39 citations · 163 across the 23 of their papers we have counts for
6 papers · 1 filter
OpenScholar: Synthesizing Scientific Literature with Retrieval-augmented LMs
Akari Asai, Jacqueline He, Rulin Shao +22
Scientific progress depends on researchers' ability to synthesize the growing body of literature. Can large language models (LMs) assist scientists in this task? We introduce OpenS…
OLMoE: Open Mixture-of-Experts Language Models
Niklas Muennighoff, Luca Soldaini, Dirk Groeneveld +21
We introduce OLMoE, a fully open, state-of-the-art language model leveraging sparse Mixture-of-Experts (MoE). OLMoE-1B-7B has 7 billion (B) parameters but uses only 1B per input to…
CHIME: LLM-Assisted Hierarchical Organization of Scientific Studies for Literature Review Support
Chao-Chun Hsu, Erin Bransom, Jenna Sparks +5
Literature review requires researchers to synthesize a large amount of information and is increasingly challenging as the scientific literature expands. In this work, we investigat…
SciRIFF: A Resource to Enhance Language Model Instruction-Following over Scientific Literature
David Wadden, Kejian Shi, Jacob Morrison +11
We present SciRIFF (Scientific Resource for Instruction-Following and Finetuning), a dataset of 137K instruction-following instances for training and evaluation, covering 54 tasks.…
Source-Aware Training Enables Knowledge Attribution in Language Models
Muhammad Khalifa, David Wadden, Emma Strubell +4
Large language models (LLMs) learn a vast amount of knowledge during pretraining, but they are often oblivious to the source(s) of such knowledge. We investigate the problem of int…
KIWI: A Dataset of Knowledge-Intensive Writing Instructions for Answering Research Questions
Fangyuan Xu, Kyle Lo, Luca Soldaini +3
Large language models (LLMs) adapted to follow user instructions are now widely deployed as conversational agents. In this work, we examine one increasingly common instruction-foll…