2 code implementations • 22 Nov 2023 • John X. Morris, Wenting Zhao, Justin T. Chiu, Vitaly Shmatikov, Alexander M. Rush
We consider the problem of language model inversion and show that next-token probabilities contain a surprising amount of information about the preceding text.
no code implementations • 14 Nov 2023 • Sedrick Keh, Justin T. Chiu, Daniel Fried
When a model is trying to gather information in an interactive setting, it benefits from asking informative questions.
1 code implementation • 26 Oct 2023 • Justin T. Chiu, Wenting Zhao, Derek Chen, Saujas Vaduguru, Alexander M. Rush, Daniel Fried
Large language models (LLMs) excel at processing and generating both text and code.
no code implementations • 24 May 2023 • Wenting Zhao, Justin T. Chiu, Claire Cardie, Alexander M. Rush
Instead of using direct supervision, this work proposes an approach for abductive commonsense reasoning that exploits the fact that only a subset of explanations is correct for a given context.
no code implementations • 23 May 2023 • Wenting Zhao, Justin T. Chiu, Claire Cardie, Alexander M. Rush
Explainable multi-hop question answering (QA) not only predicts answers but also identifies rationales, i. e. subsets of input sentences used to derive the answers.
1 code implementation • 25 Oct 2022 • Zhiying Xu, Francis Y. Yan, Rachee Singh, Justin T. Chiu, Alexander M. Rush, Minlan Yu
The rapid expansion of global cloud wide-area networks (WANs) has posed a challenge for commercial optimization engines to efficiently solve network traffic engineering (TE) problems at scale.
Multi-agent Reinforcement Learning Reinforcement Learning (RL)
1 code implementation • 20 Oct 2022 • John X. Morris, Justin T. Chiu, Ramin Zabih, Alexander M. Rush
We propose an unsupervised deidentification method that masks words that leak personally-identifying information.
1 code implementation • NeurIPS 2021 • Justin T. Chiu, Yuntian Deng, Alexander M. Rush
This work demonstrates a simple approach to reduce the computational and memory complexity of a large class of structured models.
1 code implementation • EMNLP 2021 • Daniel Fried, Justin T. Chiu, Dan Klein
We present a grounded neural dialogue model that successfully collaborates with people in a partially-observable reference game.
1 code implementation • EMNLP 2020 • Justin T. Chiu, Alexander M. Rush
The hidden Markov model (HMM) is a fundamental tool for sequence modeling that cleanly separates the hidden state from the emission structure.