Search Results for author:
Found 23 papers, 13 papers with code
Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning
It is also a general-purpose model that can do both text-to-image and image-to-text generation, allowing us to introduce self-contained contrastive decoding methods that produce high-quality outputs.
Ranked #2 on
Text-to-Image Generation
on COCO
Shepherd: A Critic for Language Model Generation
As large language models improve, there is increasing interest in techniques that leverage these models' capabilities to refine their own outputs.
Understanding In-Context Learning via Supportive Pretraining Data
We observe that a continued pretraining on this small subset significantly improves the model's ICL ability, by up to 18%.
Open-Domain Text Evaluation via Meta Distribution Modeling
We investigate MDM for open-domain text generation evaluation under two paradigms: 1) \emph{Generative} MDM, which leverages the Meta-Distribution Methods to generate in-domain negative samples for training discriminator-based metrics; 2) \emph{Discriminative} MDM, which directly uses distribution discrepancies between two language models for evaluation.
Gender Biases in Automatic Evaluation Metrics: A Case Study on Image Captioning
Pretrained model-based evaluation metrics have demonstrated strong performance with high correlations with human judgments in various natural language generation tasks such as image captioning.
Variation of Gender Biases in Visual Recognition Models Before and After Finetuning
We also find that (2) models finetuned on larger scale datasets are more likely to introduce new biased associations.
OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization
To this end, we create OPT-IML Bench: a large benchmark for Instruction Meta-Learning (IML) of 2000 NLP tasks consolidated into task categories from 8 existing benchmarks, and prepare an evaluation framework to measure three types of model generalizations: to tasks from fully held-out categories, to held-out tasks from seen categories, and to held-out instances from seen tasks.
Ranked #15 on
Natural Language Inference
on RTE
ALERT: Adapting Language Models to Reasoning Tasks
Current large language models can perform reasonably well on complex tasks that require step-by-step reasoning with few-shot learning.
Text Characterization Toolkit
In NLP, models are usually evaluated by reporting single-number performance scores on a number of readily available benchmarks, without much deeper analysis.
Selective Annotation Makes Language Models Better Few-Shot Learners
Departing from recent in-context learning methods, we formulate an annotation-efficient, two-step framework: selective annotation that chooses a pool of examples to annotate from unlabeled data in advance, followed by prompt retrieval that retrieves task examples from the annotated pool at test time.
OPT: Open Pre-trained Transformer Language Models
Large language models, which are often trained for hundreds of thousands of compute days, have shown remarkable capabilities for zero- and few-shot learning.
Ranked #2 on
Stereotypical Bias Analysis
on CrowS-Pairs
Few-shot Learning with Multilingual Language Models
Large-scale generative language models such as GPT-3 are competitive few-shot learners.
Identifying and Mitigating Spurious Correlations for Improving Robustness in NLP Models
Recently, NLP models have achieved remarkable progress across a variety of tasks; however, they have also been criticized for being not robust.
General Multi-label Image Classification with Transformers
Multi-label image classification is the task of predicting a set of labels corresponding to objects, attributes or other entities present in an image.
Visual News: Benchmark and Challenges in News Image Captioning
We propose Visual News Captioner, an entity-aware model for the task of news image captioning.
CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation
Experiments on real-world NLP datasets demonstrate that our method can generate more diverse and fluent adversarial texts, compared to many existing adversarial text generation approaches.
Double-Hard Debias: Tailoring Word Embeddings for Gender Bias Mitigation
Word embeddings derived from human-generated corpora inherit strong gender bias which can be further amplified by downstream models.
Gender Bias in Contextualized Word Embeddings
In this paper, we quantify, analyze and mitigate gender bias exhibited in ELMo's contextualized word vectors.
Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations
In this work, we present a framework to measure and mitigate intrinsic biases with respect to protected variables --such as gender-- in visual recognition tasks.
Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods
We introduce a new benchmark, WinoBias, for coreference resolution focused on gender bias.
Feedback-prop: Convolutional Neural Network Inference under Partial Evidence
We propose an inference procedure for deep convolutional neural networks (CNNs) when partial evidence is available.
Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints
Language is increasingly being used to define rich visual recognition problems with supporting image collections sourced from the web.
