EMNLP: Unsupervised Concept Vector Extraction for Bias Control in LLMs

20 November 2025 LLM, Hannah Cyberey, Yangfeng Ji, activation steering, bias, fairness

Our paper on extracting concept vectors for LLMs was presented at the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP):

Hannah Cyberey, Yangfeng Ji, and David Evans. In Conference on Empirical Methods in Natural Language Processing (EMNLP). Suzhou, China. November 2025. [ACL Anthology [arXiv] [Code]

Steering “gender” concept in QWEN-1.8B, evaluated on an example from Winogenerated fill-in- the-blank task. Baseline shows the original probabilities with no steering applied.

Congratulations, Dr. Cyberey!

10 July 2025 Hannah Cyberey, natural language processing, adversarial machine learning, bias, censorship, steering, fairness, PhD defense

Congratulations to Hannah Cyberey for successfully defending her PhD thesis!

Sensitivity Auditing for Trustworthy Language Models

Large language models (LLMs) have demonstrated impressive capabilities across a wide range of tasks. Yet, they remain unreliable and pose serious social and ethical risks, including reinforcing social stereotypes, spreading misinformation, and facilitating malicious uses. Despite their growing presence in high-stakes settings, current evaluation practices often fail to address these risks.

This dissertation aims to advance the reliability of LLMs by developing rigorous, context-aware evaluation methodologies. We argue that model reliability should be assessed with respect to its intended uses (i.e., how it should operate and under what context) through fine-grained measurements beyond binary judgments. We propose to (1) improve evaluation reliability, (2) design mitigation strategies to control model behavior, and (3) develop auditing techniques for accountability.

The Mismeasure of Man and Models

11 August 2024 Hannah Chen, Yangfeng Ji, David Evans, auditing, LLMs, bias

Evaluating Allocational Harms in Large Language Models

Blog post written by Hannah Chen

Our work considers allocational harms that arise when model predictions are used to distribute scarce resources or opportunities.

Current Bias Metrics Do Not Reliably Reflect Allocation Disparities

Several methods have been proposed to audit large language models (LLMs) for bias when used in critical decision-making, such as resume screening for hiring. Yet, these methods focus on predictions, without considering how the predictions are used to make decisions. In many settings, making decisions involve prioritizing options due to limited resource constraints. We find that prediction-based evaluation methods, which measure bias as the average performance gap (δ) in prediction outcomes, do not reliably reflect disparities in allocation decision outcomes.

Adjectives Can Reveal Gender Biases Within NLP Models

17 August 2023 Jason Briegel, Hannah Chen, adversarial machine learning, LLMs, generative AI, bias, machine learning

Post by Jason Briegel and Hannah Chen

Because NLP models are trained with human corpora (and now, increasingly on text generated by other NLP models that were originally trained on human language), they are prone to inheriting common human stereotypes and biases. This is problematic, because with their growing prominence they may further propagate these stereotypes (Sun et al., 2019). For example, interest is growing in mitigating bias in the field of machine translation, where systems such as Google translate were observed to default to translating gender-neutral pronouns as male pronouns, even with feminine cues (Savoldi et al., 2021).