8 citations · 16 across the 8 of their papers we have counts for
9 papers
Decoupling Memory from Context: Structured Memory for Token-Efficient Test-Time Continual Learning
Yehya Farhat, Michael Desmond, Anastasios Kyrillidis
Large language models (LLMs) are increasingly deployed in enterprise, scientific, and medical applications, where agents must incorporate domain-specific knowledge and adapt from e…
EvalAssist: A Human-Centered Tool for LLM-as-a-Judge
Zahra Ashktorab, Werner Geyer, Michael Desmond +6
With the broad availability of large language models and their ability to generate vast outputs using varied prompts and configurations, determining the best output for a given tas…
Granite Guardian
Inkit Padhi, Manish Nagireddy, Giandomenico Cornacchia +20
We introduce the Granite Guardian models, a suite of safeguards designed to provide risk detection for prompts and responses, enabling safe and responsible use in combination with…
Evaluating the Prompt Steerability of Large Language Models
Erik Miehling, Michael Desmond, Karthikeyan Natesan Ramamurthy +5
Building pluralistic AI requires designing models that are able to be shaped to represent a wide range of value systems and cultures. Achieving this requires first being able to ev…
Black-box Uncertainty Quantification Method for LLM-as-a-Judge
Nico Wagner, Michael Desmond, Rahul Nair +6
LLM-as-a-Judge is a widely used method for evaluating the performance of Large Language Models (LLMs) across various tasks. We address the challenge of quantifying the uncertainty…
Aligning Human and LLM Judgments: Insights from EvalAssist on Task-Specific Evaluations and AI-assisted Assessment Strategy Preferences
Zahra Ashktorab, Michael Desmond, Qian Pan +7
Evaluation of large language model (LLM) outputs requires users to make critical judgments about the best outputs across various configurations. This process is costly and takes ti…
Emerging Reliance Behaviors in Human-AI Content Grounded Data Generation: The Role of Cognitive Forcing Functions and Hallucinations
Zahra Ashktorab, Qian Pan, Werner Geyer +5
We investigate the impact of hallucinations and Cognitive Forcing Functions in human-AI collaborative content-grounded data generation, focusing on the use of Large Language Models…
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…
Exploring Prompt Engineering Practices in the Enterprise
Michael Desmond, Michelle Brachman
Interaction with Large Language Models (LLMs) is primarily carried out via prompting. A prompt is a natural language instruction designed to elicit certain behaviour or output from…