2 citations · 4 across the 15 of their papers we have counts for
16 papers
Let the Experts Speak: Improving Survival Prediction & Calibration via Mixture-of-Experts Heads
Todd Morrill, Aahlad Puli, Murad Megjhani +2
Deep mixture-of-experts models have attracted a lot of attention for survival analysis problems, particularly for their ability to cluster similar patients together. In practice, g…
Guiding LLM Decision-Making with Fairness Reward Models
Zara Hall, Melanie Subbiah, Thomas P Zollo +2
Large language models are increasingly used to support high-stakes decisions, potentially influencing who is granted bail or receives a loan. Naive chain-of-thought sampling can im…
Confidence Calibration in Vision-Language-Action Models
Thomas P Zollo, Richard Zemel
Trustworthy robot behavior requires not only high levels of task success but also that the robot can reliably quantify how likely it is to succeed. To this end, we present a first-…
Replay Can Provably Increase Forgetting
Yasaman Mahdaviyeh, James Lucas, Mengye Ren +3
Continual learning seeks to enable machine learning systems to solve an increasing corpus of tasks sequentially. A critical challenge for continual learning is forgetting, where th…
Towards Safety Reasoning in LLMs: AI-agentic Deliberation for Policy-embedded CoT Data Creation
Tharindu Kumarage, Ninareh Mehrabi, Anil Ramakrishna +6
Safety reasoning is a recent paradigm where LLMs reason over safety policies before generating responses, thereby mitigating limitations in existing safety measures such as over-re…
Societal Alignment Frameworks Can Improve LLM Alignment
Karolina Stańczak, Nicholas Meade, Mehar Bhatia +14
Recent progress in large language models (LLMs) has focused on producing responses that meet human expectations and align with shared values - a process coined alignment. However,…
Towards Effective Discrimination Testing for Generative AI
Thomas P. Zollo, Nikita Rajaneesh, Richard Zemel +2
Generative AI (GenAI) models present new challenges in regulating against discriminatory behavior. In this paper, we argue that GenAI fairness research still has not met these chal…
Attribute Controlled Fine-tuning for Large Language Models: A Case Study on Detoxification
Tao Meng, Ninareh Mehrabi, Palash Goyal +6
We propose a constraint learning schema for fine-tuning Large Language Models (LLMs) with attribute control. Given a training corpus and control criteria formulated as a sequence-l…
Improving Predictor Reliability with Selective Recalibration
Thomas P. Zollo, Zhun Deng, Jake C. Snell +2
A reliable deep learning system should be able to accurately express its confidence with respect to its predictions, a quality known as calibration. One of the most effective ways…
Toward Informal Language Processing: Knowledge of Slang in Large Language Models
Zhewei Sun, Qian Hu, Rahul Gupta +2
Recent advancement in large language models (LLMs) has offered a strong potential for natural language systems to process informal language. A representative form of informal langu…
Partial Federated Learning
Tiantian Feng, Anil Ramakrishna, Jimit Majmudar +6
Federated Learning (FL) is a popular algorithm to train machine learning models on user data constrained to edge devices (for example, mobile phones) due to privacy concerns. Typic…
Tokenization Matters: Navigating Data-Scarce Tokenization for Gender Inclusive Language Technologies
Anaelia Ovalle, Ninareh Mehrabi, Palash Goyal +6
Gender-inclusive NLP research has documented the harmful limitations of gender binary-centric large language models (LLM), such as the inability to correctly use gender-diverse Eng…
JAB: Joint Adversarial Prompting and Belief Augmentation
Ninareh Mehrabi, Palash Goyal, Anil Ramakrishna +6
With the recent surge of language models in different applications, attention to safety and robustness of these models has gained significant importance. Here we introduce a joint…
Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models
Thomas P. Zollo, Todd Morrill, Zhun Deng +3
The recent explosion in the capabilities of large language models has led to a wave of interest in how best to prompt a model to perform a given task. While it may be tempting to s…
On the steerability of large language models toward data-driven personas
Junyi Li, Ninareh Mehrabi, Charith Peris +5
Large language models (LLMs) are known to generate biased responses where the opinions of certain groups and populations are underrepresented. Here, we present a novel approach to…
Coordinated Replay Sample Selection for Continual Federated Learning
Jack Good, Jimit Majmudar, Christophe Dupuy +5
Continual Federated Learning (CFL) combines Federated Learning (FL), the decentralized learning of a central model on a number of client devices that may not communicate their data…