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no code implementations • 13 Nov 2023 • Skyler Hallinan, Faeze Brahman, Ximing Lu, JaeHun Jung, Sean Welleck, Yejin Choi

We propose STEER: Unified Style Transfer with Expert Reinforcement, a unified frame-work developed to overcome the challenge of limited parallel data for style transfer.

1 code implementation • 27 Oct 2023 • Sean Welleck, Rahul Saha

LLMSTEP is a Lean 4 tactic that sends a user's proof state to a server hosting a language model.

3 code implementations • 16 Oct 2023 • Zhangir Azerbayev, Hailey Schoelkopf, Keiran Paster, Marco Dos Santos, Stephen Mcaleer, Albert Q. Jiang, Jia Deng, Stella Biderman, Sean Welleck

We present Llemma, a large language model for mathematics.

Ranked #5 on Automated Theorem Proving on miniF2F-test

1 code implementation • NeurIPS 2023 • Nouha Dziri, Ximing Lu, Melanie Sclar, Xiang Lorraine Li, Liwei Jiang, Bill Yuchen Lin, Peter West, Chandra Bhagavatula, Ronan Le Bras, Jena D. Hwang, Soumya Sanyal, Sean Welleck, Xiang Ren, Allyson Ettinger, Zaid Harchaoui, Yejin Choi

We formulate compositional tasks as computation graphs to systematically quantify the level of complexity, and break down reasoning steps into intermediate sub-procedures.

no code implementations • 24 May 2023 • Ximing Lu, Faeze Brahman, Peter West, Jaehun Jang, Khyathi Chandu, Abhilasha Ravichander, Lianhui Qin, Prithviraj Ammanabrolu, Liwei Jiang, Sahana Ramnath, Nouha Dziri, Jillian Fisher, Bill Yuchen Lin, Skyler Hallinan, Xiang Ren, Sean Welleck, Yejin Choi

In particular, tailoring GPT-2 with IPA can outperform GPT-3, while tailoring GPT- 3 with IPA brings a major performance boost over GPT-3 (and sometimes even over GPT-4).

1 code implementation • NeurIPS 2023 • Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, Shashank Gupta, Bodhisattwa Prasad Majumder, Katherine Hermann, Sean Welleck, Amir Yazdanbakhsh, Peter Clark

Motivated by how humans refine their written text, we introduce Self-Refine, an approach for improving initial outputs from LLMs through iterative feedback and refinement.

1 code implementation • 30 Dec 2022 • Krishna Pillutla, Lang Liu, John Thickstun, Sean Welleck, Swabha Swayamdipta, Rowan Zellers, Sewoong Oh, Yejin Choi, Zaid Harchaoui

We present MAUVE, a family of comparison measures between pairs of distributions such as those encountered in the generative modeling of text or images.

2 code implementations • 20 Dec 2022 • Pan Lu, Liang Qiu, Wenhao Yu, Sean Welleck, Kai-Wei Chang

Mathematical reasoning is a fundamental aspect of human intelligence and is applicable in various fields, including science, engineering, finance, and everyday life.

1 code implementation • 31 Oct 2022 • Swaroop Mishra, Matthew Finlayson, Pan Lu, Leonard Tang, Sean Welleck, Chitta Baral, Tanmay Rajpurohit, Oyvind Tafjord, Ashish Sabharwal, Peter Clark, Ashwin Kalyan

Mathematical reasoning skills are essential for general-purpose intelligent systems to perform tasks from grocery shopping to climate modeling.

Ranked #1 on Mathematical Reasoning on Lila (OOD)

no code implementations • 31 Oct 2022 • Sean Welleck, Ximing Lu, Peter West, Faeze Brahman, Tianxiao Shen, Daniel Khashabi, Yejin Choi

Sequence generation applications require satisfying semantic constraints, such as ensuring that programs are correct, using certain keywords, or avoiding undesirable content.

3 code implementations • 21 Oct 2022 • Albert Q. Jiang, Sean Welleck, Jin Peng Zhou, Wenda Li, Jiacheng Liu, Mateja Jamnik, Timothée Lacroix, Yuhuai Wu, Guillaume Lample

In this work, we introduce Draft, Sketch, and Prove (DSP), a method that maps informal proofs to formal proof sketches, and uses the sketches to guide an automated prover by directing its search to easier sub-problems.

Ranked #3 on Automated Theorem Proving on miniF2F-valid (Pass@100 metric)

1 code implementation • 6 Oct 2022 • Jiacheng Liu, Skyler Hallinan, Ximing Lu, Pengfei He, Sean Welleck, Hannaneh Hajishirzi, Yejin Choi

Our work is the first to report that knowledge generated by models that are orders of magnitude smaller than GPT-3, even without direct supervision on the knowledge itself, can exceed the quality of commonsense knowledge elicited from GPT-3.

1 code implementation • 26 May 2022 • Ximing Lu, Sean Welleck, Jack Hessel, Liwei Jiang, Lianhui Qin, Peter West, Prithviraj Ammanabrolu, Yejin Choi

Large-scale language models often learn behaviors that are misaligned with user expectations.

1 code implementation • 25 May 2022 • Sean Welleck, Jiacheng Liu, Ximing Lu, Hannaneh Hajishirzi, Yejin Choi

Theorem proving in natural mathematical language - the mixture of symbolic and natural language used by humans - plays a central role in mathematical advances and education, and tests aspects of reasoning that are core to intelligence.

no code implementations • 24 May 2022 • JaeHun Jung, Lianhui Qin, Sean Welleck, Faeze Brahman, Chandra Bhagavatula, Ronan Le Bras, Yejin Choi

Despite their impressive capabilities, large pre-trained language models (LMs) struggle with consistent reasoning; recently, prompting LMs to generate explanations that self-guide the inference has emerged as a promising direction to amend this.

1 code implementation • 23 Feb 2022 • Lianhui Qin, Sean Welleck, Daniel Khashabi, Yejin Choi

Many applications of text generation require incorporating different constraints to control the semantics or style of generated text.

1 code implementation • NAACL 2022 • Ximing Lu, Sean Welleck, Peter West, Liwei Jiang, Jungo Kasai, Daniel Khashabi, Ronan Le Bras, Lianhui Qin, Youngjae Yu, Rowan Zellers, Noah A. Smith, Yejin Choi

To enable constrained generation, we build on NeuroLogic decoding (Lu et al., 2021), combining its flexibility in incorporating logical constraints with A*esque estimates of future constraint satisfaction.

Ranked #1 on Text Generation on ROCStories

1 code implementation • NAACL 2022 • Daniel Khashabi, Shane Lyu, Sewon Min, Lianhui Qin, Kyle Richardson, Sean Welleck, Hannaneh Hajishirzi, Tushar Khot, Ashish Sabharwal, Sameer Singh, Yejin Choi

Fine-tuning continuous prompts for target tasks has recently emerged as a compact alternative to full model fine-tuning.

1 code implementation • ACL 2022 • Jiacheng Liu, Alisa Liu, Ximing Lu, Sean Welleck, Peter West, Ronan Le Bras, Yejin Choi, Hannaneh Hajishirzi

It remains an open question whether incorporating external knowledge benefits commonsense reasoning while maintaining the flexibility of pretrained sequence models.

1 code implementation • NAACL 2022 • Peter West, Chandra Bhagavatula, Jack Hessel, Jena D. Hwang, Liwei Jiang, Ronan Le Bras, Ximing Lu, Sean Welleck, Yejin Choi

We apply this to the ATOMIC resource, and share our new symbolic knowledge graph and commonsense models.

1 code implementation • 28 Sep 2021 • Sean Welleck, Peter West, Jize Cao, Yejin Choi

Neural sequence models trained with maximum likelihood estimation have led to breakthroughs in many tasks, where success is defined by the gap between training and test performance.

Out-of-Distribution Generalization
Systematic Generalization
**+1**

1 code implementation • NeurIPS 2021 • Lang Liu, Krishna Pillutla, Sean Welleck, Sewoong Oh, Yejin Choi, Zaid Harchaoui

The spectacular success of deep generative models calls for quantitative tools to measure their statistical performance.

1 code implementation • ACL (spnlp) 2021 • Ilia Kulikov, Sean Welleck, Kyunghyun Cho

We propose to study these phenomena by investigating how the modes, or local maxima, of a distribution are maintained throughout the full learning chain of the ground-truth, empirical, learned and decoding-induced distributions, via the newly proposed mode recovery cost.

1 code implementation • 24 Mar 2021 • Sean Welleck, Jiacheng Liu, Ronan Le Bras, Hannaneh Hajishirzi, Yejin Choi, Kyunghyun Cho

Understanding and creating mathematics using natural mathematical language - the mixture of symbolic and natural language used by humans - is a challenging and important problem for driving progress in machine learning.

3 code implementations • NeurIPS 2021 • Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, John Thickstun, Sean Welleck, Yejin Choi, Zaid Harchaoui

As major progress is made in open-ended text generation, measuring how close machine-generated text is to human language remains a critical open problem.

1 code implementation • 4 Jun 2020 • Sean Welleck, Kyunghyun Cho

Typical approaches to directly optimizing the task loss such as policy gradient and minimum risk training are based around sampling in the sequence space to obtain candidate update directions that are scored based on the loss of a single sequence.

1 code implementation • EMNLP 2020 • Sean Welleck, Ilia Kulikov, Jaedeok Kim, Richard Yuanzhe Pang, Kyunghyun Cho

Despite strong performance on a variety of tasks, neural sequence models trained with maximum likelihood have been shown to exhibit issues such as length bias and degenerate repetition.

1 code implementation • ACL 2020 • Margaret Li, Stephen Roller, Ilia Kulikov, Sean Welleck, Y-Lan Boureau, Kyunghyun Cho, Jason Weston

Generative dialogue models currently suffer from a number of problems which standard maximum likelihood training does not address.

4 code implementations • ICLR 2020 • Sean Welleck, Ilia Kulikov, Stephen Roller, Emily Dinan, Kyunghyun Cho, Jason Weston

Neural text generation is a key tool in natural language applications, but it is well known there are major problems at its core.

1 code implementation • 29 May 2019 • Elman Mansimov, Alex Wang, Sean Welleck, Kyunghyun Cho

We investigate this problem by proposing a generalized model of sequence generation that unifies decoding in directed and undirected models.

no code implementations • RANLP 2019 • Sean Welleck, Kyunghyun Cho

We propose a method for non-projective dependency parsing by incrementally predicting a set of edges.

1 code implementation • WS 2019 • Sean Welleck, Kianté Brantley, Hal Daumé III, Kyunghyun Cho

Standard sequential generation methods assume a pre-specified generation order, such as text generation methods which generate words from left to right.

no code implementations • ACL 2019 • Sean Welleck, Jason Weston, Arthur Szlam, Kyunghyun Cho

Consistency is a long standing issue faced by dialogue models.

no code implementations • ICLR 2018 • Sean Welleck, Zixin Yao, Yu Gai, Jialin Mao, Zheng Zhang, Kyunghyun Cho

In this paper, we propose a novel multiset loss function by viewing this problem from the perspective of sequential decision making.

no code implementations • NeurIPS 2017 • Sean Welleck, Jialin Mao, Kyunghyun Cho, Zheng Zhang

Humans process visual scenes selectively and sequentially using attention.

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