Search Results for author: Sean Welleck

Found 35 papers, 27 papers with code

STEER: Unified Style Transfer with Expert Reinforcement

no code implementations13 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.

Style Transfer Text Style Transfer

LLMSTEP: LLM proofstep suggestions in Lean

1 code implementation27 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.

Language Modelling

Faith and Fate: Limits of Transformers on Compositionality

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.

MAUVE Scores for Generative Models: Theory and Practice

1 code implementation30 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.


A Survey of Deep Learning for Mathematical Reasoning

2 code implementations20 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.

Math Mathematical Reasoning

Generating Sequences by Learning to Self-Correct

no code implementations31 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.

Language Modelling Program Synthesis

Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs

3 code implementations21 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)

Automated Theorem Proving Language Modelling

Rainier: Reinforced Knowledge Introspector for Commonsense Question Answering

1 code implementation6 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.

Question Answering Reinforcement Learning (RL)

NaturalProver: Grounded Mathematical Proof Generation with Language Models

1 code implementation25 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.

Automated Theorem Proving Language Modelling

Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations

no code implementations24 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.

COLD Decoding: Energy-based Constrained Text Generation with Langevin Dynamics

1 code implementation23 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.

counterfactual Counterfactual Reasoning +1

NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics

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.

Machine Translation Table-to-Text Generation

Generated Knowledge Prompting for Commonsense Reasoning

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.

Language Modelling Open-Ended Question Answering

Symbolic Brittleness in Sequence Models: on Systematic Generalization in Symbolic Mathematics

1 code implementation28 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

Mode recovery in neural autoregressive sequence modeling

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.

NaturalProofs: Mathematical Theorem Proving in Natural Language

1 code implementation24 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.

Automated Theorem Proving Domain Generalization +2

MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers

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.

Text Generation

MLE-guided parameter search for task loss minimization in neural sequence modeling

1 code implementation4 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.

Machine Translation

Consistency of a Recurrent Language Model With Respect to Incomplete Decoding

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.

Language Modelling

Don't Say That! Making Inconsistent Dialogue Unlikely with Unlikelihood Training

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.

Neural Text Generation with Unlikelihood Training

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.

Blocking Text Generation

A Generalized Framework of Sequence Generation with Application to Undirected Sequence Models

1 code implementation29 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.

Machine Translation Natural Language Inference +3

Sequential Graph Dependency Parser

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.

Dependency Parsing

Non-Monotonic Sequential Text Generation

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.

Imitation Learning Text Generation

Loss Functions for Multiset Prediction

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.

Decision Making Reinforcement Learning (RL)

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