584 citations · 1.1k across the 51 of their papers we have counts for
56 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…
FABLES: Evaluating faithfulness and content selection in book-length summarization
Yekyung Kim, Yapei Chang, Marzena Karpinska +5
While long-context large language models (LLMs) can technically summarize book-length documents (>100K tokens), the length and complexity of the documents have so far prohibited ev…
Know Your Audience: The benefits and pitfalls of generating plain language summaries beyond the "general" audience
Tal August, Kyle Lo, Noah A. Smith +1
Language models (LMs) show promise as tools for communicating science to the general public by simplifying and summarizing complex language. Because models can be prompted to gener…
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…
FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions
Orion Weller, Benjamin Chang, Sean MacAvaney +5
Modern Language Models (LMs) are capable of following long and complex instructions that enable a large and diverse set of user requests. While Information Retrieval (IR) models us…
OLMo: Accelerating the Science of Language Models
Dirk Groeneveld, Iz Beltagy, Pete Walsh +40
Language models (LMs) have become ubiquitous in both NLP research and in commercial product offerings. As their commercial importance has surged, the most powerful models have beco…
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…
InfoLossQA: Characterizing and Recovering Information Loss in Text Simplification
Jan Trienes, Sebastian Joseph, Jörg Schlötterer +5
Text simplification aims to make technical texts more accessible to laypeople but often results in deletion of information and vagueness. This work proposes InfoLossQA, a framework…
Paloma: A Benchmark for Evaluating Language Model Fit
Ian Magnusson, Akshita Bhagia, Valentin Hofmann +13
Evaluations of language models (LMs) commonly report perplexity on monolithic data held out from training. Implicitly or explicitly, this data is composed of domains--varying distr…
The Rise of Open Science: Tracking the Evolution and Perceived Value of Data and Methods Link-Sharing Practices
Hancheng Cao, Jesse Dodge, Kyle Lo +2
In recent years, funding agencies and journals increasingly advocate for open science practices (e.g. data and method sharing) to improve the transparency, access, and reproducibil…
BooookScore: A systematic exploration of book-length summarization in the era of LLMs
Yapei Chang, Kyle Lo, Tanya Goyal +1
Summarizing book-length documents (>100K tokens) that exceed the context window size of large language models (LLMs) requires first breaking the input document into smaller chunks…
When do Generative Query and Document Expansions Fail? A Comprehensive Study Across Methods, Retrievers, and Datasets
Orion Weller, Kyle Lo, David Wadden +4
Using large language models (LMs) for query or document expansion can improve generalization in information retrieval. However, it is unknown whether these techniques are universal…
Efficiency Pentathlon: A Standardized Arena for Efficiency Evaluation
Hao Peng, Qingqing Cao, Jesse Dodge +11
Rising computational demands of modern natural language processing (NLP) systems have increased the barrier to entry for cutting-edge research while posing serious environmental co…
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…
Decomposing Complex Queries for Tip-of-the-tongue Retrieval
Kevin Lin, Kyle Lo, Joseph E. Gonzalez +1
When re-finding items, users who forget or are uncertain about identifying details often rely on creative strategies for expressing their information needs -- complex queries that…
Complex Mathematical Symbol Definition Structures: A Dataset and Model for Coordination Resolution in Definition Extraction
Anna Martin-Boyle, Andrew Head, Kyle Lo +3
Mathematical symbol definition extraction is important for improving scholarly reading interfaces and scholarly information extraction (IE). However, the task poses several challen…
A Question Answering Framework for Decontextualizing User-facing Snippets from Scientific Documents
Benjamin Newman, Luca Soldaini, Raymond Fok +2
Many real-world applications (e.g., note taking, search) require extracting a sentence or paragraph from a document and showing that snippet to a human outside of the source docume…
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 BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset
Hugo Laurençon, Lucile Saulnier, Thomas Wang +51
As language models grow ever larger, the need for large-scale high-quality text datasets has never been more pressing, especially in multilingual settings. The BigScience workshop,…
CiteSee: Augmenting Citations in Scientific Papers with Persistent and Personalized Historical Context
Joseph Chee Chang, Amy X. Zhang, Jonathan Bragg +4
When reading a scholarly article, inline citations help researchers contextualize the current article and discover relevant prior work. However, it can be challenging to prioritize…
LongEval: Guidelines for Human Evaluation of Faithfulness in Long-form Summarization
Kalpesh Krishna, Erin Bransom, Bailey Kuehl +4
While human evaluation remains best practice for accurately judging the faithfulness of automatically-generated summaries, few solutions exist to address the increased difficulty a…
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…
Open Domain Multi-document Summarization: A Comprehensive Study of Model Brittleness under Retrieval
John Giorgi, Luca Soldaini, Bo Wang +4
Multi-document summarization (MDS) assumes a set of topic-related documents are provided as input. In practice, this document set is not always available; it would need to be retri…