Itay Laish
YOU?
Author Swipe
View article: LLMs Accelerate Annotation for Medical Information Extraction
LLMs Accelerate Annotation for Medical Information Extraction Open
The unstructured nature of clinical notes within electronic health records often conceals vital patient-related information, making it challenging to access or interpret. To uncover this hidden information, specialized Natural Language Pro…
View article: Surfacing Biases in Large Language Models using Contrastive Input Decoding
Surfacing Biases in Large Language Models using Contrastive Input Decoding Open
Ensuring that large language models (LMs) are fair, robust and useful requires an understanding of how different modifications to their inputs impact the model's behaviour. In the context of open-text generation tasks, however, such an eva…
View article: Useful Confidence Measures: Beyond the Max Score
Useful Confidence Measures: Beyond the Max Score Open
An important component in deploying machine learning (ML) in safety-critic applications is having a reliable measure of confidence in the ML model's predictions. For a classifier $f$ producing a probability vector $f(x)$ over the candidate…
View article: Building a Clinically-Focused Problem List From Medical Notes
Building a Clinically-Focused Problem List From Medical Notes Open
Amir Feder, Itay Laish, Shashank Agarwal, Uri Lerner, Avel Atias, Cathy Cheung, Peter Clardy, Alon Peled-Cohen, Rachana Fellinger, Hengrui Liu, Lan Huong Nguyen, Birju Patel, Natan Potikha, Amir Taubenfeld, Liwen Xu, Seung Doo Yang, Ayelet…
View article: Section Classification in Clinical Notes with Multi-task Transformers
Section Classification in Clinical Notes with Multi-task Transformers Open
Clinical notes are the backbone of electronic health records, often containing vital information not observed in other structured data. Unfortunately, the unstructured nature of clinical notes can lead to critical patient-related informati…
View article: Learning and Evaluating a Differentially Private Pre-trained Language Model
Learning and Evaluating a Differentially Private Pre-trained Language Model Open
Shlomo Hoory, Amir Feder, Avichai Tendler, Alon Cohen, Sofia Erell, Itay Laish, Hootan Nakhost, Uri Stemmer, Ayelet Benjamini, Avinatan Hassidim, Yossi Matias. Proceedings of the Third Workshop on Privacy in Natural Language Processing. 20…
View article: Learning and Evaluating a Differentially Private Pre-trained Language Model
Learning and Evaluating a Differentially Private Pre-trained Language Model Open
Shlomo Hoory, Amir Feder, Avichai Tendler, Sofia Erell, Alon Peled-Cohen, Itay Laish, Hootan Nakhost, Uri Stemmer, Ayelet Benjamini, Avinatan Hassidim, Yossi Matias. Findings of the Association for Computational Linguistics: EMNLP 2021. 20…
View article: Audio De-identification: A New Entity Recognition Task
Audio De-identification: A New Entity Recognition Task Open
Named Entity Recognition (NER) has been mostly studied in the context of written text. Specifically, NER is an important step in de-identification (de-ID) of medical records, many of which are recorded conversations between a patient and a…
View article: Audio De-identification - a New Entity Recognition Task
Audio De-identification - a New Entity Recognition Task Open
Ido Cohn, Itay Laish, Genady Beryozkin, Gang Li, Izhak Shafran, Idan Szpektor, Tzvika Hartman, Avinatan Hassidim, Yossi Matias. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguisti…
View article: Efficient Dynamic Approximate Distance Oracles for Vertex-Labeled Planar Graphs
Efficient Dynamic Approximate Distance Oracles for Vertex-Labeled Planar Graphs Open
Let $G$ be a graph where each vertex is associated with a label. A Vertex-Labeled Approximate Distance Oracle is a data structure that, given a vertex $v$ and a label $λ$, returns a $(1+\varepsilon)$-approximation of the distance from $v$ …