Search Results for author: Lucas Torroba Hennigen

Found 15 papers, 10 papers with code

Principled Gradient-based Markov Chain Monte Carlo for Text Generation

no code implementations29 Dec 2023 Li Du, Afra Amini, Lucas Torroba Hennigen, Xinyan Velocity Yu, Jason Eisner, Holden Lee, Ryan Cotterell

Recent papers have demonstrated the possibility of energy-based text generation by adapting gradient-based sampling algorithms, a paradigm of MCMC algorithms that promises fast convergence.

Language Modelling Text Generation

Towards Verifiable Text Generation with Symbolic References

no code implementations15 Nov 2023 Lucas Torroba Hennigen, Shannon Shen, Aniruddha Nrusimha, Bernhard Gapp, David Sontag, Yoon Kim

LLMs are vulnerable to hallucinations, and thus their outputs generally require laborious human verification for high-stakes applications.

Question Answering Text Generation

Generalizing Backpropagation for Gradient-Based Interpretability

1 code implementation6 Jul 2023 Kevin Du, Lucas Torroba Hennigen, Niklas Stoehr, Alexander Warstadt, Ryan Cotterell

Many popular feature-attribution methods for interpreting deep neural networks rely on computing the gradients of a model's output with respect to its inputs.

Deriving Language Models from Masked Language Models

1 code implementation24 May 2023 Lucas Torroba Hennigen, Yoon Kim

Masked language models (MLM) do not explicitly define a distribution over language, i. e., they are not language models per se.

Learning to Grow Pretrained Models for Efficient Transformer Training

no code implementations2 Mar 2023 Peihao Wang, Rameswar Panda, Lucas Torroba Hennigen, Philip Greengard, Leonid Karlinsky, Rogerio Feris, David Daniel Cox, Zhangyang Wang, Yoon Kim

Scaling transformers has led to significant breakthroughs in many domains, leading to a paradigm in which larger versions of existing models are trained and released on a periodic basis.

A Measure-Theoretic Characterization of Tight Language Models

no code implementations20 Dec 2022 Li Du, Lucas Torroba Hennigen, Tiago Pimentel, Clara Meister, Jason Eisner, Ryan Cotterell

Language modeling, a central task in natural language processing, involves estimating a probability distribution over strings.

Language Modelling

An Ordinal Latent Variable Model of Conflict Intensity

1 code implementation8 Oct 2022 Niklas Stoehr, Lucas Torroba Hennigen, Josef Valvoda, Robert West, Ryan Cotterell, Aaron Schein

It is based only on the action category ("what") and disregards the subject ("who") and object ("to whom") of an event, as well as contextual information, like associated casualty count, that should contribute to the perception of an event's "intensity".

Event Extraction

UniMorph 4.0: Universal Morphology

no code implementations LREC 2022 Khuyagbaatar Batsuren, Omer Goldman, Salam Khalifa, Nizar Habash, Witold Kieraś, Gábor Bella, Brian Leonard, Garrett Nicolai, Kyle Gorman, Yustinus Ghanggo Ate, Maria Ryskina, Sabrina J. Mielke, Elena Budianskaya, Charbel El-Khaissi, Tiago Pimentel, Michael Gasser, William Lane, Mohit Raj, Matt Coler, Jaime Rafael Montoya Samame, Delio Siticonatzi Camaiteri, Benoît Sagot, Esaú Zumaeta Rojas, Didier López Francis, Arturo Oncevay, Juan López Bautista, Gema Celeste Silva Villegas, Lucas Torroba Hennigen, Adam Ek, David Guriel, Peter Dirix, Jean-Philippe Bernardy, Andrey Scherbakov, Aziyana Bayyr-ool, Antonios Anastasopoulos, Roberto Zariquiey, Karina Sheifer, Sofya Ganieva, Hilaria Cruz, Ritván Karahóǧa, Stella Markantonatou, George Pavlidis, Matvey Plugaryov, Elena Klyachko, Ali Salehi, Candy Angulo, Jatayu Baxi, Andrew Krizhanovsky, Natalia Krizhanovskaya, Elizabeth Salesky, Clara Vania, Sardana Ivanova, Jennifer White, Rowan Hall Maudslay, Josef Valvoda, Ran Zmigrod, Paula Czarnowska, Irene Nikkarinen, Aelita Salchak, Brijesh Bhatt, Christopher Straughn, Zoey Liu, Jonathan North Washington, Yuval Pinter, Duygu Ataman, Marcin Wolinski, Totok Suhardijanto, Anna Yablonskaya, Niklas Stoehr, Hossep Dolatian, Zahroh Nuriah, Shyam Ratan, Francis M. Tyers, Edoardo M. Ponti, Grant Aiton, Aryaman Arora, Richard J. Hatcher, Ritesh Kumar, Jeremiah Young, Daria Rodionova, Anastasia Yemelina, Taras Andrushko, Igor Marchenko, Polina Mashkovtseva, Alexandra Serova, Emily Prud'hommeaux, Maria Nepomniashchaya, Fausto Giunchiglia, Eleanor Chodroff, Mans Hulden, Miikka Silfverberg, Arya D. McCarthy, David Yarowsky, Ryan Cotterell, Reut Tsarfaty, Ekaterina Vylomova

The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation and a type-level resource of annotated data in diverse languages realizing that schema.

Morphological Inflection

Same Neurons, Different Languages: Probing Morphosyntax in Multilingual Pre-trained Models

1 code implementation NAACL 2022 Karolina Stańczak, Edoardo Ponti, Lucas Torroba Hennigen, Ryan Cotterell, Isabelle Augenstein

The success of multilingual pre-trained models is underpinned by their ability to learn representations shared by multiple languages even in absence of any explicit supervision.

A Latent-Variable Model for Intrinsic Probing

2 code implementations20 Jan 2022 Karolina Stańczak, Lucas Torroba Hennigen, Adina Williams, Ryan Cotterell, Isabelle Augenstein

The success of pre-trained contextualized representations has prompted researchers to analyze them for the presence of linguistic information.


Probing as Quantifying Inductive Bias

1 code implementation ACL 2022 Alexander Immer, Lucas Torroba Hennigen, Vincent Fortuin, Ryan Cotterell

Such performance improvements have motivated researchers to quantify and understand the linguistic information encoded in these representations.

Bayesian Inference Inductive Bias

Intrinsic Probing through Dimension Selection

1 code implementation EMNLP 2020 Lucas Torroba Hennigen, Adina Williams, Ryan Cotterell

Most modern NLP systems make use of pre-trained contextual representations that attain astonishingly high performance on a variety of tasks.

Word Embeddings

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