Bias in large language models

Bias in large language models is a term for systematic deviations in the performance of large language models (LLMs) that lead to the generation of responses that unfairly or inaccurately reflect reality, reproducing and amplifying existing societal stereotypes. Unlike random errors, bias is systematic in nature and is caused by the characteristics of the training data and algorithms. LLMs can reproduce gender, ethnic, and other stereotypes, which poses a serious problem, especially in high-stakes fields such as medicine, law, and finance.

Sources of Bias

Bias in LLMs arises from two main sources: biased data and the characteristics of the algorithms themselves.

Biased Training Data

The primary cause of bias is training data that reflects historical, social, and cultural skews present in the world. LLMs are trained on vast corpora of text from the internet, books, and other human-created sources, and consequently, they inherit all the stereotypes contained within them.

• Unbalanced Representation: If certain demographic groups (e.g., ethnic minorities, women in specific professions) are underrepresented in the data, the model forms a distorted view of them. For example, LLMs often associate the word "doctor" with men and "nurse" with women, reproducing historical gender stereotypes.
• Historical and Cultural Skews: Data often reflects dominant cultural views and historical prejudices. A model trained on such texts will reproduce these views while ignoring alternative perspectives.

Algorithmic Amplification

The architecture and training algorithms of LLMs can not only reproduce but also amplify existing biases in the data. Most modern LLMs are based on transformers and predict the next word based on statistical patterns. This causes the model to favor the most common patterns, which reinforces and amplifies dominant opinions and stereotypes, while rare and atypical cases are ignored. This mechanism can turn a minor skew in the data into a pronounced bias in the model's responses.

Types of Bias and Examples

Social and Demographic Biases

This is the most studied type of bias, which includes stereotypes related to gender, race, age, religion, and other social characteristics.

• Gender Stereotypes: LLMs often associate certain professions and qualities with a specific gender. For example, when prompted for a "strong leader," the model is more likely to generate a description of a man.
• Racial and Ethnic Stereotypes: Models can reproduce negative stereotypes about various ethnic groups. Studies have shown that LLM-based moderation algorithms may judge messages written in African American Vernacular English (AAVE) more harshly, mistakenly considering them more offensive.
• In-group Bias ("Us vs. Them"): A 2024 study showed that LLMs exhibit significant in-group bias. When given a prompt associating it with a specific group ("We..."), the model tends to speak favorably of that group and disparagingly of "outsiders".

Structural and Cognitive Biases

These biases are related to architectural features and information processing.

• Positional Bias: A study from the Massachusetts Institute of Technology (MIT) found that models give disproportionate weight to information at the beginning and end of a document, often "missing" details in the middle. This can affect accuracy when working with long texts.
• Tendency to Average: As probabilistic models, LLMs tend to generate the most frequent (average) responses, which leads to ignoring rare but important facts, exceptions, and minority opinions.
• Confirmation Bias: LLMs can exhibit a tendency to reproduce logical patterns present in the training data, even if they contain biases, and to ignore contradictory information.

A Practical Example

A World Bank study found that when analyzing interviews with refugees, an LLM systematically distorted the meaning of their statements based on their origin and gender. The model misinterpreted the aspirations of refugee parents for their children's success, likely due to the absence of similar narratives in its training data, which consisted predominantly of texts by "middle-class white authors".

Risks and Consequences

• Amplification of Discrimination: In fields such as hiring, lending, and law, biased LLMs can make discriminatory decisions, reinforcing social inequality.
• Propagation of Stereotypes: The widespread use of LLMs in search engines and chatbots can lead to the replication and normalization of harmful stereotypes.
• Erosion of Trust in Technology: If users encounter systematic bias, it erodes their trust in artificial intelligence technologies as a whole.
• Creation of Information Bubbles: Algorithms can shape outputs to align with a user's presumed views, which supports echo chambers and marginalizes minority opinions.

Methods for Detecting and Mitigating Bias

To combat bias, researchers and developers apply a comprehensive approach, working at three levels: data, model, and post-processing.

Data-Level Interventions

This is the most fundamental approach. It includes:

• Cleaning and Balancing: Removing toxic and biased content from training data.
• Data Augmentation: Adding examples from underrepresented groups to balance proportions.

Model-Level Modification

This approach focuses on modifying the training algorithm itself:

• Fairness Constraints: Special constraints are introduced into the loss function that "penalize" the model for exhibiting certain types of bias.
• Architectural Changes: Researchers are exploring options like modifying attention mechanisms or adding control modules that monitor and correct biased associations.

Post-Processing of Results

This method is applied during the response generation stage:

• Filtering and Adjustment: Special algorithms analyze the generated text and soften or remove potentially discriminatory language.
• Reinforcement Learning from Human Feedback (RLHF): The model is specifically fine-tuned to provide more neutral and safe responses based on ratings provided by humans.

Despite significant progress, completely eliminating bias from LLMs has not yet been achieved. This remains one of the key research areas aimed at creating fairer and more reliable AI systems.

External links

• Generative language models exhibit social identity biases — A study in Nature Computational Science
• Unpacking the bias of large language models — An article from MIT News
• Algorithmic bias — Wikipedia

See also

• BBQ (Bias Benchmark for Question Answering)
• BOLD (Bias in Open-Ended Language Generation Dataset)
• RealToxicityPrompts
• Generation bias (LLM)
• Stochastic parrot

Literature

• Guo, Y. et al. (2024). Bias in Large Language Models: Origin, Evaluation, and Mitigation. arXiv:2411.10915.
• Gallegos, I. O. et al. (2023). Bias and Fairness in Large Language Models: A Survey. arXiv:2309.00770.
• Wu, X. et al. (2025). On the Emergence of Position Bias in Transformers. arXiv:2502.01951.
• Hu, T. et al. (2024). Generative Language Models Exhibit Social Identity Biases. Nature Computational Science, 5, 65-75. Full text.
• Sheng, E. et al. (2019). The Woman Worked as a Babysitter: On Biases in Language Generation. In EMNLP-2019. PDF.
• Bender, E. M. et al. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?. In ACM FAccT 2021. DOI:10.1145/3442188.3445922.
• Bai, X. et al. (2024). Measuring Implicit Bias in Explicitly Unbiased Large Language Models. arXiv:2402.04105.
• Ma, C. et al. (2024). Debiasing Large Language Models with Structured Knowledge. In Findings of ACL 2024, pp. 10274-10287. [4].
• Mohammadi, B. (2024). Creativity Has Left the Chat: The Price of Debiasing Language Models. arXiv:2406.05587.
• Ayaz, A. et al. (2023). Taught by the Internet: Exploring Bias in OpenAI’s GPT-3. arXiv:2306.02428.
• Benedetto, L. & Stella, M. (2023). Cognitive Network Science Reveals Bias in GPT-3, GPT-3.5-Turbo, and GPT-4 Mirroring Math Anxiety in High-School Students. Applied Sciences, 13(3). Open access.