2 citations · 7 across the 10 of their papers we have counts for
5 papers · 1 filter
Epistemic Integrity in Large Language Models
Bijean Ghafouri, Shahrad Mohammadzadeh, James Zhou +8
Large language models are increasingly relied upon as sources of information, but their propensity for generating false or misleading statements with high confidence poses risks fo…
Template-Based Probes Are Imperfect Lenses for Counterfactual Bias Evaluation in LLMs
Farnaz Kohankhaki, D. B. Emerson, Jacob-Junqi Tian +2
Bias in large language models (LLMs) has many forms, from overt discrimination to implicit stereotypes. Counterfactual bias evaluation is a widely used approach to quantifying bias…
On The Role of Reasoning in the Identification of Subtle Stereotypes in Natural Language
Jacob-Junqi Tian, Omkar Dige, D. B. Emerson +1
Large language models (LLMs) are trained on vast, uncurated datasets that contain various forms of biases and language reinforcing harmful stereotypes that may be subsequently inhe…
Can Instruction Fine-Tuned Language Models Identify Social Bias through Prompting?
Omkar Dige, Jacob-Junqi Tian, David Emerson +1
As the breadth and depth of language model applications continue to expand rapidly, it is increasingly important to build efficient frameworks for measuring and mitigating the lear…
Soft-prompt Tuning for Large Language Models to Evaluate Bias
Jacob-Junqi Tian, David Emerson, Sevil Zanjani Miyandoab +3
Prompting large language models has gained immense popularity in recent years due to the advantage of producing good results even without the need for labelled data. However, this…