no code implementations • 2 May 2024 • James Mayfield, Eugene Yang, Dawn Lawrie, Sean MacAvaney, Paul McNamee, Douglas W. Oard, Luca Soldaini, Ian Soboroff, Orion Weller, Efsun Kayi, Kate Sanders, Marc Mason, Noah Hibbler
Reports with these qualities are necessary to satisfy the complex, nuanced, or multi-faceted information needs of users.
no code implementations • 11 Apr 2024 • Dawn Lawrie, Sean MacAvaney, James Mayfield, Paul McNamee, Douglas W. Oard, Luca Soldaini, Eugene Yang
The principal tasks are ranked retrieval of news in one of the three languages, using English topics.
1 code implementation • 22 Mar 2024 • Orion Weller, Benjamin Chang, Sean MacAvaney, Kyle Lo, Arman Cohan, Benjamin Van Durme, Dawn Lawrie, Luca Soldaini
First, we introduce our dataset FollowIR, which contains a rigorous instruction evaluation benchmark as well as a training set for helping IR models learn to better follow real-world instructions.
no code implementations • 6 Mar 2024 • Fangyuan Xu, Kyle Lo, Luca Soldaini, Bailey Kuehl, Eunsol Choi, David Wadden
To evaluate the capabilities of current LLMs on this task, we construct KIWI, a dataset of knowledge-intensive writing instructions in the scientific domain.
2 code implementations • 1 Feb 2024 • Dirk Groeneveld, Iz Beltagy, Pete Walsh, Akshita Bhagia, Rodney Kinney, Oyvind Tafjord, Ananya Harsh Jha, Hamish Ivison, Ian Magnusson, Yizhong Wang, Shane Arora, David Atkinson, Russell Authur, Khyathi Raghavi Chandu, Arman Cohan, Jennifer Dumas, Yanai Elazar, Yuling Gu, Jack Hessel, Tushar Khot, William Merrill, Jacob Morrison, Niklas Muennighoff, Aakanksha Naik, Crystal Nam, Matthew E. Peters, Valentina Pyatkin, Abhilasha Ravichander, Dustin Schwenk, Saurabh Shah, Will Smith, Emma Strubell, Nishant Subramani, Mitchell Wortsman, Pradeep Dasigi, Nathan Lambert, Kyle Richardson, Luke Zettlemoyer, Jesse Dodge, Kyle Lo, Luca Soldaini, Noah A. Smith, Hannaneh Hajishirzi
Given the importance of these details in scientifically studying these models, including their biases and potential risks, we believe it is essential for the research community to have access to powerful, truly open LMs.
1 code implementation • 31 Jan 2024 • Luca Soldaini, Rodney Kinney, Akshita Bhagia, Dustin Schwenk, David Atkinson, Russell Authur, Ben Bogin, Khyathi Chandu, Jennifer Dumas, Yanai Elazar, Valentin Hofmann, Ananya Harsh Jha, Sachin Kumar, Li Lucy, Xinxi Lyu, Nathan Lambert, Ian Magnusson, Jacob Morrison, Niklas Muennighoff, Aakanksha Naik, Crystal Nam, Matthew E. Peters, Abhilasha Ravichander, Kyle Richardson, Zejiang Shen, Emma Strubell, Nishant Subramani, Oyvind Tafjord, Pete Walsh, Luke Zettlemoyer, Noah A. Smith, Hannaneh Hajishirzi, Iz Beltagy, Dirk Groeneveld, Jesse Dodge, Kyle Lo
Language models have become a critical technology to tackling a wide range of natural language processing tasks, yet many details about how the best-performing language models were developed are not reported.
1 code implementation • 12 Jan 2024 • Li Lucy, Suchin Gururangan, Luca Soldaini, Emma Strubell, David Bamman, Lauren Klein, Jesse Dodge
Large language models' (LLMs) abilities are drawn from their pretraining data, and model development begins with data curation.
no code implementations • 16 Dec 2023 • Ian Magnusson, Akshita Bhagia, Valentin Hofmann, Luca Soldaini, Ananya Harsh Jha, Oyvind Tafjord, Dustin Schwenk, Evan Pete Walsh, Yanai Elazar, Kyle Lo, Dirk Groeneveld, Iz Beltagy, Hannaneh Hajishirzi, Noah A. Smith, Kyle Richardson, Jesse Dodge
We invite submissions to our benchmark and organize results by comparability based on compliance with guidelines such as removal of benchmark contamination from pretraining.
1 code implementation • 16 Nov 2023 • Hyunji Lee, Luca Soldaini, Arman Cohan, Minjoon Seo, Kyle Lo
Prevailing research practice today often relies on training dense retrievers on existing large datasets such as MSMARCO and then experimenting with ways to improve zero-shot generalization capabilities to unseen domains.
1 code implementation • 31 Oct 2023 • 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 Dodge
We open-source WIMBD's code and artifacts to provide a standard set of evaluations for new text-based corpora and to encourage more analyses and transparency around them.
no code implementations • 26 Sep 2023 • Pratyusha Ria Kalluri, William Agnew, Myra Cheng, Kentrell Owens, Luca Soldaini, Abeba Birhane
Moreover, the majority of these technologies specifically enable extracting data about human bodies and body parts.
no code implementations • 15 Sep 2023 • Orion Weller, Kyle Lo, David Wadden, Dawn Lawrie, Benjamin Van Durme, Arman Cohan, Luca Soldaini
Using large language models (LMs) for query or document expansion can improve generalization in information retrieval.
no code implementations • 15 Jul 2023 • Organizers Of QueerInAI, Nathan Dennler, Anaelia Ovalle, Ashwin Singh, Luca Soldaini, Arjun Subramonian, Huy Tu, William Agnew, Avijit Ghosh, Kyra Yee, Irene Font Peradejordi, Zeerak Talat, Mayra Russo, Jess de Jesus de Pinho Pinhal
However, these auditing processes have been criticized for their failure to integrate the knowledge of marginalized communities and consider the power dynamics between auditors and the communities.
no code implementations • 24 May 2023 • Benjamin Newman, Luca Soldaini, Raymond Fok, Arman Cohan, Kyle Lo
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 document.
no code implementations • 24 Apr 2023 • Dawn Lawrie, Sean MacAvaney, James Mayfield, Paul McNamee, Douglas W. Oard, Luca Soldaini, Eugene Yang
This is the first year of the TREC Neural CLIR (NeuCLIR) track, which aims to study the impact of neural approaches to cross-language information retrieval.
no code implementations • 29 Mar 2023 • Organizers Of QueerInAI, :, Anaelia Ovalle, Arjun Subramonian, Ashwin Singh, Claas Voelcker, Danica J. Sutherland, Davide Locatelli, Eva Breznik, Filip Klubička, Hang Yuan, Hetvi J, huan zhang, Jaidev Shriram, Kruno Lehman, Luca Soldaini, Maarten Sap, Marc Peter Deisenroth, Maria Leonor Pacheco, Maria Ryskina, Martin Mundt, Milind Agarwal, Nyx McLean, Pan Xu, A Pranav, Raj Korpan, Ruchira Ray, Sarah Mathew, Sarthak Arora, ST John, Tanvi Anand, Vishakha Agrawal, William Agnew, Yanan Long, Zijie J. Wang, Zeerak Talat, Avijit Ghosh, Nathaniel Dennler, Michael Noseworthy, Sharvani Jha, Emi Baylor, Aditya Joshi, Natalia Y. Bilenko, Andrew McNamara, Raphael Gontijo-Lopes, Alex Markham, Evyn Dǒng, Jackie Kay, Manu Saraswat, Nikhil Vytla, Luke Stark
We present Queer in AI as a case study for community-led participatory design in AI.
no code implementations • 25 Mar 2023 • Kyle Lo, Joseph Chee Chang, Andrew Head, Jonathan Bragg, Amy X. Zhang, Cassidy Trier, Chloe Anastasiades, Tal August, Russell Authur, Danielle Bragg, Erin Bransom, Isabel Cachola, Stefan Candra, Yoganand Chandrasekhar, Yen-Sung Chen, Evie Yu-Yen Cheng, Yvonne Chou, Doug Downey, Rob Evans, Raymond Fok, Fangzhou Hu, Regan Huff, Dongyeop Kang, Tae Soo Kim, Rodney Kinney, Aniket Kittur, Hyeonsu Kang, Egor Klevak, Bailey Kuehl, Michael Langan, Matt Latzke, Jaron Lochner, Kelsey MacMillan, Eric Marsh, Tyler Murray, Aakanksha Naik, Ngoc-Uyen Nguyen, Srishti Palani, Soya Park, Caroline Paulic, Napol Rachatasumrit, Smita Rao, Paul Sayre, Zejiang Shen, Pao Siangliulue, Luca Soldaini, Huy Tran, Madeleine van Zuylen, Lucy Lu Wang, Christopher Wilhelm, Caroline Wu, Jiangjiang Yang, Angele Zamarron, Marti A. Hearst, Daniel S. Weld
Scholarly publications are key to the transfer of knowledge from scholars to others.
1 code implementation • 22 Feb 2023 • Sean MacAvaney, Luca Soldaini
We then explore various approaches for predicting the relevance of unjudged documents with respect to a query and the known relevant document, including nearest neighbor, supervised, and prompting techniques.
1 code implementation • 24 Jan 2023 • Rodney Kinney, Chloe Anastasiades, Russell Authur, Iz Beltagy, Jonathan Bragg, Alexandra Buraczynski, Isabel Cachola, Stefan Candra, Yoganand Chandrasekhar, Arman Cohan, Miles Crawford, Doug Downey, Jason Dunkelberger, Oren Etzioni, Rob Evans, Sergey Feldman, Joseph Gorney, David Graham, Fangzhou Hu, Regan Huff, Daniel King, Sebastian Kohlmeier, Bailey Kuehl, Michael Langan, Daniel Lin, Haokun Liu, Kyle Lo, Jaron Lochner, Kelsey MacMillan, Tyler Murray, Chris Newell, Smita Rao, Shaurya Rohatgi, Paul Sayre, Zejiang Shen, Amanpreet Singh, Luca Soldaini, Shivashankar Subramanian, Amber Tanaka, Alex D. Wade, Linda Wagner, Lucy Lu Wang, Chris Wilhelm, Caroline Wu, Jiangjiang Yang, Angele Zamarron, Madeleine van Zuylen, Daniel S. Weld
The volume of scientific output is creating an urgent need for automated tools to help scientists keep up with developments in their field.
no code implementations • 20 Dec 2022 • John Giorgi, Luca Soldaini, Bo wang, Gary Bader, Kyle Lo, Lucy Lu Wang, Arman Cohan
Via extensive automatic and human evaluation, we determine: (1) state-of-the-art summarizers suffer large reductions in performance when applied to open-domain MDS, (2) additional training in the open-domain setting can reduce this sensitivity to imperfect retrieval, and (3) summarizers are insensitive to the retrieval of duplicate documents and the order of retrieved documents, but highly sensitive to other errors, like the retrieval of irrelevant documents.
Ranked #1 on
Multi-Document Summarization
on MS^2
no code implementations • 23 Oct 2022 • Matteo Gabburo, Rik Koncel-Kedziorski, Siddhant Garg, Luca Soldaini, Alessandro Moschitti
In this paper, we propose to train a GenQA model by transferring knowledge from a trained AS2 model, to overcome the aforementioned issue.
1 code implementation • 11 Jul 2022 • Jon Saad-Falcon, Amanpreet Singh, Luca Soldaini, Mike D'Arcy, Arman Cohan, Doug Downey
Real-world applications of neural language models often involve running many different models over the same corpus.
no code implementations • 20 May 2022 • Luca Di Liello, Siddhant Garg, Luca Soldaini, Alessandro Moschitti
An important task for designing QA systems is answer sentence selection (AS2): selecting the sentence containing (or constituting) the answer to a question from a set of retrieved relevant documents.
Ranked #1 on
Answer Selection
on ASNQ
1 code implementation • NAACL 2022 • Luca Di Liello, Siddhant Garg, Luca Soldaini, Alessandro Moschitti
Our evaluation on three AS2 and one fact verification datasets demonstrates the superiority of our pre-training technique over the traditional ones for transformers used as joint models for multi-candidate inference tasks, as well as when used as cross-encoders for sentence-pair formulations of these tasks.
Ranked #3 on
Fact Verification
on FEVER
1 code implementation • 15 Jan 2022 • Yoshitomo Matsubara, Luca Soldaini, Eric Lind, Alessandro Moschitti
CERBERUS consists of two components: a stack of transformer layers that is used to encode inputs, and a set of ranking heads; unlike traditional distillation technique, each of them is trained by distilling a different large transformer architecture in a way that preserves the diversity of the ensemble members.
no code implementations • 14 Oct 2021 • Benjamin Muller, Luca Soldaini, Rik Koncel-Kedziorski, Eric Lind, Alessandro Moschitti
Our cross-lingual generative system outperforms answer sentence selection baselines for all 5 languages and monolingual generative pipelines for three out of five languages studied.
no code implementations • Findings (ACL) 2021 • Chao-Chun Hsu, Eric Lind, Luca Soldaini, Alessandro Moschitti
Recent advancements in transformer-based models have greatly improved the ability of Question Answering (QA) systems to provide correct answers; in particular, answer sentence selection (AS2) models, core components of retrieval-based systems, have achieved impressive results.
no code implementations • EACL 2021 • Rujun Han, Luca Soldaini, Alessandro Moschitti
In this work, we present an approach to efficiently incorporate contextual information in AS2 models.
Machine Reading Comprehension
Open-Domain Question Answering
+1
no code implementations • COLING 2020 • Mingda Li, Xinyue Liu, Weitong Ruan, Luca Soldaini, Wael Hamza, Chengwei Su
The comparison shows that our model could recover the transcription by integrating the fragmented information among hypotheses and identifying the frequent error patterns of the ASR module, and even rewrite the query for a better understanding, which reveals the characteristic of multi-task learning of broadcasting knowledge.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
1 code implementation • ACL 2020 • Luca Soldaini, Alessandro Moschitti
Large transformer-based language models have been shown to be very effective in many classification tasks.
no code implementations • 30 Jan 2020 • Subendhu Rongali, Luca Soldaini, Emilio Monti, Wael Hamza
Virtual assistants such as Amazon Alexa, Apple Siri, and Google Assistant often rely on a semantic parsing component to understand which action(s) to execute for an utterance spoken by its users.
no code implementations • 11 Jan 2020 • Mingda Li, Weitong Ruan, Xinyue Liu, Luca Soldaini, Wael Hamza, Chengwei Su
The NLU module usually uses the first best interpretation of a given speech in downstream tasks such as domain and intent classification.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
1 code implementation • 30 Dec 2019 • Sean MacAvaney, Luca Soldaini, Nazli Goharian
While billions of non-English speaking users rely on search engines every day, the problem of ad-hoc information retrieval is rarely studied for non-English languages.
no code implementations • WS 2018 • Sean MacAvaney, Bart Desmet, Arman Cohan, Luca Soldaini, Andrew Yates, Ayah Zirikly, Nazli Goharian
Self-reported diagnosis statements have been widely employed in studying language related to mental health in social media.
no code implementations • COLING 2018 • Arman Cohan, Bart Desmet, Andrew Yates, Luca Soldaini, Sean MacAvaney, Nazli Goharian
Mental health is a significant and growing public health concern.
no code implementations • WS 2018 • Luca Soldaini, Timothy Walsh, Arman Cohan, Julien Han, Nazli Goharian
In recent years, online communities have formed around suicide and self-harm prevention.
1 code implementation • SEMEVAL 2018 • Sean MacAvaney, Luca Soldaini, Arman Cohan, Nazli Goharian
SemEval 2018 Task 7 focuses on relation ex- traction and classification in scientific literature.