About AllenNLP

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.

Featured Software

AI2 Tango

A Python library for choreographing your machine learning research. Construct machine learning experiments out of repeatable, reusable steps.

View

AllenNLP Library

A natural language processing platform for building state-of-the-art models. A complete platform for solving natural language processing tasks in PyTorch.

View
  • Evaluating In-Context Learning of Libraries for Code Generation

    Arkil Patel, Siva Reddy, Dzmitry Bahdanau, Pradeep DasigiNAACL2024 Contemporary Large Language Models (LLMs) exhibit a high degree of code generation and comprehension capability. A particularly promising area is their ability to interpret code modules from unfamiliar libraries for solving user-instructed tasks. Recent work…
  • TRAM: Bridging Trust Regions and Sharpness Aware Minimization

    Tom Sherborne, Naomi Saphra, Pradeep Dasigi, Hao PengICLR2024 By reducing the curvature of the loss surface in the parameter space, Sharpness-aware minimization (SAM) yields widespread robustness improvement under domain transfer. Instead of focusing on parameters, however, this work considers the transferability of…
  • What's In My Big Data?

    Yanai Elazar, Akshita Bhagia, Ian Magnusson, Abhilasha Ravichander, Dustin Schwenk, Alane Suhr, Pete Walsh, Dirk Groeneveld, Luca Soldaini, Sameer Singh, Hanna Hajishirzi, Noah A. Smith, Jesse DodgeICLR2024 Large text corpora are the backbone of language models. However, we have a limited understanding of the content of these corpora, including general statistics, quality, social factors, and inclusion of evaluation data (contamination). In this work, we propose…
  • Estimating the Causal Effect of Early ArXiving on Paper Acceptance

    Yanai Elazar, Jiayao Zhang, David Wadden, Boshen Zhang, Noah A. SmithCLearR2024 What is the effect of releasing a preprint of a paper before it is submitted for peer review? No randomized controlled trial has been conducted, so we turn to observational data to answer this question. We use data from the ICLR conference (2018--2022) and…
  • A Survey on Data Selection for Language Models

    Alon Albalak, Yanai Elazar, Sang Michael Xie, Shayne Longpre, Nathan Lambert, Xinyi Wang, Niklas Muennighoff, Bairu Hou, Liangming Pan, Haewon Jeong, Colin Raffel, Shiyu Chang, Tatsunori Hashimoto, William Yang WangarXiv2024 A major factor in the recent success of large language models is the use of enormous and ever-growing text datasets for unsupervised pre-training. However, naively training a model on all available data may not be optimal (or feasible), as the quality of…

Qasper

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.

A Dataset of Incomplete Information Reading Comprehension Questions

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: ZEroShot learning from Task descriptions

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.

MOCHA

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.

AI’s Climate Impact Goes beyond Its Emissions

Scientific American
December 7, 2023
Read the Article

Peeking Inside Pandora’s Box: Unveiling the Hidden Complexities of Language Model Datasets with ‘What’s in My Big Data’? (WIMBD)

Marktechpost
November 5, 2023
Read the Article

AI Is Becoming More Powerful—but Also More Secretive

Wired
October 19, 2023
Read the Article

Your Personal Information Is Probably Being Used to Train Generative AI Models

Scientific American
October 19, 2023
Read the Article

Inside the secret list of websites that make AI like ChatGPT sound smart

The Washington Post
April 19, 2023
Read the Article

AI can help address climate change—as long as it doesn’t exacerbate it

Fast Company
February 15, 2023
Read the Article

How to Detect AI-Generated Text, According to Researchers

Wired
February 8, 2023
Read the Article

Could AI help you to write your next paper?

Nature
October 31, 2022
Read the Article

Podcasts

  • NLP Highlights

    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.

    You can also find NLP Highlights on Apple Podcasts, Spotify, PlayerFM, or Stitcher.