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Found 14 papers, 6 papers with code
Best Practices and Lessons Learned on Synthetic Data
The success of AI models relies on the availability of large, diverse, and high-quality datasets, which can be challenging to obtain due to data scarcity, privacy concerns, and high costs.
Long-form factuality in large language models
Empirically, we demonstrate that LLM agents can outperform crowdsourced human annotators - on a set of ~16k individual facts, SAFE agrees with crowdsourced human annotators 72% of the time, and on a random subset of 100 disagreement cases, SAFE wins 76% of the time.
Non-robustness of diffusion estimates on networks with measurement error
First, we show that even when measurement error is vanishingly small, such that the share of missed links is close to zero, forecasts about the extent of diffusion will greatly underestimate the truth.
FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation
Specifically, we introduce FreshQA, a novel dynamic QA benchmark encompassing a diverse range of question and answer types, including questions that require fast-changing world knowledge as well as questions with false premises that need to be debunked.
Simple synthetic data reduces sycophancy in large language models
Adding these data in a lightweight finetuning step can significantly reduce sycophantic behavior on held-out prompts.
Symbol tuning improves in-context learning in language models
We present symbol tuning - finetuning language models on in-context input-label pairs where natural language labels (e. g., "positive/negative sentiment") are replaced with arbitrary symbols (e. g., "foo/bar").
Larger language models do in-context learning differently
We next study semantically-unrelated label ICL (SUL-ICL), in which labels are semantically unrelated to their inputs (e. g., foo/bar instead of negative/positive), thereby forcing language models to learn the input-label mappings shown in in-context exemplars in order to perform the task.
Calibrating Histopathology Image Classifiers using Label Smoothing
Moreover, we find that using model confidence as a proxy for annotator agreement also improves calibration and accuracy, suggesting that datasets without multiple annotators can still benefit from our proposed label smoothing methods via our proposed confidence-aware label smoothing methods.
A Petri Dish for Histopathology Image Analysis
With the rise of deep learning, there has been increased interest in using neural networks for histopathology image analysis, a field that investigates the properties of biopsy or resected specimens traditionally manually examined under a microscope by pathologists.
Learn like a Pathologist: Curriculum Learning by Annotator Agreement for Histopathology Image Classification
Based on the nature of histopathology images, a range of difficulty inherently exists among examples, and, since medical datasets are often labeled by multiple annotators, annotator agreement can be used as a natural proxy for the difficulty of a given example.
NewB: 200,000+ Sentences for Political Bias Detection
We present the Newspaper Bias Dataset (NewB), a text corpus of more than 200, 000 sentences from eleven news sources regarding Donald Trump.
What Are People Asking About COVID-19? A Question Classification Dataset
We present COVID-Q, a set of 1, 690 questions about COVID-19 from 13 sources, which we annotate into 15 question categories and 207 question clusters.
Difficulty Translation in Histopathology Images
Our model comprises a scorer, which provides an output confidence to measure the difficulty of images, and an image translator, which learns to translate images from easy-to-classify to hard-to-classify using a training set defined by the scorer.
Generative Image Translation for Data Augmentation in Colorectal Histopathology Images
We present an image translation approach to generate augmented data for mitigating data imbalances in a dataset of histopathology images of colorectal polyps, adenomatous tumors that can lead to colorectal cancer if left untreated.
