Search Results for author: Michael Saxon

Found 19 papers, 11 papers with code

Multilingual Conceptual Coverage in Text-to-Image Models

1 code implementation2 Jun 2023 Michael Saxon, William Yang Wang

We propose "Conceptual Coverage Across Languages" (CoCo-CroLa), a technique for benchmarking the degree to which any generative text-to-image system provides multilingual parity to its training language in terms of tangible nouns.


Let's Think Frame by Frame with VIP: A Video Infilling and Prediction Dataset for Evaluating Video Chain-of-Thought

1 code implementation23 May 2023 Vaishnavi Himakunthala, Andy Ouyang, Daniel Rose, Ryan He, Alex Mei, Yujie Lu, Chinmay Sonar, Michael Saxon, William Yang Wang

Despite exciting recent results showing vision-language systems' capacity to reason about images using natural language, their capacity for video reasoning remains under-explored.

Descriptive Video Prediction

Data Augmentation for Diverse Voice Conversion in Noisy Environments

no code implementations18 May 2023 Avani Tanna, Michael Saxon, Amr El Abbadi, William Yang Wang

Voice conversion (VC) models have demonstrated impressive few-shot conversion quality on the clean, native speech populations they're trained on.

Data Augmentation Denoising +1

Visual Chain of Thought: Bridging Logical Gaps with Multimodal Infillings

no code implementations3 May 2023 Daniel Rose, Vaishnavi Himakunthala, Andy Ouyang, Ryan He, Alex Mei, Yujie Lu, Michael Saxon, Chinmay Sonar, Diba Mirza, William Yang Wang

We apply VCoT to the Visual Storytelling and WikiHow summarization datasets and demonstrate through human evaluation that VCoT offers novel and consistent synthetic data augmentation beating chain of thought baselines, which can be used to enhance downstream performance.

Data Augmentation Question Answering +1

Users are the North Star for AI Transparency

no code implementations9 Mar 2023 Alex Mei, Michael Saxon, Shiyu Chang, Zachary C. Lipton, William Yang Wang

We conduct a broad literature survey, identifying many clusters of similar conceptions of transparency, tying each back to our north star with analysis of how it furthers or hinders our ideal AI transparency goals.

Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning

1 code implementation27 Jan 2023 Xinyi Wang, Wanrong Zhu, Michael Saxon, Mark Steyvers, William Yang Wang

This study aims to examine the in-context learning phenomenon through a Bayesian lens, viewing real-world LLMs as latent variable models.

Few-Shot Learning GSM8K +4

CausalDialogue: Modeling Utterance-level Causality in Conversations

1 code implementation20 Dec 2022 Yi-Lin Tuan, Alon Albalak, Wenda Xu, Michael Saxon, Connor Pryor, Lise Getoor, William Yang Wang

Despite their widespread adoption, neural conversation models have yet to exhibit natural chat capabilities with humans.

Dialogue Generation

WikiWhy: Answering and Explaining Cause-and-Effect Questions

no code implementations21 Oct 2022 Matthew Ho, Aditya Sharma, Justin Chang, Michael Saxon, Sharon Levy, Yujie Lu, William Yang Wang

As large language models (LLMs) grow larger and more sophisticated, assessing their "reasoning" capabilities in natural language grows more challenging.

Question Answering

Causal Balancing for Domain Generalization

1 code implementation10 Jun 2022 Xinyi Wang, Michael Saxon, Jiachen Li, Hongyang Zhang, Kun Zhang, William Yang Wang

While machine learning models rapidly advance the state-of-the-art on various real-world tasks, out-of-domain (OOD) generalization remains a challenging problem given the vulnerability of these models to spurious correlations.

Domain Generalization

PECO: Examining Single Sentence Label Leakage in Natural Language Inference Datasets through Progressive Evaluation of Cluster Outliers

no code implementations16 Dec 2021 Michael Saxon, Xinyi Wang, Wenda Xu, William Yang Wang

Building natural language inference (NLI) benchmarks that are both challenging for modern techniques, and free from shortcut biases is difficult.

Natural Language Inference

Self-Supervised Knowledge Assimilation for Expert-Layman Text Style Transfer

1 code implementation6 Oct 2021 Wenda Xu, Michael Saxon, Misha Sra, William Yang Wang

This is a particularly notable issue in the medical domain, where layman are often confused by medical text online.

Language Modelling Self-Supervised Learning +2

End-to-End Spoken Language Understanding for Generalized Voice Assistants

no code implementations16 Jun 2021 Michael Saxon, Samridhi Choudhary, Joseph P. McKenna, Athanasios Mouchtaris

End-to-end (E2E) spoken language understanding (SLU) systems predict utterance semantics directly from speech using a single model.

Ranked #9 on Spoken Language Understanding on Fluent Speech Commands (using extra training data)

Spoken Language Understanding

Counterfactual Maximum Likelihood Estimation for Training Deep Networks

1 code implementation NeurIPS 2021 Xinyi Wang, Wenhu Chen, Michael Saxon, William Yang Wang

Although deep learning models have driven state-of-the-art performance on a wide array of tasks, they are prone to spurious correlations that should not be learned as predictive clues.

counterfactual Domain Generalization +2

Modeling Disclosive Transparency in NLP Application Descriptions

1 code implementation EMNLP 2021 Michael Saxon, Sharon Levy, Xinyi Wang, Alon Albalak, William Yang Wang

Broader disclosive transparency$-$truth and clarity in communication regarding the function of AI systems$-$is widely considered desirable.

Fairness Language Modelling +1

Semantic Complexity in End-to-End Spoken Language Understanding

no code implementations6 Aug 2020 Joseph P. McKenna, Samridhi Choudhary, Michael Saxon, Grant P. Strimel, Athanasios Mouchtaris

We perform experiments where we vary the semantic complexity of a large, proprietary dataset and show that STI model performance correlates with our semantic complexity measures, such that performance increases as complexity values decrease.

Spoken Language Understanding

Robust Estimation of Hypernasality in Dysarthria with Acoustic Model Likelihood Features

no code implementations26 Nov 2019 Michael Saxon, Ayush Tripathi, Yishan Jiao, Julie Liss, Visar Berisha

To demonstrate that the features derived from these acoustic models are specific to hypernasal speech, we evaluate them across different dysarthria corpora.

BIG-bench Machine Learning

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