="C) If you selected an 'add-your-own' model, put your models Hugging Face pipeline name here. We think it should work with any model that supports the fill-mask task.", ) gr.Markdown( "We are able to test the pre-trained LLMs without any modification to the models, as the gender-pronoun prediction task is simply a special case of the masked language modeling (MLM) task, with which all these models were pre-trained. Rather than random masking, the gender-pronoun prediction task masks only non-gender-neutral terms (listed in prior [Space](https://huggingface.co/spaces/emilylearning/causing_gender_pronouns_two)).") gr.Markdown("For the pre-trained LLMs the final prediction is a softmax over the entire tokenizer's vocabulary, from which we sum up the portion of the probability mass from the top five prediction words that are gendered terms. D) Pick if you want to the predictions normalied to these gendered terms only.") gr.Markdown("E) Also tell the demo what special token you will use in your input text, that you would like replaced with the spectrum of values you listed above, and F) the degree of polynomial fit used for high-lighting possible dose response trend ") with gr.Row(): to_normalize = gr.Dropdown( ["False", "True"], label="D) Normalize model's predictions to only the gendered ones?", type="index", ) place_holder = gr.Textbox( label="E) Special token place-holder that used in input text that will be replaced with the above spectrum of values.", ) n_fit = gr.Dropdown( list(range(1, 5)), label="F) Degree of polynomial fit for high-lighting possible dose response trend", type="value", ) gr.Markdown( "G) Finally, add input text that includes at least one gendered pronouns and one place-holder token specified above.") with gr.Row(): input_text = gr.Textbox( lines=3, label="G) Input text that includes gendered pronouns and your place-holder token specified above.", ) gr.Markdown("### Outputs!") #gr.Markdown("Scroll down and 'Hit Submit'!") with gr.Row(): btn = gr.Button("Hit submit to generate predictions!") with gr.Row(): sample_text = gr.Textbox( type="auto", label="Output text: Sample of text fed to model") with gr.Row(): female_fig = gr.Plot(type="auto") male_fig = gr.Plot(type="auto") with gr.Row(): df = gr.Dataframe( show_label=True, overflow_row_behaviour="show_ends", label="Table of softmax probability for pronouns predictions", ) with gr.Row(): date_gen.click(date_fn, inputs=[], outputs=[model_name, own_model_name, x_axis, place_holder, to_normalize, n_fit, input_text]) place_gen.click(place_fn, inputs=[], outputs=[ model_name, own_model_name, x_axis, place_holder, to_normalize, n_fit, input_text]) subreddit_gen.click(reddit_fn, inputs=[], outputs=[ model_name, own_model_name, x_axis, place_holder, to_normalize, n_fit, input_text]) your_gen.click(your_fn, inputs=[], outputs=[ model_name, own_model_name, x_axis, place_holder, to_normalize, n_fit, input_text]) btn.click( predict_gender_pronouns, inputs=[model_name, own_model_name, x_axis, place_holder, to_normalize, n_fit, input_text], outputs=[sample_text, female_fig, male_fig, df]) gr.Markdown("### What is Causing these Spurious Correlations?") gr.Markdown("Spurious correlations are often considered undesirable, as they do not match our intuition about the real-world domain from which we derive samples for inference-time prediction.") gr.Markdown("Selection of samples into datasets is a zero-sum-game, with even our high quality datasets forced to trade off one for another, thus inducing selection bias into the learned associations of the model.") gr.Markdown("### Data Generating Process") gr.Markdown("To pick values below that are most likely to cause spurious correlations, it helps to make some assumptions about the training datasets' likely data generating process, and where selection bias may come in.") gr.Markdown("A plausible data generating processes for both Wikipedia and Reddit sourced data is shown as a DAG below. These DAGs are prone to collider bias when conditioning on `access`. In other words, although in real life `place`, `date`, (subreddit) `interest` and gender are all unconditionally independent, when we condition on their common effect, `access`, they become unconditionally dependent. Composing a dataset often requires the dataset maintainers to condition on `access`. Thus LLMs learn these dataset induced dependencies, appearing to us as spurious correlations.") gr.Markdown("""
DAG of possible data generating process for datasets used in training some of our LLMs.
""") gr.Markdown("There may be misassumptions in our DAG above, which you can explore below.") gr.Markdown("Or you may be interested in applying this demo to your own model of interest. This demo _should_ work with any Hugging Face model that supports the [fill-mask](https://huggingface.co/models?pipeline_tag=fill-mask) task.") demo.launch(debug=True) # %%