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Towards sub-milliarcsecond astrometric precision using seeing-limited imaging
Noam Segev, Eran O. Ofek, Yossi Shvartzvald +21
The Earth's atmospheric turbulence degrades the precision of ground-based astrometry. Here, we discuss these limitations and propose that, with proper treatment of systematics and…
The FACTS Leaderboard: A Comprehensive Benchmark for Large Language Model Factuality
Aileen Cheng, Alon Jacovi, Amir Globerson +62
We introduce The FACTS Leaderboard, an online leaderboard suite and associated set of benchmarks that comprehensively evaluates the ability of language models to generate factually…
Confidence Improves Self-Consistency in LLMs
Amir Taubenfeld, Tom Sheffer, Eran Ofek +4
Self-consistency decoding enhances LLMs' performance on reasoning tasks by sampling diverse reasoning paths and selecting the most frequent answer. However, it is computationally e…
A search for minute-time-scale flares from the transient AT\,2024wpp
Eran O. Ofek, Lior Ozer, Ruslan Konno +21
The AT 2018cow-like fast blue optical transient AT2022tsd showed a large number of few-minute-duration, high-luminosity (~10^43 erg/s) flares. We present an intensive search for su…
Accurate photometric calibration by fitting the system transmission
S. Garrappa, E. O. Ofek, S. Ben-Ami +10
Transforming the instrumental photometry of ground-based telescopes into a calibrated physical flux in a well-defined passband is a major challenge in astronomy. Along with the int…
DRAGged into Conflicts: Detecting and Addressing Conflicting Sources in Search-Augmented LLMs
Arie Cattan, Alon Jacovi, Ori Ram +6
Retrieval Augmented Generation (RAG) is a commonly used approach for enhancing large language models (LLMs) with relevant and up-to-date information. However, the retrieved sources…