Sean Welleck | DeepAI

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∙ 03/30/2023

Self-Refine: Iterative Refinement with Self-Feedback

Like people, LLMs do not always generate the best text for a given gener...

2 Aman Madaan, et al. ∙

∙ 12/20/2022

A Survey of Deep Learning for Mathematical Reasoning

Mathematical reasoning is a fundamental aspect of human intelligence and...

12 Pan Lu @ UCLA, et al. ∙

∙ 10/31/2022

Lila: A Unified Benchmark for Mathematical Reasoning

Mathematical reasoning skills are essential for general-purpose intellig...

15 Swaroop Mishra, et al. ∙

∙ 10/21/2022

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

The formalization of existing mathematical proofs is a notoriously diffi...

0 Albert Q. Jiang, et al. ∙

∙ 10/14/2021

Symbolic Knowledge Distillation: from General Language Models to Commonsense Models

The common practice for training commonsense models has gone from-human-...

0 Peter West, et al. ∙

∙ 09/28/2021

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

Neural sequence models trained with maximum likelihood estimation have l...

0 Sean Welleck, et al. ∙

∙ 06/15/2021

Divergence Frontiers for Generative Models: Sample Complexity, Quantization Level, and Frontier Integral

The spectacular success of deep generative models calls for quantitative...

0 Lang Liu, et al. ∙

∙ 06/10/2021

Mode recovery in neural autoregressive sequence modeling

Despite its wide use, recent studies have revealed unexpected and undesi...

5 Ilia Kulikov, et al. ∙

∙ 03/24/2021

NaturalProofs: Mathematical Theorem Proving in Natural Language

Understanding and creating mathematics using natural mathematical langua...

0 Sean Welleck, et al. ∙

∙ 06/04/2020

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

Neural autoregressive sequence models are used to generate sequences in ...

15 Sean Welleck, et al. ∙

∙ 02/06/2020

Consistency of a Recurrent Language Model With Respect to Incomplete Decoding

Despite strong performance on a variety of tasks, neural sequence models...

7 Sean Welleck, et al. ∙

∙ 11/10/2019

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

Generative dialogue models currently suffer from a number of problems wh...

0 Margaret Li, et al. ∙

∙ 08/12/2019

Neural Text Generation with Unlikelihood Training

Neural text generation is a key tool in natural language applications, b...

2 Sean Welleck, et al. ∙

∙ 05/27/2019

Sequential Graph Dependency Parser

We propose a method for non-projective dependency parsing by incremental...

2 Sean Welleck, et al. ∙

∙ 02/05/2019

Non-Monotonic Sequential Text Generation

Standard sequential generation methods assume a pre-specified generation...

8 Sean Welleck, et al. ∙

∙ 11/01/2018

Dialogue Natural Language Inference

Consistency is a long standing issue faced by dialogue models. In this p...

6 Sean Welleck, et al. ∙

∙ 11/14/2017

Loss Functions for Multiset Prediction

We study the problem of multiset prediction. The goal of multiset predic...

0 Sean Welleck, et al. ∙

∙ 11/14/2017

Saliency-based Sequential Image Attention with Multiset Prediction

Humans process visual scenes selectively and sequentially using attentio...

0 Sean Welleck, et al. ∙

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