Papers by Kristian Lum
The Impossibility of Fair LLMs (2025.acl-long)
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| Challenge: | Existing frameworks for evaluating large language models do not extend to general-purpose AI contexts or are infeasible in practice. |
| Approach: | They analyze a variety of technical fairness frameworks to find inherent challenges . they find that each framework does not logically extend to the general-purpose AI context . |
| Outcome: | The proposed frameworks do not logically extend to the general-purpose AI context or are infeasible in practice due to large amounts of unstructured training data and potential combinations of human populations, use cases, and sensitive attributes. |
Bias in Language Models: Beyond Trick Tests and Towards RUTEd Evaluation (2025.acl-long)
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| Challenge: | Standard bias benchmarks are used for large language models to measure the association between social attributes and single-word outputs. |
| Approach: | They adapt three standard bias metrics of next-word prediction to measure gender-occupation bias and develop an analogous RUTEd evaluation in three contexts of real-world LLM use. |
| Outcome: | The proposed benchmarks are robust to lengthening model outputs via a more realistic user prompt in the domain of gender-occupation bias. |
STAR: SocioTechnical Approach to Red Teaming Language Models (2024.emnlp-main)
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Laura Weidinger, John Mellor, Bernat Pegueroles, Nahema Marchal, Ravin Kumar, Kristian Lum, Canfer Akbulut, Mark Diaz, A. Bergman, Mikel Rodriguez, Verena Rieser, William Isaac
| Challenge: | STAR is a sociotechnical framework that improves on current best practices for red teaming safety of large language models. |
| Approach: | They propose a sociotechnical framework that improves on current best practices for red teaming safety of large language models. |
| Outcome: | The proposed framework improves on current best practices for red teaming safety of large language models. |