arXiv Computation and Language
Oct 9

Detecting Spin in Clinical Trials with Large Language Models

The paper presents a method for detecting spin in clinical trials by automatically identifying outcome switching using large language models (LLMs). The authors trained on 300 pairs of outcomes labeled for semantic similarity, then evaluated baseline text similarity models and open-source LLMs, applying prompt engineering, token‑probability classification, and majority voting. On a test set of 2,496 examples, the LLM‑based approach achieved an F1 score of 0.78 and accuracy of 0.90, surpassing baseline models but falling short of fine‑tuned BERT, and also produced natural‑language explanations that were manually evaluated for quality.

By Tja\v{s} Ajdovec, Marko Robnik-\v{S}ikonja, Simon \v{S}uster
arXiv Computation and Language
Oct 22

Is a 9B Decision Foundation Model Needed for Symptom-to-Disease Classification? Fine-Tuning Clef-Flash Against TF-IDF Baselines

The paper evaluates whether a 9‑billion parameter decision foundation model, Clef‑Flash, is necessary for symptom‑to‑disease classification. Fine‑tuning the model on the 24‑class Symptom2Disease benchmark yields 95.42% accuracy, but simple TF‑IDF linear classifiers achieve higher accuracy (98.75%) with far faster inference. Ablation studies show that predictions can hinge on single descriptive clauses, indicating that the benchmark may not fully capture the model’s capabilities.

By S. M. Mahbub Uddin Ahmed
arXiv Machine Learning
Jun 9

Do Larger Models Really Win in Drug Discovery? A Benchmark Assessment of Model Scaling in AI-Driven Molecular Property and Activity Prediction

arXiv:2604. 26498v3 Announce Type: replace Abstract: The rapid growth of molecular foundation models and large language models (LLMs) has encouraged a scale centred view of AI in drug discovery, in which larger pretrained models are expected to supersede compact cheminformatics models.

By Jinjiang Guo, Sheng Ding