9 papers · 1 filter
Prompting Complexity: Shortest Prompts for Texts and Behaviors in LLMs
Adrian Cosma
In this paper, we define the quantity of prompting complexity: for a fixed instruction-tuned language model, what is the shortest plausible prompt that makes deterministic decoding…
Improving Medical Communication using Rubric-Guided Counterfactual Recommendations
Adrian Cosma, Nicoleta-Nina Basoc, Andrei Niculae +2
Text-based telemedicine increasingly relies on lightweight patient feedback, however, such feedback primarily reflects perceived communication quality rather than medical accuracy.…
An In-Vitro Study on Cross-Lingual Generalization in Language Models
Adrian Cosma
Cross-lingual transfer in language models is difficult to study in natural corpora because lexical overlap, morphology, data imbalance, and tokenization are entangled. We introduce…
What Makes a Good Doctor Response? A Study on Text-Based Telemedicine
Adrian Cosma, Cosmin Dumitrache, Emilian Radoi
Text-based telemedicine has become an increasingly used mode of care, requiring clinicians to deliver medical advice clearly and effectively in writing. As platforms increasingly r…
Automatic Prompt Optimization for Dataset-Level Feature Discovery
Adrian Cosma, Oleg Szehr, David Kletz +2
Feature extraction from unstructured text is a critical step in many downstream classification pipelines, yet current approaches largely rely on hand-crafted prompts or fixed featu…
Training Language Models with homotokens Leads to Delayed Overfitting
Adrian Cosma, Stefan Ruseti, Emilian Radoi +1
Subword tokenization introduces a computational layer in language models where many distinct token sequences decode to the same surface form and preserve meaning, yet induce differ…