5 code implementations • 9 Jan 2023 • Loubna Ben allal, Raymond Li, Denis Kocetkov, Chenghao Mou, Christopher Akiki, Carlos Munoz Ferrandis, Niklas Muennighoff, Mayank Mishra, Alex Gu, Manan Dey, Logesh Kumar Umapathi, Carolyn Jane Anderson, Yangtian Zi, Joel Lamy Poirier, Hailey Schoelkopf, Sergey Troshin, Dmitry Abulkhanov, Manuel Romero, Michael Lappert, Francesco De Toni, Bernardo García del Río, Qian Liu, Shamik Bose, Urvashi Bhattacharyya, Terry Yue Zhuo, Ian Yu, Paulo Villegas, Marco Zocca, Sourab Mangrulkar, David Lansky, Huu Nguyen, Danish Contractor, Luis Villa, Jia Li, Dzmitry Bahdanau, Yacine Jernite, Sean Hughes, Daniel Fried, Arjun Guha, Harm de Vries, Leandro von Werra
The BigCode project is an open-scientific collaboration working on the responsible development of large language models for code.
4 code implementations • 9 May 2023 • Raymond Li, Loubna Ben allal, Yangtian Zi, Niklas Muennighoff, Denis Kocetkov, Chenghao Mou, Marc Marone, Christopher Akiki, Jia Li, Jenny Chim, Qian Liu, Evgenii Zheltonozhskii, Terry Yue Zhuo, Thomas Wang, Olivier Dehaene, Mishig Davaadorj, Joel Lamy-Poirier, João Monteiro, Oleh Shliazhko, Nicolas Gontier, Nicholas Meade, Armel Zebaze, Ming-Ho Yee, Logesh Kumar Umapathi, Jian Zhu, Benjamin Lipkin, Muhtasham Oblokulov, Zhiruo Wang, Rudra Murthy, Jason Stillerman, Siva Sankalp Patel, Dmitry Abulkhanov, Marco Zocca, Manan Dey, Zhihan Zhang, Nour Fahmy, Urvashi Bhattacharyya, Wenhao Yu, Swayam Singh, Sasha Luccioni, Paulo Villegas, Maxim Kunakov, Fedor Zhdanov, Manuel Romero, Tony Lee, Nadav Timor, Jennifer Ding, Claire Schlesinger, Hailey Schoelkopf, Jan Ebert, Tri Dao, Mayank Mishra, Alex Gu, Jennifer Robinson, Carolyn Jane Anderson, Brendan Dolan-Gavitt, Danish Contractor, Siva Reddy, Daniel Fried, Dzmitry Bahdanau, Yacine Jernite, Carlos Muñoz Ferrandis, Sean Hughes, Thomas Wolf, Arjun Guha, Leandro von Werra, Harm de Vries
The BigCode community, an open-scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder and StarCoderBase: 15. 5B parameter models with 8K context length, infilling capabilities and fast large-batch inference enabled by multi-query attention.
Ranked #43 on Code Generation on MBPP
1 code implementation • Science 2022 • Anton Bakhtin, Noam Brown, Emily Dinan, Gabriele Farina, Colin Flaherty, Daniel Fried, Andrew Goff, Jonathan Gray, Hengyan Hu, Athul Paul Jacob, Mojtaba Komeili, Karthik Konath, Minae Kwon, Adam Lerer, Mike Lewis, Alexander H. Miller, Sash Mitts, Aditya Renduchintala, Stephen Roller, Dirk Rowe, Weiyan Shi, Joe Spisak, Alexander Wei, David Wu, Hugh Zhang, Markus Zijlstra
Despite much progress in training AI systems to imitate human language, building agents that use language to communicate intentionally with humans in interactive environments remains a major challenge.
1 code implementation • 25 Jul 2023 • Shuyan Zhou, Frank F. Xu, Hao Zhu, Xuhui Zhou, Robert Lo, Abishek Sridhar, Xianyi Cheng, Tianyue Ou, Yonatan Bisk, Daniel Fried, Uri Alon, Graham Neubig
Building upon our environment, we release a set of benchmark tasks focusing on evaluating the functional correctness of task completions.
1 code implementation • 31 Jan 2023 • Jing Yu Koh, Ruslan Salakhutdinov, Daniel Fried
We propose an efficient method to ground pretrained text-only language models to the visual domain, enabling them to process arbitrarily interleaved image-and-text data, and generate text interleaved with retrieved images.
1 code implementation • NeurIPS 2023 • Jing Yu Koh, Daniel Fried, Ruslan Salakhutdinov
This mapping network translates hidden representations of text into the embedding space of the visual models, enabling us to leverage the strong text representations of the LLM for visual outputs.
3 code implementations • 12 Apr 2022 • Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Wen-tau Yih, Luke Zettlemoyer, Mike Lewis
Our model is the first generative model that is able to directly perform zero-shot code infilling, which we evaluate on challenging tasks such as type inference, comment generation, and variable re-naming.
Ranked #85 on Code Generation on MBPP
1 code implementation • 18 Nov 2022 • Yuhang Lai, Chengxi Li, Yiming Wang, Tianyi Zhang, Ruiqi Zhong, Luke Zettlemoyer, Scott Wen-tau Yih, Daniel Fried, Sida Wang, Tao Yu
We introduce DS-1000, a code generation benchmark with a thousand data science problems spanning seven Python libraries, such as NumPy and Pandas.
2 code implementations • 27 Oct 2022 • Xiang Lisa Li, Ari Holtzman, Daniel Fried, Percy Liang, Jason Eisner, Tatsunori Hashimoto, Luke Zettlemoyer, Mike Lewis
We propose contrastive decoding (CD), a reliable decoding approach that optimizes a contrastive objective subject to a plausibility constraint.
1 code implementation • 24 Jan 2024 • Jing Yu Koh, Robert Lo, Lawrence Jang, Vikram Duvvur, Ming Chong Lim, Po-Yu Huang, Graham Neubig, Shuyan Zhou, Ruslan Salakhutdinov, Daniel Fried
Through extensive quantitative and qualitative analysis, we identify several limitations of text-only LLM agents, and reveal gaps in the capabilities of state-of-the-art multimodal language agents.
1 code implementation • NeurIPS 2018 • Daniel Fried, Ronghang Hu, Volkan Cirik, Anna Rohrbach, Jacob Andreas, Louis-Philippe Morency, Taylor Berg-Kirkpatrick, Kate Saenko, Dan Klein, Trevor Darrell
We use this speaker model to (1) synthesize new instructions for data augmentation and to (2) implement pragmatic reasoning, which evaluates how well candidate action sequences explain an instruction.
1 code implementation • 23 Feb 2024 • Jacob Mitchell Springer, Suhas Kotha, Daniel Fried, Graham Neubig, aditi raghunathan
In this work, we address an architectural limitation of autoregressive models: token embeddings cannot contain information from tokens that appear later in the input.
2 code implementations • NAACL 2019 • Sheng Shen, Daniel Fried, Jacob Andreas, Dan Klein
We improve the informativeness of models for conditional text generation using techniques from computational pragmatics.
Ranked #1 on Data-to-Text Generation on E2E NLG Challenge
Abstractive Text Summarization Conditional Text Generation +3
1 code implementation • 18 Oct 2023 • Xuhui Zhou, Hao Zhu, Leena Mathur, Ruohong Zhang, Haofei Yu, Zhengyang Qi, Louis-Philippe Morency, Yonatan Bisk, Daniel Fried, Graham Neubig, Maarten Sap
We present SOTOPIA, an open-ended environment to simulate complex social interactions between artificial agents and evaluate their social intelligence.
1 code implementation • 29 Nov 2022 • Tianyi Zhang, Tao Yu, Tatsunori B. Hashimoto, Mike Lewis, Wen-tau Yih, Daniel Fried, Sida I. Wang
Sampling diverse programs from a code language model and reranking with model likelihood is a popular method for code generation but it is prone to preferring degenerate solutions.
Ranked #23 on Code Generation on MBPP
1 code implementation • 20 Dec 2022 • Zhiruo Wang, Shuyan Zhou, Daniel Fried, Graham Neubig
To extend the scope of coding queries to more realistic settings, we propose ODEX, the first Open-Domain EXecution-based natural language (NL) to Python code generation dataset.
1 code implementation • 25 Apr 2022 • Freda Shi, Daniel Fried, Marjan Ghazvininejad, Luke Zettlemoyer, Sida I. Wang
In this work, we introduce execution result--based minimum Bayes risk decoding (MBR-EXEC) for program selection and show that it improves the few-shot performance of pretrained code models on natural-language-to-code tasks.
Ranked #37 on Code Generation on MBPP
1 code implementation • ACL 2019 • Daniel Fried, Nikita Kitaev, Dan Klein
Neural parsers obtain state-of-the-art results on benchmark treebanks for constituency parsing -- but to what degree do they generalize to other domains?
1 code implementation • ACL 2020 • Daniel Fried, Jean-Baptiste Alayrac, Phil Blunsom, Chris Dyer, Stephen Clark, Aida Nematzadeh
We apply a generative segmental model of task structure, guided by narration, to action segmentation in video.
1 code implementation • ACL 2022 • Jessy Lin, Daniel Fried, Dan Klein, Anca Dragan
In classic instruction following, language like "I'd like the JetBlue flight" maps to actions (e. g., selecting that flight).
1 code implementation • 23 Jan 2024 • Zhiruo Wang, Daniel Fried, Graham Neubig
Language models (LMs) can solve tasks such as answering questions about tables or images by writing programs.
1 code implementation • NAACL 2018 • Daniel Fried, Jacob Andreas, Dan Klein
We show that explicit pragmatic inference aids in correctly generating and following natural language instructions for complex, sequential tasks.
1 code implementation • 28 Nov 2022 • Grace Luo, Giscard Biamby, Trevor Darrell, Daniel Fried, Anna Rohrbach
We propose the task of Geolocation via Guidebook Grounding that uses a dataset of StreetView images from a diverse set of locations and an associated textual guidebook for GeoGuessr, a popular interactive geolocation game.
1 code implementation • EMNLP 2021 • Daniel Fried, Justin T. Chiu, Dan Klein
We present a grounded neural dialogue model that successfully collaborates with people in a partially-observable reference game.
1 code implementation • 26 Oct 2023 • Justin T. Chiu, Wenting Zhao, Derek Chen, Saujas Vaduguru, Alexander M. Rush, Daniel Fried
Large language models (LLMs) excel at processing and generating both text and code.
1 code implementation • 15 Jun 2023 • Jiefu Ou, Benno Krojer, Daniel Fried
We propose a simple yet effective and robust method for contrastive captioning: generating discriminative captions that distinguish target images from very similar alternative distractor images.
1 code implementation • 1 Nov 2023 • Yiqing Xie, Atharva Naik, Daniel Fried, Carolyn Rose
One major challenge of translating code between programming languages is that parallel training data is often limited.
1 code implementation • 3 Nov 2023 • Alex Wilf, Alex Tianyi Xu, Paul Pu Liang, Alexander Obolenskiy, Daniel Fried, Louis-Philippe Morency
We observe that prevalent KD techniques and state of the art data augmentation strategies fall short in this constrained setting.
no code implementations • ACL 2018 • Daniel Fried, Dan Klein
Dynamic oracles provide strong supervision for training constituency parsers with exploration, but must be custom defined for a given parser's transition system.
no code implementations • EMNLP 2017 • Mitchell Stern, Daniel Fried, Dan Klein
Generative neural models have recently achieved state-of-the-art results for constituency parsing.
no code implementations • ACL 2017 • Daniel Fried, Mitchell Stern, Dan Klein
Recent work has proposed several generative neural models for constituency parsing that achieve state-of-the-art results.
Ranked #14 on Constituency Parsing on Penn Treebank
no code implementations • LREC 2016 • Dane Bell, Daniel Fried, Luwen Huangfu, Mihai Surdeanu, Stephen Kobourov
The strategy uses a game-like quiz with data and questions acquired semi-automatically from Twitter.
no code implementations • 18 Dec 2014 • Daniel Fried, Kevin Duh
We investigate the hypothesis that word representations ought to incorporate both distributional and relational semantics.
no code implementations • 14 Dec 2014 • Daniel Fried, Kevin Duh
We investigate the hypothesis that word representations ought to incorporate both distributional and relational semantics.
no code implementations • 8 Sep 2014 • Daniel Fried, Mihai Surdeanu, Stephen Kobourov, Melanie Hingle, Dane Bell
We investigate the predictive power behind the language of food on social media.
no code implementations • TACL 2015 • Daniel Fried, Peter Jansen, Gustave Hahn-Powell, Mihai Surdeanu, Peter Clark
We introduce a higher-order formalism that allows all these lexical semantic models to chain direct evidence to construct indirect associations between question and answer texts, by casting the task as the traversal of graphs that encode direct term associations.
no code implementations • ACL 2019 • Ronghang Hu, Daniel Fried, Anna Rohrbach, Dan Klein, Trevor Darrell, Kate Saenko
The actual grounding can connect language to the environment through multiple modalities, e. g. "stop at the door" might ground into visual objects, while "turn right" might rely only on the geometric structure of a route.
no code implementations • 27 May 2020 • Adhiguna Kuncoro, Lingpeng Kong, Daniel Fried, Dani Yogatama, Laura Rimell, Chris Dyer, Phil Blunsom
Textual representation learners trained on large amounts of data have achieved notable success on downstream tasks; intriguingly, they have also performed well on challenging tests of syntactic competence.
no code implementations • NAACL 2021 • Rodolfo Corona, Daniel Fried, Coline Devin, Dan Klein, Trevor Darrell
In our approach, subgoal modules each carry out natural language instructions for a specific subgoal type.
no code implementations • NAACL (TeachingNLP) 2021 • David Gaddy, Daniel Fried, Nikita Kitaev, Mitchell Stern, Rodolfo Corona, John DeNero, Dan Klein
We present a set of assignments for a graduate-level NLP course.
no code implementations • 24 Oct 2022 • Maarten Sap, Ronan LeBras, Daniel Fried, Yejin Choi
We show that one of today's largest language models (GPT-3; Brown et al., 2020) lacks this kind of social intelligence out-of-the box, using two tasks: SocialIQa (Sap et al., 2019), which measures models' ability to understand intents and reactions of participants of social interactions, and ToMi (Le et al., 2019), which measures whether models can infer mental states and realities of participants of situations.
no code implementations • 15 Nov 2022 • Daniel Fried, Nicholas Tomlin, Jennifer Hu, Roma Patel, Aida Nematzadeh
People rely heavily on context to enrich meaning beyond what is literally said, enabling concise but effective communication.
no code implementations • 22 Nov 2022 • Weiyan Shi, Emily Dinan, Adi Renduchintala, Daniel Fried, Athul Paul Jacob, Zhou Yu, Mike Lewis
Existing approaches built separate classifiers to detect nonsense in dialogues.
1 code implementation • 1 Sep 2023 • Yewen Pu, Saujas Vaduguru, Priyan Vaithilingam, Elena Glassman, Daniel Fried
We prove that for a pragmatic synthesizer that uses a single demonstration, our global ranking method exactly replicates RSA's ranked responses.
no code implementations • 23 Oct 2023 • Yihan Cao, Shuyi Chen, Ryan Liu, Zhiruo Wang, Daniel Fried
A persistent challenge to table question answering (TableQA) by generating executable programs has been adapting to varied table structures, typically requiring domain-specific logical forms.
1 code implementation • 9 Nov 2023 • Saujas Vaduguru, Daniel Fried, Yewen Pu
Programming-by-example is the task of synthesizing a program that is consistent with a set of user-provided input-output examples.
no code implementations • 14 Nov 2023 • Sedrick Keh, Justin T. Chiu, Daniel Fried
When a model is trying to gather information in an interactive setting, it benefits from asking informative questions.
no code implementations • 18 Mar 2024 • Zhiruo Wang, Zhoujun Cheng, Hao Zhu, Daniel Fried, Graham Neubig
Language models (LMs) are powerful yet mostly for text generation tasks.
no code implementations • 1 Apr 2024 • Casey Kennington, Malihe Alikhani, Heather Pon-Barry, Katherine Atwell, Yonatan Bisk, Daniel Fried, Felix Gervits, Zhao Han, Mert Inan, Michael Johnston, Raj Korpan, Diane Litman, Matthew Marge, Cynthia Matuszek, Ross Mead, Shiwali Mohan, Raymond Mooney, Natalie Parde, Jivko Sinapov, Angela Stewart, Matthew Stone, Stefanie Tellex, Tom Williams
The ability to interact with machines using natural human language is becoming not just commonplace, but expected.
no code implementations • 31 Mar 2024 • Yiqing Xie, Alex Xie, Divyanshu Sheth, PengFei Liu, Daniel Fried, Carolyn Rose
We will release the code of both the framework and the dataset upon acceptance.