Tal Schuster

Tal Schuster

Tal Schuster is a Research Scientist at Google DeepMind working on Large Language Models. He is developing robust and efficient methods for improving the performance of LLMs across all scales.

For more details and full list of publications see his Personal Website and Google Scholar profile.

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Conformal Risk Control
Anastasios N. Angelopoulos
Stephen Bates
Adam Fisch
Lihua Lei
Tal Schuster
ICLR (2024)
SEMQA: Semi-Extractive Multi-Source Question Answering
Tal Schuster
Adam Lelkes
Haitian Sun
Jai Gupta
Jonathan Berant
William Cohen
Don Metzler
NAACL (2024) (to appear)
Attribute First, then Generate: Locally-attributable Grounded Text Generation
Aviv Slobodkin
Eran Hirsch
Arie Cattan
Tal Schuster
Ido Dagan
ACL (2024) (to appear)
Conformal Language Modeling
Victor Quach
Adam Fisch
Tal Schuster
Adam Yala
Jae Ho Sohn
Tommi Jaakkola
Regina Barzilay
ICLR (2024)
PropSegmEnt: A Large-Scale Corpus for Proposition-Level Segmentation and Entailment Recognition
Sihao Chen
Senaka Buthpitiya
Alex Fabrikant
Dan Roth
Tal Schuster
Findings of ACL 2023
SDOH-NLI: a Dataset for Inferring Social Determinants of Health from Clinical Notes
Adam Lelkes
Alvin Rajkomar
Eric Loreaux
Ming-Jun Chen
Tal Schuster
Findings of EMNLP (2023)
UL2: Unifying Language Learning Paradigms
Yi Tay
Mostafa Dehghani
Vinh Tran
Xavier Garcia
Jason Wei
Xuezhi Wang
Hyung Won Chung
Dara Bahri
Tal Schuster
Steven Zheng
Denny Zhou
Neil Houlsby
Don Metzler
ICLR (2023)
LAIT: Efficient Multi-Segment Encoding in Transformers with Layer-Adjustable Interaction
Jeremiah Lev Milbauer
Annie Louis
Javad Hosseini
Alex Fabrikant
Don Metzler
Tal Schuster
ACL (2023)
Stretching Sentence-pair NLI Models to Reason over Long Documents and Clusters
Tal Schuster
Sihao Chen
Senaka Wimal Buthpitiya
Alex Fabrikant
Don Metzler
Findings of EMNLP (2022)
Tomayto, Tomahto. Beyond Token-level Answer Equivalence for Question Answering Evaluation
Jannis Bulian
Christian Carl Friedrich Buck
Wojciech Paweł Gajewski
Benjamin Boerschinger
Tal Schuster
EMNLP 2022
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