# %% 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", 1, '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("These spurious associations 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("""