David Reitter

David Reitter studies natural language processing in the contexts of dialogue and generative models. His most recent work has solved problems in generating grounded text. Such text appears, for example, in certain chat or summarization systems. Grounded text only makes claims that are based on available evidence. Dr. Reitter has studied ways to detect claims that are not grounded in evidence; these evaluation methods have become the basis for generative models across many Google products.

Reitter's research interests cover diverse areas of computational cognitive science. Reitter, with academic colleagues, started a subfield of psycholinguistics that used large-scale observational datasets to understand how the mind processes language and allows us to engage in conversation.

David Reitter has authored more than 120 papers in both cognitive psychology and computer science, and Aquamacs, a widely used software package. Prof. Reitter joined Google from Penn State, where he directed an NSF-funded research group on computational cognition and language processing. He holds a Ph.D. from the University of Edinburgh, prior degrees in linguistics and computer science, and was a fellow at MIT's Media Lab Europe (working on multimodal user interfaces) and a post-doc at Carnegie Mellon University (working on cognitive modeling).

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Investigating Content Planning for Navigating Trade-offs in Knowledge-Grounded Dialogue
Kushal Chawla
Hannah Rashkin
Gaurav Singh Tomar
David Reitter
EACL (2024) (to appear)
Characterizing Attribution and Fluency Tradeoffs for Retrieval-Augmented Large Language Models
Renat Aksitov
Chung-ching Chang
David Reitter
Siamak Shakeri
Yunhsuan Sung
arXiv (2023)
Measuring Attribution in Natural Language Generation Models
Hannah Rashkin
Vitaly Nikolaev
Matthew Lamm
Lora Aroyo
Michael Collins
Dipanjan Das
Slav Petrov
Gaurav Singh Tomar
Iulia Turc
David Reitter
Computational Linguistics, 49 (2023), pp. 777-840
Evaluating Attribution in Dialogue Systems: The BEGIN Benchmark
Nouha Dziri
Hannah Jane Rashkin
Tal Linzen
David Reitter
Transactions of the Association for Computational Linguistics, 10 (2022), 1066–1083
Dungeons and Dragons as a Challenge Problem for Artificial Intelligence
Chris Callison-Burch
Daphne Ippolito
David Reitter
Gaurav Singh Tomar
Lara Martin
NAACL Wordplay Workshop, ACL (2022) (to appear)
CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning
Ellen Wu
Yi Luan
Hannah Jane Rashkin
David Reitter
Hannaneh Hajishirzi
Mari Ostendorf
Gaurav Singh Tomar
The 2022 Conference on Empirical Methods in Natural Language Processing (2022)
Increasing Faithfulness in Knowledge-Grounded Dialogue with Controllable Features
Hannah Jane Rashkin
David Reitter
Gaurav Singh Tomar
Dipanjan Das
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (2021), pp. 704-718
Indirect Associations in Learning Semantic and Syntactic Lexical Relationships
Matthew A. Kelly
Moojan Ghafurian
Robert L. West
David Reitter
Journal of Memory and Language, 115 (2020), pp. 104153
Which sentence embeddings and which layers encode syntactic structure?
M. A. Kelly
Yang Xu
Jesús Calvillo
David Reitter
(2020)
Like a Baby: Visually Situated Neural Language Acquisition
Alexander G. Ororbia II
Ankur Mali
Matthew A. Kelly
David Reitter
57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy (2019)
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