4 citations · 14 across the 9 of their papers we have counts for
10 papers
The Calls are Coming from Inside the Model: Investigating Probe-based Detection of Tool-Calling Errors in LLMs
Eric Yeats, Brendan Kennedy, Loc Truong +5
The hidden states of large language models (LLMs) are known to capture rich information relating to model knowledge and behavior that can be hard to extract from examination of inp…
TAGTorch: A PyTorch Library for Geometry, Topology, and Symmetry-Aware Machine Learning
Brendan Kennedy, Tegan Emerson, Gregory Roek +2
Over the last decade, neural networks have been applied to an increasingly diverse range of applications, including data with rich geometric, topological, or symmetry-related struc…
What do Geometric Hallucination Detection Metrics Actually Measure?
Eric Yeats, John Buckheit, Sarah Scullen +9
Hallucination remains a barrier to deploying generative models in high-consequence applications. This is especially true in cases where external ground truth is not readily availab…
Directional Concentration Uncertainty: A representational approach to uncertainty quantification for generative models
Souradeep Chattopadhyay, Brendan Kennedy, Sai Munikoti +2
In the critical task of making generative models trustworthy and robust, methods for Uncertainty Quantification (UQ) have begun to show encouraging potential. However, many of thes…
Social-Group-Agnostic Word Embedding Debiasing via the Stereotype Content Model
Ali Omrani, Brendan Kennedy, Mohammad Atari +1
Existing word embedding debiasing methods require social-group-specific word pairs (e.g., "man"-"woman") for each social attribute (e.g., gender), which cannot be used to mitigate…
The Moral Foundations Reddit Corpus
Jackson Trager, Alireza S. Ziabari, Elnaz Rahmati +15
Moral framing and sentiment can affect a variety of online and offline behaviors, including donation, environmental action, political engagement, and protest. Various computational…
Hate Speech Classifiers Learn Human-Like Social Stereotypes
Aida Mostafazadeh Davani, Mohammad Atari, Brendan Kennedy +1
Social stereotypes negatively impact individuals' judgements about different groups and may have a critical role in how people understand language directed toward minority social g…
Improving Counterfactual Generation for Fair Hate Speech Detection
Aida Mostafazadeh Davani, Ali Omrani, Brendan Kennedy +3
Bias mitigation approaches reduce models' dependence on sensitive features of data, such as social group tokens (SGTs), resulting in equal predictions across the sensitive features…
Fair Hate Speech Detection through Evaluation of Social Group Counterfactuals
Aida Mostafazadeh Davani, Ali Omrani, Brendan Kennedy +3
Approaches for mitigating bias in supervised models are designed to reduce models' dependence on specific sensitive features of the input data, e.g., mentioned social groups. Howev…
On Transferability of Bias Mitigation Effects in Language Model Fine-Tuning
Xisen Jin, Francesco Barbieri, Brendan Kennedy +3
Fine-tuned language models have been shown to exhibit biases against protected groups in a host of modeling tasks such as text classification and coreference resolution. Previous w…