22 citations · 24 across the 5 of their papers we have counts for
5 papers
Liars' Bench: Evaluating Lie Detectors for Language Models
Kieron Kretschmar, Walter Laurito, Sharan Maiya +1
Prior work has introduced techniques for detecting when large language models (LLMs) lie, that is, generate statements they believe are false. However, these techniques are typical…
Cluster-norm for Unsupervised Probing of Knowledge
Walter Laurito, Sharan Maiya, Grégoire Dhimoïla +3
The deployment of language models brings challenges in generating reliable information, especially when these models are fine-tuned using human preferences. To extract encoded know…
AI-AI Bias: large language models favor communications generated by large language models
Walter Laurito, Benjamin Davis, Peli Grietzer +3
Are large language models (LLMs) biased in favor of communications produced by LLMs, leading to possible antihuman discrimination? Using a classical experimental design inspired by…
Towards Solving Fuzzy Tasks with Human Feedback: A Retrospective of the MineRL BASALT 2022 Competition
Stephanie Milani, Anssi Kanervisto, Karolis Ramanauskas +27
To facilitate research in the direction of fine-tuning foundation models from human feedback, we held the MineRL BASALT Competition on Fine-Tuning from Human Feedback at NeurIPS 20…
AIFB-WebScience at SemEval-2022 Task 12: Relation Extraction First -- Using Relation Extraction to Identify Entities
Nicholas Popovic, Walter Laurito, Michael Färber
In this paper, we present an end-to-end joint entity and relation extraction approach based on transformer-based language models. We apply the model to the task of linking mathemat…