Search Results for author:
Found 8 papers, 6 papers with code
What Languages are Easy to Language-Model? A Perspective from Learning Probabilistic Regular Languages
We find that the RLM rank, which corresponds to the size of linear space spanned by the logits of its conditional distributions, and the expected length of sampled strings are strong and significant predictors of learnability for both RNNs and Transformers.
Imitation of Life: A Search Engine for Biologically Inspired Design
We release data and code; we view BARcode as a step towards addressing the challenges that have historically hindered the practical application of BID to engineering innovation.
Factcheck-Bench: Fine-Grained Evaluation Benchmark for Automatic Fact-checkers
The increased use of large language models (LLMs) across a variety of real-world applications calls for mechanisms to verify the factual accuracy of their outputs.
PHD: Pixel-Based Language Modeling of Historical Documents
We then pre-train our model, PHD, on a combination of synthetic scans and real historical newspapers from the 1700-1900 period.
Measuring Intersectional Biases in Historical Documents
We find that there is a trade-off between the stability of the word embeddings and their compatibility with the historical dataset.
Optical Character Recognition
Optical Character Recognition (OCR)
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Multilingual Event Extraction from Historical Newspaper Adverts
We find that: 1) even with scarce annotated data, it is possible to achieve surprisingly good results by formulating the problem as an extractive QA task and leveraging existing datasets and models for modern languages; and 2) cross-lingual low-resource learning for historical languages is highly challenging, and machine translation of the historical datasets to the considered target languages is, in practice, often the best-performing solution.
Temporally stable video segmentation without video annotations
Temporally consistent dense video annotations are scarce and hard to collect.
How Did This Get Funded?! Automatically Identifying Quirky Scientific Achievements
We construct a dataset containing thousands of funny papers and use it to learn classifiers, combining findings from psychology and linguistics with recent advances in NLP.
