1 citations · 3 across the 6 of their papers we have counts for
6 papers
A Representation-Level Assessment of Bias Mitigation in Foundation Models
Svetoslav Nizhnichenkov, Rahul Nair, Elizabeth Daly +1
We investigate how successful bias mitigation reshapes the embedding space of encoder-only and decoder-only foundation models, offering an internal audit of model behaviour through…
Interpreting LLM-as-a-Judge Policies via Verifiable Global Explanations
Jasmina Gajcin, Erik Miehling, Rahul Nair +3
Using LLMs to evaluate text, that is, LLM-as-a-judge, is increasingly being used at scale to augment or even replace human annotations. As such, it is imperative that we understand…
Humble AI in the real-world: the case of algorithmic hiring
Rahul Nair, Inge Vejsbjerg, Elizabeth Daly +2
Humble AI (Knowles et al., 2023) argues for cautiousness in AI development and deployments through scepticism (accounting for limitations of statistical learning), curiosity (accou…
Human-Centered Design Recommendations for LLM-as-a-Judge
Qian Pan, Zahra Ashktorab, Michael Desmond +5
Traditional reference-based metrics, such as BLEU and ROUGE, are less effective for assessing outputs from Large Language Models (LLMs) that produce highly creative or superior-qua…
Ranking Large Language Models without Ground Truth
Amit Dhurandhar, Rahul Nair, Moninder Singh +2
Evaluation and ranking of large language models (LLMs) has become an important problem with the proliferation of these models and their impact. Evaluation methods either require hu…
Explaining Knock-on Effects of Bias Mitigation
Svetoslav Nizhnichenkov, Rahul Nair, Elizabeth Daly +1
In machine learning systems, bias mitigation approaches aim to make outcomes fairer across privileged and unprivileged groups. Bias mitigation methods work in different ways and ha…