132 citations · 289 across the 26 of their papers we have counts for
27 papers
Olmo 3
Team Olmo, :, Allyson Ettinger +66
We introduce Olmo 3, a family of state-of-the-art, fully-open language models at the 7B and 32B parameter scales. Olmo 3 model construction targets long-context reasoning, function…
DR Tulu: Reinforcement Learning with Evolving Rubrics for Deep Research
Rulin Shao, Akari Asai, Shannon Zejiang Shen +18
Deep research agents perform multi-step research to produce long-form, well-attributed answers. However, most open deep research agents are trained on easily verifiable short-form…
Completion Collaboration: Scaling Collaborative Effort with Agents
Shannon Zejiang Shen, Valerie Chen, Ken Gu +11
Current evaluations of agents remain centered around one-shot task completion, failing to account for the inherently iterative and collaborative nature of many real-world problems,…
SPG: Sandwiched Policy Gradient for Masked Diffusion Language Models
Chenyu Wang, Paria Rashidinejad, DiJia Su +9
Diffusion large language models (dLLMs) are emerging as an efficient alternative to autoregressive models due to their ability to decode multiple tokens in parallel. However, align…
When One LLM Drools, Multi-LLM Collaboration Rules
Shangbin Feng, Wenxuan Ding, Alisa Liu +10
This position paper argues that in many realistic (i.e., complex, contextualized, subjective) scenarios, one LLM is not enough to produce a reliable output. We challenge the status…
Retrieval-augmented systems can be dangerous medical communicators
Lionel Wong, Ayman Ali, Raymond Xiong +3
Patients have long sought health information online, and increasingly, they are turning to generative AI to answer their health-related queries. Given the high stakes of the medica…
SelfCite: Self-Supervised Alignment for Context Attribution in Large Language Models
Yung-Sung Chuang, Benjamin Cohen-Wang, Shannon Zejiang Shen +6
We introduce SelfCite, a novel self-supervised approach that aligns LLMs to generate high-quality, fine-grained, sentence-level citations for the statements in their generated resp…
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.…
A Design Space for Intelligent and Interactive Writing Assistants
Mina Lee, Katy Ilonka Gero, John Joon Young Chung +33
In our era of rapid technological advancement, the research landscape for writing assistants has become increasingly fragmented across various research communities. We seek to addr…
Learning to Decode Collaboratively with Multiple Language Models
Shannon Zejiang Shen, Hunter Lang, Bailin Wang +2
We propose a method to teach multiple large language models (LLM) to collaborate by interleaving their generations at the token level. We model the decision of which LLM generates…
A Data-Centric Approach To Generate Faithful and High Quality Patient Summaries with Large Language Models
Stefan Hegselmann, Shannon Zejiang Shen, Florian Gierse +3
Patients often face difficulties in understanding their hospitalizations, while healthcare workers have limited resources to provide explanations. In this work, we investigate the…
Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research
Luca Soldaini, Rodney Kinney, Akshita Bhagia +33
Information about pretraining corpora used to train the current best-performing language models is seldom discussed: commercial models rarely detail their data, and even open model…
Towards Verifiable Text Generation with Symbolic References
Lucas Torroba Hennigen, Shannon Shen, Aniruddha Nrusimha +3
LLMs are vulnerable to hallucinations, and thus their outputs generally require laborious human verification for high-stakes applications. To this end, we propose symbolically grou…
American Stories: A Large-Scale Structured Text Dataset of Historical U.S. Newspapers
Melissa Dell, Jacob Carlson, Tom Bryan +7
Existing full text datasets of U.S. public domain newspapers do not recognize the often complex layouts of newspaper scans, and as a result the digitized content scrambles texts fr…
Conceptualizing Machine Learning for Dynamic Information Retrieval of Electronic Health Record Notes
Sharon Jiang, Shannon Shen, Monica Agrawal +5
The large amount of time clinicians spend sifting through patient notes and documenting in electronic health records (EHRs) is a leading cause of clinician burnout. By proactively…
Are Layout-Infused Language Models Robust to Layout Distribution Shifts? A Case Study with Scientific Documents
Catherine Chen, Zejiang Shen, Dan Klein +3
Recent work has shown that infusing layout features into language models (LMs) improves processing of visually-rich documents such as scientific papers. Layout-infused LMs are ofte…
The Semantic Reader Project: Augmenting Scholarly Documents through AI-Powered Interactive Reading Interfaces
Kyle Lo, Joseph Chee Chang, Andrew Head +52
Scholarly publications are key to the transfer of knowledge from scholars to others. However, research papers are information-dense, and as the volume of the scientific literature…
Beyond Summarization: Designing AI Support for Real-World Expository Writing Tasks
Zejiang Shen, Tal August, Pao Siangliulue +6
Large language models have introduced exciting new opportunities and challenges in designing and developing new AI-assisted writing support tools. Recent work has shown that levera…
The Semantic Scholar Open Data Platform
Rodney Kinney, Chloe Anastasiades, Russell Authur +45
The volume of scientific output is creating an urgent need for automated tools to help scientists keep up with developments in their field. Semantic Scholar (S2) is an open data pl…
Multi-LexSum: Real-World Summaries of Civil Rights Lawsuits at Multiple Granularities
Zejiang Shen, Kyle Lo, Lauren Yu +3
With the advent of large language models, methods for abstractive summarization have made great strides, creating potential for use in applications to aid knowledge workers process…
Don't Say What You Don't Know: Improving the Consistency of Abstractive Summarization by Constraining Beam Search
Daniel King, Zejiang Shen, Nishant Subramani +3
Abstractive summarization systems today produce fluent and relevant output, but often "hallucinate" statements not supported by the source text. We analyze the connection between h…
VILA: Improving Structured Content Extraction from Scientific PDFs Using Visual Layout Groups
Zejiang Shen, Kyle Lo, Lucy Lu Wang +3
Accurately extracting structured content from PDFs is a critical first step for NLP over scientific papers. Recent work has improved extraction accuracy by incorporating elementary…
LayoutParser: A Unified Toolkit for Deep Learning Based Document Image Analysis
Zejiang Shen, Ruochen Zhang, Melissa Dell +3
Recent advances in document image analysis (DIA) have been primarily driven by the application of neural networks. Ideally, research outcomes could be easily deployed in production…
PAWLS: PDF Annotation With Labels and Structure
Mark Neumann, Zejiang Shen, Sam Skjonsberg
Adobe's Portable Document Format (PDF) is a popular way of distributing view-only documents with a rich visual markup. This presents a challenge to NLP practitioners who wish to us…