Nir Grinberg

@nirg.bsky.social

183 Followers 174 Following

Assistant prof. at BGU in the field of Computational Social Science.

Personalization performance across BLEU, RougeL, BertScoreF1, and POS-JSD.
The ablation study showing performance as a function of training size and LoRA rank
The illustrative diagram shows our approach for training personalized LLMs to emulate Judge A (textualist) vs. Judge B (purposivist) and how those trained models might respond to a legal query.
Sociodemographic characteristics among different political exposure types. Sample averages are marked in a gray dashed line. Ninety-five percent bootstrapped CIs are shown (mostly occluded due to their small size). CI = confidence interval.
The composition of political exposure across clusters. The share of politics curated by different actor types (y-axis) across clusters (x-axis). Darker-colored bars represent direct exposure to media organizations, journalists, politicians, OLs, and social peers. Lighter-colored bars represent indirect exposure to media organizations, journalists, politicians, or opinion leaders through social peers. OL = opinion leader.
Prototypical types of individual political exposure. Each point in panel (A) represents the political exposure of a single panel member, reduced to two dimensions using the UMAP algorithm, and colored by the cluster assignment obtained from HDBSCAN. Panel (B) shows the median number of political tweets available to individuals per day (left bars), and their percentage out of all tweets available to them on Twitter (right bars). Cluster labels and their share in the population are specified on the x-axis. Colors are consistent between the two figure panels. Ninety-five percent bootstrapped CIs are omitted from the figure due to their small magnitude, which are upper bounded by twenty-seven exposures to tweets and 0.28 percent, respectively. OL = opinion leader; CI = confidence interval; UMAP = Uniform Manifold Approximation and Projection.