1 citations · 2 across the 5 of their papers we have counts for
5 papers
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…
Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai
Parinthapat Pengpun, Can Udomcharoenchaikit, Weerayut Buaphet +1
We present a synthetic data approach for instruction-tuning large language models (LLMs) for low-resource languages in a data-efficient manner, specifically focusing on Thai. We id…
DOLOMITES: Domain-Specific Long-Form Methodical Tasks
Chaitanya Malaviya, Priyanka Agrawal, Kuzman Ganchev +7
Experts in various fields routinely perform methodical writing tasks to plan, organize, and report their work. From a clinician writing a differential diagnosis for a patient, to a…
FRACTAL: Fine-Grained Scoring from Aggregate Text Labels
Yukti Makhija, Priyanka Agrawal, Rishi Saket +1
Large language models (LLMs) are being increasingly tuned to power complex generation tasks such as writing, fact-seeking, querying and reasoning. Traditionally, human or model fee…
Language and Task Arithmetic with Parameter-Efficient Layers for Zero-Shot Summarization
Alexandra Chronopoulou, Jonas Pfeiffer, Joshua Maynez +3
Parameter-efficient fine-tuning (PEFT) using labeled task data can significantly improve the performance of large language models (LLMs) on the downstream task. However, there are…