2 research outputs found
Human Evaluation of Text-to-Image Models on a Multi-Task Benchmark
We provide a new multi-task benchmark for evaluating text-to-image models. We
perform a human evaluation comparing the most common open-source (Stable
Diffusion) and commercial (DALL-E 2) models. Twenty computer science AI
graduate students evaluated the two models, on three tasks, at three difficulty
levels, across ten prompts each, providing 3,600 ratings. Text-to-image
generation has seen rapid progress to the point that many recent models have
demonstrated their ability to create realistic high-resolution images for
various prompts. However, current text-to-image methods and the broader body of
research in vision-language understanding still struggle with intricate text
prompts that contain many objects with multiple attributes and relationships.
We introduce a new text-to-image benchmark that contains a suite of thirty-two
tasks over multiple applications that capture a model's ability to handle
different features of a text prompt. For example, asking a model to generate a
varying number of the same object to measure its ability to count or providing
a text prompt with several objects that each have a different attribute to
identify its ability to match objects and attributes correctly. Rather than
subjectively evaluating text-to-image results on a set of prompts, our new
multi-task benchmark consists of challenge tasks at three difficulty levels
(easy, medium, and hard) and human ratings for each generated image.Comment: NeurIPS 2022 Workshop on Human Evaluation of Generative Models (HEGM
From Human Days to Machine Seconds: Automatically Answering and Generating Machine Learning Final Exams
A final exam in machine learning at a top institution such as MIT, Harvard,
or Cornell typically takes faculty days to write, and students hours to solve.
We demonstrate that large language models pass machine learning finals at a
human level, on finals available online after the models were trained, and
automatically generate new human-quality final exam questions in seconds.
Previous work has developed program synthesis and few-shot learning methods to
solve university-level problem set questions in mathematics and STEM courses.
In this work, we develop and compare methods that solve final exams, which
differ from problem sets in several ways: the questions are longer, have
multiple parts, are more complicated, and span a broader set of topics. We
curate a dataset and benchmark of questions from machine learning final exams
available online and code for answering these questions and generating new
questions. We show how to generate new questions from other questions and
course notes. For reproducibility and future research on this final exam
benchmark, we use automatic checkers for multiple-choice, numeric, and
questions with expression answers. We perform ablation studies comparing
zero-shot learning with few-shot learning and chain-of-thought prompting using
GPT-3, OPT, Codex, and ChatGPT across machine learning topics and find that
few-shot learning methods perform best. We highlight the transformative
potential of language models to streamline the writing and solution of
large-scale assessments, significantly reducing the workload from human days to
mere machine seconds. Our results suggest that rather than banning large
language models such as ChatGPT in class, instructors should teach students to
harness them by asking students meta-questions about correctness, completeness,
and originality of the responses generated, encouraging critical thinking in
academic studies.Comment: 9 page