The AllenNLP team envisions language-centered AI that equitably serves humanity. We work to improve NLP systems' performance and accountability, and advance scientific methodologies for evaluating and understanding those systems. We deliver high-impact research of our own and masterfully-engineered open-source tools to accelerate NLP research around the world.
A Python library for choreographing your machine learning research. Construct machine learning experiments out of repeatable, reusable steps.View
A natural language processing platform for building state-of-the-art models. A complete platform for solving natural language processing tasks in PyTorch.View
- Zeqiu Wu, Yushi Hu, Weijia Shi, Nouha Dziri, Alane Suhr, Prithviraj Ammanabrolu, Noah A. Smith, Mari Ostendorf, Hanna HajishirziNeurIPS • 2023 Language models (LMs) often exhibit undesirable text generation behaviors, including generating false, toxic, or irrelevant outputs. Reinforcement learning from human feedback (RLHF) - where human preference judgments on LM outputs are transformed into a…
- Yizhong Wang, Hamish Ivison, Pradeep Dasigi, Jack Hessel, Tushar Khot, Khyathi Raghavi Chandu, David Wadden, Kelsey MacMillan, Noah A. Smith, Iz Beltagy, Hanna HajishirziNeurIPS • 2023 In this work we explore recent advances in instruction-tuning language models on a range of open instruction-following datasets. Despite recent claims that open models can be on par with state-of-the-art proprietary models, these claims are often accompanied…
- Jungo Kasai, Keisuke Sakaguchi, Yoichi Takahashi, Ronan Le Bras, Akari Asai, Xinyan Velocity Yu, Dragomir R. Radev, Noah A. Smith, Yejin Choi, Kentaro InuiNeurIPS • 2023 We introduce R EAL T IME QA, a dynamic question answering (QA) platform that announces questions and evaluates systems on a regular basis (weekly in this version). R E AL T IME QA inquires about the current world, and QA systems need to answer questions about…
- Hila Gonen, Srini Iyer, Terra Blevins, Noah A. Smith, Luke ZettlemoyerEMNLP Findings • 2023 Language models can be prompted to perform a wide variety of zero- and few-shot learning problems. However, performance varies significantly with the choice of prompt, and we do not yet understand why this happens or how to pick the best prompts. In this work…
- Orevaoghene Ahia, Sachin Kumar, Hila Gonen, Jungo Kasai, David R. Mortensen, Noah A. Smith, Yulia TsvetkovEMNLP • 2023 Language models have graduated from being research prototypes to commercialized products offered as web APIs, and recent works have highlighted the multilingual capabilities of these products. The API vendors charge their users based on usage, more…
Question Answering on Research Papers
A dataset containing 1585 papers with 5049 information-seeking questions asked by regular readers of NLP papers, and answered by a separate set of NLP practitioners.
13K reading comprehension questions on Wikipedia paragraphs that require following links in those paragraphs to other Wikipedia pages
IIRC is a crowdsourced dataset consisting of information-seeking questions requiring models to identify and then retrieve necessary information that is missing from the original context. Each original context is a paragraph from English Wikipedia and it comes with a set of links to other Wikipedia pages, and answering the questions requires finding the appropriate links to follow and retrieving relevant information from those linked pages that is missing from the original context.
ZEST is a benchmark for zero-shot generalization to unseen NLP tasks, with 25K labeled instances across 1,251 different tasks.
ZEST tests whether NLP systems can perform unseen tasks in a zero-shot way, given a natural language description of the task. It is an instantiation of our proposed framework "learning from task descriptions". The tasks include classification, typed entity extraction and relationship extraction, and each task is paired with 20 different annotated (input, output) examples. ZEST's structure allows us to systematically test whether models can generalize in five different ways.
A benchmark for training and evaluating generative reading comprehension metrics.
Posing reading comprehension as a generation problem provides a great deal of flexibility, allowing for open-ended questions with few restrictions on possible answers. However, progress is impeded by existing generation metrics, which rely on token overlap and are agnostic to the nuances of reading comprehension. To address this, we introduce a benchmark for training and evaluating generative reading comprehension metrics: MOdeling Correctness with Human Annotations. MOCHA contains 40K human judgement scores on model outputs from 6 diverse question answering datasets and an additional set of minimal pairs for evaluation. Using MOCHA, we train an evaluation metric: LERC, a Learned Evaluation metric for Reading Comprehension, to mimic human judgement scores.
Peeking Inside Pandora’s Box: Unveiling the Hidden Complexities of Language Model Datasets with ‘What’s in My Big Data’? (WIMBD)
November 5, 2023
AI Is Becoming More Powerful—but Also More Secretive
October 19, 2023
Your Personal Information Is Probably Being Used to Train Generative AI Models
October 19, 2023
Inside the secret list of websites that make AI like ChatGPT sound smart
April 19, 2023
AI can help address climate change—as long as it doesn’t exacerbate it
February 15, 2023
How to Detect AI-Generated Text, According to Researchers
February 8, 2023
Could AI help you to write your next paper?
October 31, 2022
How to shrink AI’s ballooning carbon footprint
July 19, 2022
NLP Highlights is AllenNLP’s podcast for discussing recent and interesting work related to natural language processing. Hosts from the AllenNLP team at AI2 offer short discussions of papers and occasionally interview authors about their work.