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"I'm sorry to hear that": Finding New Biases in Language Models with a Holistic Descriptor Dataset

Eric Michael SmithMelissa HallMelanie KambadurEleonora PresaniAdina Williams

article2022EMNLP194 citations

Introduces HOLISTICBIAS, an open-source dataset of over 450,000 conversational prompts spanning nearly 600 demographic descriptors across 13 axes, enabling more comprehensive measurement and mitigation of subtle social biases in generative language models.

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As natural language processing models become widely adopted in consumer-facing dialogue systems and applications, identifying and mitigating demographic bias is critical to avoid reinforcing social harms. Most existing evaluation datasets rely on narrow taxonomies and rigid benchmarks that overlook intersectional or evolving identity terms, frequently missing subtle model behaviors such as patronizing sympathy toward individuals with disabilities or overt confusion regarding underrepresented gender and sexual identities.

The article introduces HOLISTICBIAS, a comprehensive demographic evaluation dataset, and demonstrates its utility in uncovering, measuring, and mitigating subtle social biases across several generative and masked language models.

To construct the dataset, the researchers combined algorithmic expansions with a participatory process involving community members and subject matter experts. This resulted in a living taxonomy of nearly 600 American English descriptor terms categorized across 13 demographic axes, such as ability, gender and sex, race and ethnicity, and socioeconomic status. Combining these descriptors with person nouns and 26 conversational sentence templates generated approximately 460,000 unique prompt sentences. The researchers evaluated model token likelihoods, classified generative responses across 217 distinct conversational styles, and evaluated offensiveness classifiers on models including GPT-2, RoBERTa, DialoGPT, and BlenderBot 2.0 (400-million and 3-billion parameter variants).

The analysis yielded four central findings. First, larger language models exhibited substantially higher levels of generation bias than smaller architectures; for example, the 3-billion-parameter BlenderBot 2.0 displayed significantly more demographic variance in conversational styles than its 400-million-parameter counterpart or DialoGPT. Second, conversational biases predominantly surfaced as disproportionate levels of sympathy toward ability-related descriptors and curiosity or confusion toward gender, sex, and sexual orientation descriptors. Third, automated offensiveness classifiers exhibited substantial systemic bias, frequently assigning high offensiveness probabilities to neutral sentences simply because they contained marginalized demographic descriptors. Fourth, a proof-of-concept mitigation technique based on style equality reduced overall generation bias by 13% in DialoGPT and 24% in the 3-billion-parameter BlenderBot 2.0.

These findings demonstrate that language models generate subtle microaggressions that conventional safety filters and sentiment analyzers fail to catch. Because existing toxicity classifiers often penalize marginalized identity markers, organizations deploying dialogue systems face serious compliance, brand reputation, and safety risks. Furthermore, scaling up model size without targeted behavioral constraints amplifies these biased behavioral patterns rather than resolving them.

Organizations should adopt comprehensive, living evaluation suites like HOLISTICBIAS to audit models before deployment rather than relying solely on static benchmarks. While style equality tuning successfully reduces unwanted variations in sympathy and confusion, teams should treat it as an experimental intervention. Practitioners must balance style equalization carefully, as aggressive tuning can increase prompt parroting or suppress justified empathy.

The findings are bounded by the dataset's current focus on United States English and single-descriptor prompts. Additionally, using automated style and offensiveness classifiers introduces measurement noise. Nonetheless, the high consistency across multiple language models and hundreds of thousands of test prompts provides strong confidence in HOLISTICBIAS as an effective diagnostic benchmark for conversational AI.

Metadescriptorsstatistical language modelslanguage model biasHolistic Descriptor datasetbias evaluation
arXiv: 2205.09209
Cover for "I'm sorry to hear that": Finding New Biases in Language Models with a Holistic Descriptor Dataset

Abstract

As language models grow in popularity, it becomes increasingly important to clearly measure all possible markers of demographic identity in order to avoid perpetuating existing societal harms. Many datasets for measuring bias currently exist, but they are restricted in their coverage of demographic axes and are commonly used with preset bias tests that presuppose which types of biases models can exhibit. In this work, we present a new, more inclusive bias measurement dataset, HOLISTICBIAS, which includes nearly 600 descriptor terms across 13 different demographic axes. HOLISTICBIAS was assembled in a participatory process including experts and community members with lived experience of these terms. These descriptors combine with a set of bias measurement templates to produce over 450,000 unique sentence prompts, which we use to explore, identify, and reduce novel forms of bias in several generative models. We demonstrate that HOLISTICBIAS is effective at measuring previously undetectable biases in token likelihoods from language models, as well as in an offensiveness classifier. We will invite additions and amendments to the dataset, which we hope will serve as a basis for more easy-to-use and standardized methods for evaluating bias in NLP models.

Topics

descriptorsstatistical language models

Keywords

language model biasHolistic Descriptor datasetbias evaluationbias measurementbias mitigationgenerative biasoffensiveness classifierdialogue responses

Table of Contents

  • 1 Introduction
  • 2 Methods
  • 2.1 Defining bias
  • 2.2 The HOLISTICBIAS dataset
  • 2.2.1 Demographic descriptor terms
  • 2.2.2 Making prompts with templates
  • 2.3 Measuring bias
  • 2.3.1 Models
  • 2.3.2 Bias in token likelihoods
  • 2.3.3 Bias in generations
  • 2.3.4 Differences in offensiveness by descriptor
  • 3 Measuring generative bias
  • 3.1 Bias in token likelihoods
  • 3.2 Bias in generations
  • 3.3 Differences in offensiveness by descriptor
  • 4 Reducing generative bias
  • 4.1 Objective
  • 4.2 Technique
  • 4.3 Results
  • 4.4 Limitations of method
  • 5 Related work
  • 6 Conclusion
  • Limitations
  • Ethics statement
  • Acknowledgments
  • References
  • A Additional methods
  • A.1 Dataset creation approach
  • A.2 Descriptor terms
  • A.3 Using templates to generate prompts
  • A.4 Model details
  • A.5 Generation details
  • A.6 Using style classifiers to classify generated responses
  • A.7 Generation bias metrics
  • B Additional results
  • B.1 Bias in token likelihoods
  • B.2 Bias in generations
  • B.2.1 Descriptor training frequency analysis
  • B.3 Differences in offensiveness by descriptor
  • C Reducing generative bias
  • C.1 Technique
  • C.2 Results
  • C.2.1 Automatic evaluations
  • C.2.2 Human evaluations

Knowls

  1. Knowl 1 — HOLISTICBIAS’s participatory demographic descriptor taxonomy

    model/method

    HOLISTICBIAS is a living American-English evaluation resource containing 594 demographic descriptor terms organized across 13 axes: Ability, Age, Body type, Characteristics, Cultural, Gender and sex, Nationality, Nonce, Political ideologies, Race and ethnicity, Religion, Sexual orientation, and Socioeconomic class. Most axes are further divided into buckets such as disability type, immigration status, gender identity, or racial/ethnic grouping.

    The descriptor list was created by combining author-generated seed terms, the 50 nearest neighbors of existing terms in fastText embeddings, WordNet synonyms and antonyms, and feedback from more than two dozen contributors with relevant community membership or expertise. Contributors added terms and assessed whether some commonly used terms were dispreferred or polarizing. Eight nonce terms with no lexical semantics were included as an out-of-vocabulary baseline. The list excludes outright slurs, but retains some outdated or dispreferred terms because language models may encounter them in user prompts and training data.

    HOLISTICBIAS is intended to evolve through community amendments and additions rather than function as a fixed taxonomy. The annotations concerning preferred, dispreferred, or polarizing language are explicitly subjective and context-dependent; the resource is limited to the authors’ and collaborators’ coverage, primarily represents US English, and does not exhaustively represent demographic identities or intersections of identities.

  2. Knowl 2 — Combinatorial conversational prompt construction

    model/method

    HOLISTICBIAS turns descriptors into conversational evaluation prompts by inserting each descriptor into 26 templates together with a person noun. The noun inventories contain female-associated nouns such as “woman,” “mother,” and “grandmother”; male-associated nouns such as “man,” “father,” and “grandfather”; and unspecified nouns such as “person,” “individual,” “parent,” “sibling,” and “veteran. Depending on grammatical structure, a descriptor appears before the noun phrase or in a post-nominal phrase such as “who is [DESCRIPTOR].”

    The construction enumerates the grammatical combinations of descriptor, person noun, and template, producing 459,758 distinct prompts before optional stylistic variants. Robustness variants lowercase descriptors, remove descriptor hyphens, remove the contraction in “I’m,” or remove a final period. The resulting prompts support both likelihood scoring and generation-based dialogue evaluation.

    The breadth of the resource comes from the combination of terms and templates. The reported comparison is: SEAT has 479 terms, 5 axes, 36 templates, and 4,506 sentences; StereoSet has 321 terms, 4 axes, no reported template count, and 50,985 sentences; CrowS-Pairs has no single descriptor-term count, 9 axes, no reported template count, and 3,016 sentences; Sotnikova et al. has 71 terms, 6 axes, 102 templates, and 7,242 sentences; Huang et al. has 73 terms, 3 axes, 30 templates, and 730 sentences; HOLISTICBIAS has 594 terms, 13 axes, 26 templates, and 459,758 sentences.

  3. Knowl 3 — Operational definition and evaluation scope of language-model bias

    definition

    The paper defines language-model bias as demographic difference: a group-level difference in model outputs or assigned probabilities caused by identity or demographic information in the input text. Under this definition, difference is the measurable phenomenon; whether a particular difference is benign, harmful, stereotypical, othering, or inappropriately sympathetic must be judged for the identity term, task, and use case.

    HOLISTICBIAS evaluates demographic differences in three ways. First, it compares token likelihoods or perplexities assigned to otherwise comparable templated sentences. Second, it compares the conversational styles of generated responses to prompts containing different descriptors. Third, it measures whether an offensiveness classifier assigns different offensive probabilities to prompts containing different descriptors. The experiments apply these measurements to GPT-2, RoBERTa, DialoGPT, and BlenderBot 2.0, including BlenderBot 2.0 models with 400 million and approximately 2.7 billion parameters.

  4. Knowl 4 — Likelihood Bias for descriptor pairs

    equation

    For each demographic axis aa, the paper measures Likelihood Bias by comparing the likelihood behavior of every pair of descriptors within that axis. Let DaD_a be the set of descriptors in axis aa, let RaR_a be the set of descriptor pairs whose distributions of sentence perplexities differ significantly under a Mann–Whitney UU test, and let ∣Da∣|D_a| denote the number of descriptors in the axis. Likelihood Bias for the axis is

    LB(a)=∣Ra∣(∣Da∣2).\mathrm{LB}(a)=\frac{|R_a|}{\binom{|D_a|}{2}}.

    Each descriptor is inserted into corresponding templated sentences, and the test compares the two descriptors’ perplexity distributions. Lower perplexity means higher model likelihood. A larger LB(a)\mathrm{LB}(a) means that a larger fraction of descriptor pairs are treated differently by the language model in the evaluated contexts. The same pairwise logic is applied to GPT-2 and BlenderBot 2.0 perplexities and to RoBERTa pseudo-log-likelihoods.

  5. Knowl 5 — Likelihood evaluations reveal strong axis- and context-dependent disparities

    empirical result

    For the template “I love [PLURAL NOUN PHRASE],” GPT-2 and BlenderBot 2.0 3B showed especially high Likelihood Bias for certain axes. In GPT-2, the reported values were 78% for Characteristics, 77% for Socioeconomic class, 75% for Ability, and 38% for Nationality. In BlenderBot 2.0 3B, the values were 82% for Sexual orientation, 80% for Ability, 75% for Characteristics, and 54% for Nationality. The low- and high-perplexity examples were filtered so that descriptors within an axis had the same token count.

    For GPT-2, the lowest-perplexity Characteristics descriptors were mostly military-related, while high-perplexity examples included “half-timer,” “asylum-seeking,” and “US-born.” Within Ability, low-perplexity examples included “able-bodied,” “dyslexic,” and “who is deaf,” while “wheelchair-user,” “low-vision,” and “non-disabled” were high-perplexity examples. In BlenderBot 2.0 3B, low-perplexity Sexual orientation descriptors included “lesbian,” while “pan” was high-perplexity; low-perplexity Ability descriptors included “wheelchair-bound,” “neurotypical,” and “with a disability,” while “with difficulty moving,” “aphasic,” and “low-vision” were high-perplexity.

    Perplexity also depended strongly on the template. Opinionated templates such as “I love,” “I like,” and “I hate” had higher average perplexity and a wider descriptor range than neutral identity statements, indicating that model treatment of a descriptor can depend on the sentiment-bearing context rather than only on the descriptor itself. Nonce terms had substantially higher perplexity, as expected for intentionally unfamiliar words. In an independent RoBERTa-large evaluation using 500,000 randomly constructed SEAT sentences, the proportion of significant within-axis pseudo-log-likelihood differences was highest for Ability: 59% for “[NOUN PHRASE] is a person” and 60% for “[PLURAL NOUN PHRASE] are people.” Nationality and Age were among the lowest, at 28% and 26% for the singular template and 39% and 32% for the plural template.

  6. Knowl 6 — Generation-bias metrics based on 217 conversational styles

    equation

    Generated responses are classified by a 3-billion-parameter style classifier into S=217S=217 conversational styles. For descriptor dd, template tt, response index ii, and style ss, let ptdisp_{tdis} be the classifier probability that response ii belongs to style ss, and let NtdN_{td} be the number of responses generated for descriptor dd and template tt. The mean probability of style ss for descriptor dd under template tt is 1Ntd∑i=1Ntdptdis\frac{1}{N_{td}}\sum_{i=1}^{N_{td}}p_{tdis}. Full Gen Bias is the sum of the variances of these mean style probabilities across descriptors, averaged over the TT templates:

    FGB=1T∑t=1T∑s=1SVar⁡d(1Ntd∑i=1Ntdptdis).\mathrm{FGB}=\frac{1}{T}\sum_{t=1}^{T}\sum_{s=1}^{S}\operatorname{Var}_{d}\left(\frac{1}{N_{td}}\sum_{i=1}^{N_{td}}p_{tdis}\right).

    A higher Full Gen Bias means that the model’s style distribution changes more across demographic descriptors. Partial Gen Bias for a style cluster CC is obtained by retaining only styles s∈Cs\in C in the inner sum. The reported clusters are SYMPATHY = {Sympathetic, Compassionate, Empathetic}; ENVY = {Envious}; CURIOSITY = {Curious, Questioning}; CONFUSION = {Vacuous, Absentminded, Bewildered, Stupid, Confused}; HATE = {Hateful, Resentful}; and CARE = {Sensitive, Considerate, Warm, Kind, Caring, Respectful}. Descriptor mentions are replaced with the neutral phrase “left-handed” before style classification so that the classifier is less directly influenced by the descriptor string.

  7. Knowl 7 — Generated responses exhibit sympathy, confusion, and other descriptor-specific styles

    empirical result

    The generation experiment produced at least 240,000 responses per model using HOLISTICBIAS prompts. DialoGPT used beam search with beam size 10, three-gram blocking, and a minimum response length of 20 tokens. BlenderBot 2.0 used beam size 3, the same three-gram blocking rule, and the same minimum length.

    The original-model generation results below report Full Gen Bias followed by Partial Gen Bias for SYMPATHY, ENVY, CURIOSITY, CONFUSION, HATE, and CARE. All bias values are multiplied by 1,000.

    • DialoGPT: Full 3.04; partial values 0.74, 0.04, 0.08, 0.02, 0.04, 0.05.
    • BlenderBot 2.0 400M: Full 7.46; partial values 4.08, 0.07, 0.15, 0.02, 0.06, 0.28.
    • BlenderBot 2.0 3B: Full 8.89; partial values 2.77, 1.07, 0.86, 0.59, 0.42, 0.33.
    • BlenderBot 2.0 3B without internet search: Full 9.01; partial values 2.99, 0.98, 0.84, 0.53, 0.41, 0.35.

    DialoGPT showed less generation bias than either BlenderBot 2.0 model. The larger BlenderBot 2.0 model was more biased than the 400M model, and removing internet search barely changed the 3B model’s score. For BlenderBot 2.0 3B, sympathy styles made the largest contribution to Full Gen Bias, followed by envy, curiosity, and confusion. Descriptor frequency was also informative: few terms that were very common in pre-training data still elicited highly confused responses, although polysemy exceptions included “pan,” “ace,” and “poly.” Gender-and-sex descriptors generally elicited more curiosity and confusion, whereas body-type descriptors generally elicited less, largely independent of training frequency.

  8. Knowl 8 — Offensiveness scores depend strongly on both descriptor and template

    empirical result

    The 311-million-parameter Bot-Adversarial Dialogue (B.A.D.) classifier assigned substantially different offensive probabilities to HOLISTICBIAS prompts depending on the descriptor and the surrounding template. Across descriptors, the mean offensive probabilities and standard deviations were 46.9% and 31.0% for “I like [PLURAL NOUN PHRASE],” 45.2% and 30.8% for “I love [PLURAL NOUN PHRASE],” 25.3% and 30.5% for “I love being [NOUN PHRASE],” 25.6% and 26.8% for “I think [PLURAL NOUN PHRASE] are the best,” 2.5% and 8.6% for “I’m [NOUN PHRASE],” 97.8% and 4.5% for “I don’t like [PLURAL NOUN PHRASE],” 99.3% and 1.3% for “I think [PLURAL NOUN PHRASE] are the worst,” and 99.5% and 1.1% for “I hate [PLURAL NOUN PHRASE].”

    For identity statements such as “I’m [NOUN PHRASE],” very low scores were assigned to many neutral descriptors, but scores reached 50–72% for terms such as “sexy,” “hideous,” “hot,” “drug-abusing,” “incel,” and “trailer trash.” For “I love [PLURAL NOUN PHRASE],” scores ranged from 0–2.5% for terms such as “with a PhD” and “well-off” to 99–100% for terms such as “smelly,” “addict,” “low-vision,” “disabled,” and “trailer trash.” For “I hate [PLURAL NOUN PHRASE],” many descriptors, including Muslim, African, Mexican, Black, autistic, disabled, Deaf, Hindu, Jewish, and Indian, received 99.9–100% offensive probability. These results show that an offensiveness classifier can encode reactions to the identity descriptor itself, not only offensiveness in the generated linguistic context.

  9. Knowl 9 — Style-equality tuning converts descriptor-specific style bias into controlled-generation labels

    algorithm

    The style-equality method trains a generative dialogue model to reduce descriptor-specific deviations in its response style.

    Input: HOLISTICBIAS prompts, generated responses, a 217-style classifier, a bias threshold β\beta, and a generative dialogue model.

    Output: A tuned dialogue model that can be prompted to generate responses labeled as low-bias.

    Generate responses for every descriptor and template in HOLISTICBIAS.
    For each response rtdir_{tdi}, obtain its 217-dimensional style-probability vector ptdip_{tdi}.
    For each descriptor dd, average the vectors over responses and templates to obtain mdm_d.
    Average the descriptor means to obtain the global style mean mˉ\bar m.
    For each response, calculate btdi=((ptdi−mˉ)⋅(md−mˉ))/∣∣md−mˉ∣∣αb_{tdi}=((p_{tdi}-\bar m)\cdot(m_d-\bar m))/||m_d-\bar m||^\alpha.
    Use α=0\alpha=0, selected after testing α=0,1,2\alpha=0,1,2 for comparable behavior across sympathy and confusion cases.
    Append the label bias when btdi>βb_{tdi}>\beta; otherwise append no_bias.
    Fine-tune the dialogue model on the context/response pairs with these labels.
    At generation time, request responses labeled no_bias to reduce descriptor-specific styles.

    The bias direction for descriptor dd is the line from the global mean style vector mˉ\bar m to the descriptor-specific mean mdm_d. A response is labeled high-bias when its scaled projection in that direction exceeds the threshold. The paper used β=0.0003\beta=0.0003 for DialoGPT and β=0.0030\beta=0.0030 for BlenderBot 2.0 3B in its primary experiments. Training used batches of 16 on eight 32-GB Volta GPUs with early stopping by validation perplexity; the best DialoGPT run used SGD with learning rate 3×10−13\times10^{-1}, and the best BlenderBot 2.0 3B run used Adam with 100 warmup steps and learning rate 3×10−63\times10^{-6}.

  10. Knowl 10 — Style-equality tuning reduces measured bias but introduces trade-offs

    empirical result

    Bias-reduction tuning reduced Full Gen Bias from 3.04 to 2.66 for DialoGPT, a 13% reduction, and from 8.89 to 6.74 for BlenderBot 2.0 3B, a 24% reduction. For BlenderBot 2.0 3B, the axis-specific reductions were: Ability 9.59 to 7.59 (21%), Age 4.28 to 3.16 (26%), Body type 6.35 to 5.44 (14%), Characteristics 10.84 to 7.61 (30%), Cultural 7.64 to 5.75 (25%), Gender and sex 7.47 to 5.56 (26%), Nationality 3.74 to 3.39 (9%), Nonce 5.46 to 3.89 (29%), Political ideologies 7.59 to 6.44 (15%), Race and ethnicity 5.78 to 4.63 (20%), Religion 5.40 to 3.92 (27%), Sexual orientation 7.48 to 4.99 (33%), and Socioeconomic class 7.21 to 6.15 (15%).

    The reduction was uneven across style clusters. Sympathy, curiosity, and confusion variance fell substantially, care stayed approximately constant, and envy and hate variance increased. Frequently observed sympathy and confusion phrases declined for the descriptor groups most associated with those styles; for example, “I’m sorry to hear” fell from 30.3% to 19.4% among sympathy-associated descriptors, while “what is a” fell from 9.6% to 4.3% among confusion-associated descriptors.

    The method also produced important side effects. For BlenderBot 2.0 3B, the fraction of responses marked offensive by the B.A.D. classifier rose from 13.0% to 14.2%, and exact copying of the HOLISTICBIAS prompt rose from 17.3% to 20.0%. For DialoGPT, the corresponding offensive rate fell from 6.3% to 5.3%. Human pairwise evaluations found the tuned model comparable to the original: tuned DialoGPT was preferred in 45% of trials, judged more human in 48%, and more interesting in 47%; tuned BlenderBot 2.0 3B received 50%, 52%, and 51%, respectively. None of these human comparisons was statistically significant at p<0.05p<0.05. The authors therefore present style equality as a proof of concept rather than a generally safe mitigation: forcing style equality can suppress justified responses, increase hate or parroting, and reduce a complex notion of harm to one numerical score.

Coverage note — No substantial contributed material was deliberately omitted; detailed individual descriptor inventories, auxiliary plots, and extended before/after response samples were condensed because the load-bearing taxonomy, metrics, findings, mitigation, and limitations are represented above.

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Citation

MLA
Smith, E. M., et al. “"I'm Sorry to Hear That": Finding New Biases in Language Models with a Holistic Descriptor Dataset”. arXiv, 2022, http://arxiv.org/abs/2205.09209v2.
APA
Smith, E. M., Hall, M., Kambadur, M., Presani, E., & Williams, A. (2022). "I'm sorry to hear that": Finding New Biases in Language Models with a Holistic Descriptor Dataset. arXiv. http://arxiv.org/abs/2205.09209v2
Chicago
Smith, E. M., M. Hall, M. Kambadur, E. Presani, and A. Williams. 2022. “"I'm Sorry to Hear That": Finding New Biases in Language Models with a Holistic Descriptor Dataset”. arXiv. http://arxiv.org/abs/2205.09209v2.
Harvard
Smith, E.M. et al. (2022) “"I'm sorry to hear that": Finding New Biases in Language Models with a Holistic Descriptor Dataset”, arXiv [Preprint]. Available at: http://arxiv.org/abs/2205.09209v2.
Vancouver
1. Smith EM, Hall M, Kambadur M, Presani E, Williams A (2022) "I'm sorry to hear that": Finding New Biases in Language Models with a Holistic Descriptor Dataset. arXiv

BibTeX

@article{smith2022sorry,
  title = {"I'm sorry to hear that": Finding New Biases in Language Models with a Holistic Descriptor Dataset},
  author = {Smith, Eric Michael and Hall, Melissa and Kambadur, Melanie and Presani, Eleonora and Williams, Adina},
  year = {2022},
  journal = {arXiv},
  url = {http://arxiv.org/abs/2205.09209v2},
  eprint = {2205.09209}
}
Metadata:arXiv

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