45 citations · 127 across the 24 of their papers we have counts for
3 papers · 1 filter
Do Factuality Methods Make LLMs More Factual? Reproducibility and Fair Comparison
Pratuat Amatya, Vinay Setty, V Venktesh +1
Large Language Models (LLMs) are aligned to human preferences, after pre-training and supervised fine-tuning steps. However, standard RL alignment steps such as RLHF do not focus o…
Data Efficient Sample Selection for In-Context Learning
V Venktesh, Cem levi, Cem Levi +1
The In-context learning (ICL) paradigm aids large language models (LLMs) to adapt to new tasks without need for fine-tuning. However, selecting an optimal combination of demonstrat…
The CLEF-2026 CheckThat! Lab: Advancing Multilingual Fact-Checking
Julia Maria Struß, Sebastian Schellhammer, Stefan Dietze +9
The CheckThat! lab aims to advance the development of innovative technologies combating disinformation and manipulation efforts in online communication across a multitude of langua…