Search Results for author:
Found 9 papers, 5 papers with code
PaperQA: Retrieval-Augmented Generative Agent for Scientific Research
We present PaperQA, a RAG agent for answering questions over the scientific literature.
BioPlanner: Automatic Evaluation of LLMs on Protocol Planning in Biology
Here we present an automatic evaluation framework for the task of planning experimental protocols, and we introduce BioProt: a dataset of biology protocols with corresponding pseudocode representations.
VisoGender: A dataset for benchmarking gender bias in image-text pronoun resolution
We introduce VisoGender, a novel dataset for benchmarking gender bias in vision-language models.
Balancing the Picture: Debiasing Vision-Language Datasets with Synthetic Contrast Sets
To address this issue, we propose a novel dataset debiasing pipeline to augment the COCO dataset with synthetic, gender-balanced contrast sets, where only the gender of the subject is edited and the background is fixed.
What does CLIP know about a red circle? Visual prompt engineering for VLMs
Large-scale Vision-Language Models, such as CLIP, learn powerful image-text representations that have found numerous applications, from zero-shot classification to text-to-image generation.
A Prompt Array Keeps the Bias Away: Debiasing Vision-Language Models with Adversarial Learning
Vision-language models can encode societal biases and stereotypes, but there are challenges to measuring and mitigating these multimodal harms due to lacking measurement robustness and feature degradation.
Memes in the Wild: Assessing the Generalizability of the Hateful Memes Challenge Dataset
In this paper, we collect hateful and non-hateful memes from Pinterest to evaluate out-of-sample performance on models pre-trained on the Facebook dataset.
Privacy-preserving Object Detection
Privacy considerations and bias in datasets are quickly becoming high-priority issues that the computer vision community needs to face.
Bias Out-of-the-Box: An Empirical Analysis of Intersectional Occupational Biases in Popular Generative Language Models
Using a template-based data collection pipeline, we collect 396K sentence completions made by GPT-2 and find: (i) The machine-predicted jobs are less diverse and more stereotypical for women than for men, especially for intersections; (ii) Intersectional interactions are highly relevant for occupational associations, which we quantify by fitting 262 logistic models; (iii) For most occupations, GPT-2 reflects the skewed gender and ethnicity distribution found in US Labor Bureau data, and even pulls the societally-skewed distribution towards gender parity in cases where its predictions deviate from real labor market observations.
