I’m Gabriel Recchia, a cognitive scientist interested in methods for aligning modern language models–i.e., getting them to do what we want them to do. I’m particularly interested in methods for accomplishing this in cases where naïve approaches to eliciting human preferences might not yield a training signal that results in the behaviour we want. One class of approaches involves making it easier for people to accurately evaluate the outputs of language models, e.g., by training them to critique their own outputs, or by training them to produce “reasoning traces” that are easy to evaluate. To find out how successful or unsuccessful different approaches are, we need to run experiments with human participants.
Previously, I was at the University of Cambridge’s Winton Centre for Risk and Evidence Communication, where I work on how to communicate information in ways that support comprehension and informed decision-making; I also lead on user testing research and evaluation of patient-friendly genetic reports and the NHS: Predict family of prognostic tools. I have spent much of my career involved in research investigating the capabilities, properties, and applications of distributional models trained on large volumes of text, and have continued this while at the Winton Centre to explore their applications in characterizing how risk is communicated and perceived. See my Google Scholar profile for a list of my most cited papers.
Previously, I was at the Centre for Research in the Arts, Social Sciences and Humanities, where I worked with distributional approaches to the analysis of large corpora of historical texts, and investigated conceptual change by attending to shifting statistical associations between words over time. This position also involved the development and testing of user interfaces for the display of complex quantitative information to individuals of various backgrounds.
I received my bachelor’s degree in Symbolic Systems from Stanford University in 2007, and my doctorate is in Cognitive Science at Indiana University, with a minor in computational linguistics and with language modelling as my content specialization. Publications, skills, and previous employers are listed on my CV; Google Scholar may be more up-to-date.