Search Results for author: Mor Geva

Found 42 papers, 28 papers with code

What’s in Your Head? Emergent Behaviour in Multi-Task Transformer Models

no code implementations EMNLP 2021 Mor Geva, Uri Katz, Aviv Ben-Arie, Jonathan Berant

In this work, we examine the behaviour of non-target heads, that is, the output of heads when given input that belongs to a different task than the one they were trained for.

Language Modelling Question Answering

From Loops to Oops: Fallback Behaviors of Language Models Under Uncertainty

1 code implementation8 Jul 2024 Maor Ivgi, Ori Yoran, Jonathan Berant, Mor Geva

Our experiments reveal a clear and consistent ordering of fallback behaviors, across all these axes: the more advanced an LLM is (i. e., trained on more tokens, has more parameters, or instruction-tuned), its fallback behavior shifts from sequence repetitions, to degenerate text, and then to hallucinations.

Instruction Following

Hopping Too Late: Exploring the Limitations of Large Language Models on Multi-Hop Queries

1 code implementation18 Jun 2024 Eden Biran, Daniela Gottesman, Sohee Yang, Mor Geva, Amir Globerson

Motivated by this, we study how LLMs answer multi-hop queries such as "The spouse of the performer of Imagine is".

From Insights to Actions: The Impact of Interpretability and Analysis Research on NLP

no code implementations18 Jun 2024 Marius Mosbach, Vagrant Gautam, Tomás Vergara-Browne, Dietrich Klakow, Mor Geva

Interpretability and analysis (IA) research is a growing subfield within NLP with the goal of developing a deeper understanding of the behavior or inner workings of NLP systems and methods.

Estimating Knowledge in Large Language Models Without Generating a Single Token

no code implementations18 Jun 2024 Daniela Gottesman, Mor Geva

We study this question with two tasks: given a subject entity, the goal is to predict (a) the ability of the model to answer common questions about the entity, and (b) the factuality of responses generated by the model about the entity.

Intrinsic Evaluation of Unlearning Using Parametric Knowledge Traces

1 code implementation17 Jun 2024 Yihuai Hong, Lei Yu, Shauli Ravfogel, Haiqin Yang, Mor Geva

To this end, we propose a general methodology for eliciting directions in the parameter space (termed "concept vectors") that encode concrete concepts, and construct ConceptVectors, a benchmark dataset containing hundreds of common concepts and their parametric knowledge traces within two open-source LLMs.

Can Large Language Models Faithfully Express Their Intrinsic Uncertainty in Words?

no code implementations27 May 2024 Gal Yona, Roee Aharoni, Mor Geva

We posit that large language models (LLMs) should be capable of expressing their intrinsic uncertainty in natural language.

Question Answering

Do Large Language Models Latently Perform Multi-Hop Reasoning?

no code implementations26 Feb 2024 Sohee Yang, Elena Gribovskaya, Nora Kassner, Mor Geva, Sebastian Riedel

We find strong evidence of latent multi-hop reasoning for the prompts of certain relation types, with the reasoning pathway used in more than 80% of the prompts.

Backward Lens: Projecting Language Model Gradients into the Vocabulary Space

no code implementations20 Feb 2024 Shahar Katz, Yonatan Belinkov, Mor Geva, Lior Wolf

Understanding how Transformer-based Language Models (LMs) learn and recall information is a key goal of the deep learning community.

Language Modelling

The Hidden Space of Transformer Language Adapters

no code implementations20 Feb 2024 Jesujoba O. Alabi, Marius Mosbach, Matan Eyal, Dietrich Klakow, Mor Geva

We analyze the operation of transformer language adapters, which are small modules trained on top of a frozen language model to adapt its predictions to new target languages.

Language Modelling

A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for Verifiers of Reasoning Chains

no code implementations1 Feb 2024 Alon Jacovi, Yonatan Bitton, Bernd Bohnet, Jonathan Herzig, Or Honovich, Michael Tseng, Michael Collins, Roee Aharoni, Mor Geva

REVEAL includes comprehensive labels for the relevance, attribution to evidence passages, and logical correctness of each reasoning step in a language model's answer, across a variety of datasets and state-of-the-art language models.

Open-Domain Question Answering

Patchscopes: A Unifying Framework for Inspecting Hidden Representations of Language Models

1 code implementation11 Jan 2024 Asma Ghandeharioun, Avi Caciularu, Adam Pearce, Lucas Dixon, Mor Geva

We introduce a framework called Patchscopes and show how it can be used to answer a wide range of questions about an LLM's computation.

Narrowing the Knowledge Evaluation Gap: Open-Domain Question Answering with Multi-Granularity Answers

no code implementations9 Jan 2024 Gal Yona, Roee Aharoni, Mor Geva

In this work, we propose GRANOLA QA, a novel evaluation setting where a predicted answer is evaluated in terms of accuracy and informativeness against a set of multi-granularity answers.

Informativeness Open-Domain Question Answering

In-Context Learning Creates Task Vectors

1 code implementation24 Oct 2023 Roee Hendel, Mor Geva, Amir Globerson

In-context learning (ICL) in Large Language Models (LLMs) has emerged as a powerful new learning paradigm.

In-Context Learning

CRoW: Benchmarking Commonsense Reasoning in Real-World Tasks

1 code implementation23 Oct 2023 Mete Ismayilzada, Debjit Paul, Syrielle Montariol, Mor Geva, Antoine Bosselut

Recent efforts in natural language processing (NLP) commonsense reasoning research have yielded a considerable number of new datasets and benchmarks.


A Comprehensive Evaluation of Tool-Assisted Generation Strategies

no code implementations16 Oct 2023 Alon Jacovi, Avi Caciularu, Jonathan Herzig, Roee Aharoni, Bernd Bohnet, Mor Geva

A growing area of research investigates augmenting language models with tools (e. g., search engines, calculators) to overcome their shortcomings (e. g., missing or incorrect knowledge, incorrect logical inferences).


Evaluating the Ripple Effects of Knowledge Editing in Language Models

1 code implementation24 Jul 2023 Roi Cohen, Eden Biran, Ori Yoran, Amir Globerson, Mor Geva

This has led to the development of various editing methods that allow updating facts encoded by the model.

knowledge editing

The Hidden Language of Diffusion Models

1 code implementation1 Jun 2023 Hila Chefer, Oran Lang, Mor Geva, Volodymyr Polosukhin, Assaf Shocher, Michal Irani, Inbar Mosseri, Lior Wolf

In this work, we present Conceptor, a novel method to interpret the internal representation of a textual concept by a diffusion model.

Bias Detection Image Manipulation

LM vs LM: Detecting Factual Errors via Cross Examination

no code implementations22 May 2023 Roi Cohen, May Hamri, Mor Geva, Amir Globerson

A prominent weakness of modern language models (LMs) is their tendency to generate factually incorrect text, which hinders their usability.

Dissecting Recall of Factual Associations in Auto-Regressive Language Models

1 code implementation28 Apr 2023 Mor Geva, Jasmijn Bastings, Katja Filippova, Amir Globerson

Given a subject-relation query, we study how the model aggregates information about the subject and relation to predict the correct attribute.

Attribute Attribute Extraction +1

Jump to Conclusions: Short-Cutting Transformers With Linear Transformations

2 code implementations16 Mar 2023 Alexander Yom Din, Taelin Karidi, Leshem Choshen, Mor Geva

This approximation far exceeds the prevailing practice of inspecting hidden representations from all layers, in the space of the final layer.

Decision Making Language Modelling

Crawling the Internal Knowledge-Base of Language Models

no code implementations30 Jan 2023 Roi Cohen, Mor Geva, Jonathan Berant, Amir Globerson

Here, we propose to address this goal by extracting a knowledge-graph of facts from a given language model.

Language Modelling

Understanding Transformer Memorization Recall Through Idioms

1 code implementation7 Oct 2022 Adi Haviv, Ido Cohen, Jacob Gidron, Roei Schuster, Yoav Goldberg, Mor Geva

In this work, we offer the first methodological framework for probing and characterizing recall of memorized sequences in transformer LMs.


Analyzing Transformers in Embedding Space

1 code implementation6 Sep 2022 Guy Dar, Mor Geva, Ankit Gupta, Jonathan Berant

In this work, we present a theoretical analysis where all parameters of a trained Transformer are interpreted by projecting them into the embedding space, that is, the space of vocabulary items they operate on.

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

4 code implementations9 Jun 2022 Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R. Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, Agnieszka Kluska, Aitor Lewkowycz, Akshat Agarwal, Alethea Power, Alex Ray, Alex Warstadt, Alexander W. Kocurek, Ali Safaya, Ali Tazarv, Alice Xiang, Alicia Parrish, Allen Nie, Aman Hussain, Amanda Askell, Amanda Dsouza, Ambrose Slone, Ameet Rahane, Anantharaman S. Iyer, Anders Andreassen, Andrea Madotto, Andrea Santilli, Andreas Stuhlmüller, Andrew Dai, Andrew La, Andrew Lampinen, Andy Zou, Angela Jiang, Angelica Chen, Anh Vuong, Animesh Gupta, Anna Gottardi, Antonio Norelli, Anu Venkatesh, Arash Gholamidavoodi, Arfa Tabassum, Arul Menezes, Arun Kirubarajan, Asher Mullokandov, Ashish Sabharwal, Austin Herrick, Avia Efrat, Aykut Erdem, Ayla Karakaş, B. Ryan Roberts, Bao Sheng Loe, Barret Zoph, Bartłomiej Bojanowski, Batuhan Özyurt, Behnam Hedayatnia, Behnam Neyshabur, Benjamin Inden, Benno Stein, Berk Ekmekci, Bill Yuchen Lin, Blake Howald, Bryan Orinion, Cameron Diao, Cameron Dour, Catherine Stinson, Cedrick Argueta, César Ferri Ramírez, Chandan Singh, Charles Rathkopf, Chenlin Meng, Chitta Baral, Chiyu Wu, Chris Callison-Burch, Chris Waites, Christian Voigt, Christopher D. Manning, Christopher Potts, Cindy Ramirez, Clara E. Rivera, Clemencia Siro, Colin Raffel, Courtney Ashcraft, Cristina Garbacea, Damien Sileo, Dan Garrette, Dan Hendrycks, Dan Kilman, Dan Roth, Daniel Freeman, Daniel Khashabi, Daniel Levy, Daniel Moseguí González, Danielle Perszyk, Danny Hernandez, Danqi Chen, Daphne Ippolito, Dar Gilboa, David Dohan, David Drakard, David Jurgens, Debajyoti Datta, Deep Ganguli, Denis Emelin, Denis Kleyko, Deniz Yuret, Derek Chen, Derek Tam, Dieuwke Hupkes, Diganta Misra, Dilyar Buzan, Dimitri Coelho Mollo, Diyi Yang, Dong-Ho Lee, Dylan Schrader, Ekaterina Shutova, Ekin Dogus Cubuk, Elad Segal, Eleanor Hagerman, Elizabeth Barnes, Elizabeth Donoway, Ellie Pavlick, Emanuele Rodola, Emma Lam, Eric Chu, Eric Tang, Erkut Erdem, Ernie Chang, Ethan A. Chi, Ethan Dyer, Ethan Jerzak, Ethan Kim, Eunice Engefu Manyasi, Evgenii Zheltonozhskii, Fanyue Xia, Fatemeh Siar, Fernando Martínez-Plumed, Francesca Happé, Francois Chollet, Frieda Rong, Gaurav Mishra, Genta Indra Winata, Gerard de Melo, Germán Kruszewski, Giambattista Parascandolo, Giorgio Mariani, Gloria Wang, Gonzalo Jaimovitch-López, Gregor Betz, Guy Gur-Ari, Hana Galijasevic, Hannah Kim, Hannah Rashkin, Hannaneh Hajishirzi, Harsh Mehta, Hayden Bogar, Henry Shevlin, Hinrich Schütze, Hiromu Yakura, Hongming Zhang, Hugh Mee Wong, Ian Ng, Isaac Noble, Jaap Jumelet, Jack Geissinger, Jackson Kernion, Jacob Hilton, Jaehoon Lee, Jaime Fernández Fisac, James B. Simon, James Koppel, James Zheng, James Zou, Jan Kocoń, Jana Thompson, Janelle Wingfield, Jared Kaplan, Jarema Radom, Jascha Sohl-Dickstein, Jason Phang, Jason Wei, Jason Yosinski, Jekaterina Novikova, Jelle Bosscher, Jennifer Marsh, Jeremy Kim, Jeroen Taal, Jesse Engel, Jesujoba Alabi, Jiacheng Xu, Jiaming Song, Jillian Tang, Joan Waweru, John Burden, John Miller, John U. Balis, Jonathan Batchelder, Jonathan Berant, Jörg Frohberg, Jos Rozen, Jose Hernandez-Orallo, Joseph Boudeman, Joseph Guerr, Joseph Jones, Joshua B. Tenenbaum, Joshua S. Rule, Joyce Chua, Kamil Kanclerz, Karen Livescu, Karl Krauth, Karthik Gopalakrishnan, Katerina Ignatyeva, Katja Markert, Kaustubh D. Dhole, Kevin Gimpel, Kevin Omondi, Kory Mathewson, Kristen Chiafullo, Ksenia Shkaruta, Kumar Shridhar, Kyle McDonell, Kyle Richardson, Laria Reynolds, Leo Gao, Li Zhang, Liam Dugan, Lianhui Qin, Lidia Contreras-Ochando, Louis-Philippe Morency, Luca Moschella, Lucas Lam, Lucy Noble, Ludwig Schmidt, Luheng He, Luis Oliveros Colón, Luke Metz, Lütfi Kerem Şenel, Maarten Bosma, Maarten Sap, Maartje ter Hoeve, Maheen Farooqi, Manaal Faruqui, Mantas Mazeika, Marco Baturan, Marco Marelli, Marco Maru, Maria Jose Ramírez Quintana, Marie Tolkiehn, Mario Giulianelli, Martha Lewis, Martin Potthast, Matthew L. Leavitt, Matthias Hagen, Mátyás Schubert, Medina Orduna Baitemirova, Melody Arnaud, Melvin McElrath, Michael A. Yee, Michael Cohen, Michael Gu, Michael Ivanitskiy, Michael Starritt, Michael Strube, Michał Swędrowski, Michele Bevilacqua, Michihiro Yasunaga, Mihir Kale, Mike Cain, Mimee Xu, Mirac Suzgun, Mitch Walker, Mo Tiwari, Mohit Bansal, Moin Aminnaseri, Mor Geva, Mozhdeh Gheini, Mukund Varma T, Nanyun Peng, Nathan A. Chi, Nayeon Lee, Neta Gur-Ari Krakover, Nicholas Cameron, Nicholas Roberts, Nick Doiron, Nicole Martinez, Nikita Nangia, Niklas Deckers, Niklas Muennighoff, Nitish Shirish Keskar, Niveditha S. Iyer, Noah Constant, Noah Fiedel, Nuan Wen, Oliver Zhang, Omar Agha, Omar Elbaghdadi, Omer Levy, Owain Evans, Pablo Antonio Moreno Casares, Parth Doshi, Pascale Fung, Paul Pu Liang, Paul Vicol, Pegah Alipoormolabashi, Peiyuan Liao, Percy Liang, Peter Chang, Peter Eckersley, Phu Mon Htut, Pinyu Hwang, Piotr Miłkowski, Piyush Patil, Pouya Pezeshkpour, Priti Oli, Qiaozhu Mei, Qing Lyu, Qinlang Chen, Rabin Banjade, Rachel Etta Rudolph, Raefer Gabriel, Rahel Habacker, Ramon Risco, Raphaël Millière, Rhythm Garg, Richard Barnes, Rif A. Saurous, Riku Arakawa, Robbe Raymaekers, Robert Frank, Rohan Sikand, Roman Novak, Roman Sitelew, Ronan LeBras, Rosanne Liu, Rowan Jacobs, Rui Zhang, Ruslan Salakhutdinov, Ryan Chi, Ryan Lee, Ryan Stovall, Ryan Teehan, Rylan Yang, Sahib Singh, Saif M. Mohammad, Sajant Anand, Sam Dillavou, Sam Shleifer, Sam Wiseman, Samuel Gruetter, Samuel R. Bowman, Samuel S. Schoenholz, Sanghyun Han, Sanjeev Kwatra, Sarah A. Rous, Sarik Ghazarian, Sayan Ghosh, Sean Casey, Sebastian Bischoff, Sebastian Gehrmann, Sebastian Schuster, Sepideh Sadeghi, Shadi Hamdan, Sharon Zhou, Shashank Srivastava, Sherry Shi, Shikhar Singh, Shima Asaadi, Shixiang Shane Gu, Shubh Pachchigar, Shubham Toshniwal, Shyam Upadhyay, Shyamolima, Debnath, Siamak Shakeri, Simon Thormeyer, Simone Melzi, Siva Reddy, Sneha Priscilla Makini, Soo-Hwan Lee, Spencer Torene, Sriharsha Hatwar, Stanislas Dehaene, Stefan Divic, Stefano Ermon, Stella Biderman, Stephanie Lin, Stephen Prasad, Steven T. Piantadosi, Stuart M. Shieber, Summer Misherghi, Svetlana Kiritchenko, Swaroop Mishra, Tal Linzen, Tal Schuster, Tao Li, Tao Yu, Tariq Ali, Tatsu Hashimoto, Te-Lin Wu, Théo Desbordes, Theodore Rothschild, Thomas Phan, Tianle Wang, Tiberius Nkinyili, Timo Schick, Timofei Kornev, Titus Tunduny, Tobias Gerstenberg, Trenton Chang, Trishala Neeraj, Tushar Khot, Tyler Shultz, Uri Shaham, Vedant Misra, Vera Demberg, Victoria Nyamai, Vikas Raunak, Vinay Ramasesh, Vinay Uday Prabhu, Vishakh Padmakumar, Vivek Srikumar, William Fedus, William Saunders, William Zhang, Wout Vossen, Xiang Ren, Xiaoyu Tong, Xinran Zhao, Xinyi Wu, Xudong Shen, Yadollah Yaghoobzadeh, Yair Lakretz, Yangqiu Song, Yasaman Bahri, Yejin Choi, Yichi Yang, Yiding Hao, Yifu Chen, Yonatan Belinkov, Yu Hou, Yufang Hou, Yuntao Bai, Zachary Seid, Zhuoye Zhao, Zijian Wang, Zijie J. Wang, ZiRui Wang, Ziyi Wu

BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.

Common Sense Reasoning Math +1

Don't Blame the Annotator: Bias Already Starts in the Annotation Instructions

no code implementations1 May 2022 Mihir Parmar, Swaroop Mishra, Mor Geva, Chitta Baral

In this work, we hypothesize that annotators pick up on patterns in the crowdsourcing instructions, which bias them to write many similar examples that are then over-represented in the collected data.

Inferring Implicit Relations in Complex Questions with Language Models

1 code implementation28 Apr 2022 Uri Katz, Mor Geva, Jonathan Berant

A prominent challenge for modern language understanding systems is the ability to answer implicit reasoning questions, where the required reasoning steps for answering the question are not mentioned in the text explicitly.

Implicit Relations Question Answering +1

LM-Debugger: An Interactive Tool for Inspection and Intervention in Transformer-Based Language Models

1 code implementation26 Apr 2022 Mor Geva, Avi Caciularu, Guy Dar, Paul Roit, Shoval Sadde, Micah Shlain, Bar Tamir, Yoav Goldberg

The opaque nature and unexplained behavior of transformer-based language models (LMs) have spurred a wide interest in interpreting their predictions.

Transformer Feed-Forward Layers Build Predictions by Promoting Concepts in the Vocabulary Space

1 code implementation28 Mar 2022 Mor Geva, Avi Caciularu, Kevin Ro Wang, Yoav Goldberg

Transformer-based language models (LMs) are at the core of modern NLP, but their internal prediction construction process is opaque and largely not understood.

SCROLLS: Standardized CompaRison Over Long Language Sequences

2 code implementations10 Jan 2022 Uri Shaham, Elad Segal, Maor Ivgi, Avia Efrat, Ori Yoran, Adi Haviv, Ankit Gupta, Wenhan Xiong, Mor Geva, Jonathan Berant, Omer Levy

NLP benchmarks have largely focused on short texts, such as sentences and paragraphs, even though long texts comprise a considerable amount of natural language in the wild.

Decoder Long-range modeling +2

Break, Perturb, Build: Automatic Perturbation of Reasoning Paths Through Question Decomposition

1 code implementation29 Jul 2021 Mor Geva, Tomer Wolfson, Jonathan Berant

We evaluate a range of RC models on our evaluation sets, which reveals large performance gaps on generated examples compared to the original data.

Natural Language Understanding Reading Comprehension

What's in your Head? Emergent Behaviour in Multi-Task Transformer Models

no code implementations13 Apr 2021 Mor Geva, Uri Katz, Aviv Ben-Arie, Jonathan Berant

In this work, we examine the behaviour of non-target heads, that is, the output of heads when given input that belongs to a different task than the one they were trained for.

Language Modelling Question Answering

Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies

1 code implementation6 Jan 2021 Mor Geva, Daniel Khashabi, Elad Segal, Tushar Khot, Dan Roth, Jonathan Berant

A key limitation in current datasets for multi-hop reasoning is that the required steps for answering the question are mentioned in it explicitly.

Question Answering StrategyQA

Transformer Feed-Forward Layers Are Key-Value Memories

1 code implementation EMNLP 2021 Mor Geva, Roei Schuster, Jonathan Berant, Omer Levy

Feed-forward layers constitute two-thirds of a transformer model's parameters, yet their role in the network remains under-explored.

Injecting Numerical Reasoning Skills into Language Models

2 code implementations ACL 2020 Mor Geva, Ankit Gupta, Jonathan Berant

In this work, we show that numerical reasoning is amenable to automatic data generation, and thus one can inject this skill into pre-trained LMs, by generating large amounts of data, and training in a multi-task setup.

Data Augmentation Decoder +3

DiscoFuse: A Large-Scale Dataset for Discourse-Based Sentence Fusion

2 code implementations NAACL 2019 Mor Geva, Eric Malmi, Idan Szpektor, Jonathan Berant

We author a set of rules for identifying a diverse set of discourse phenomena in raw text, and decomposing the text into two independent sentences.

Sentence Sentence Fusion +2

Emergence of Communication in an Interactive World with Consistent Speakers

1 code implementation3 Sep 2018 Ben Bogin, Mor Geva, Jonathan Berant

Training agents to communicate with one another given task-based supervision only has attracted considerable attention recently, due to the growing interest in developing models for human-agent interaction.

Learning to Search in Long Documents Using Document Structure

1 code implementation COLING 2018 Mor Geva, Jonathan Berant

Reading comprehension models are based on recurrent neural networks that sequentially process the document tokens.

Information Retrieval Question Answering +2

Evaluating Semantic Parsing against a Simple Web-based Question Answering Model

1 code implementation SEMEVAL 2017 Alon Talmor, Mor Geva, Jonathan Berant

Semantic parsing shines at analyzing complex natural language that involves composition and computation over multiple pieces of evidence.

Question Answering Semantic Parsing

Cannot find the paper you are looking for? You can Submit a new open access paper.