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ORCIDDan Iter
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2020 – today
- 2024
[c19]Simeng Sun, Yang Liu, Dan Iter, Chenguang Zhu, Mohit Iyyer:
How Does In-Context Learning Help Prompt Tuning? EACL (Findings) 2024: 156-165- [c18]Simeng Sun, Yang Liu, Shuohang Wang, Dan Iter, Chenguang Zhu, Mohit Iyyer:
PEARL: Prompting Large Language Models to Plan and Execute Actions Over Long Documents. EACL (1) 2024: 469-486 - [i15]Marah I Abdin, Sam Ade Jacobs, Ammar Ahmad Awan, Jyoti Aneja, Ahmed Awadallah, Hany Awadalla, Nguyen Bach, Amit Bahree, Arash Bakhtiari, Harkirat S. Behl, Alon Benhaim, Misha Bilenko, Johan Bjorck, Sébastien Bubeck, Martin Cai, Caio César Teodoro Mendes, Weizhu Chen, Vishrav Chaudhary, Parul Chopra, Allie Del Giorno, Gustavo de Rosa, Matthew Dixon, Ronen Eldan, Dan Iter, Amit Garg, Abhishek Goswami, Suriya Gunasekar, Emman Haider, Junheng Hao, Russell J. Hewett, Jamie Huynh, Mojan Javaheripi, Xin Jin, Piero Kauffmann, Nikos Karampatziakis, Dongwoo Kim, Mahoud Khademi, Lev Kurilenko, James R. Lee, Yin Tat Lee, Yuanzhi Li, Chen Liang, Weishung Liu, Eric Lin, Zeqi Lin, Piyush Madan, Arindam Mitra, Hardik Modi, Anh Nguyen, Brandon Norick, Barun Patra, Daniel Perez-Becker, Thomas Portet, Reid Pryzant, Heyang Qin, Marko Radmilac, Corby Rosset, Sambudha Roy, Olatunji Ruwase, Olli Saarikivi, Amin Saied, Adil Salim, Michael Santacroce, Shital Shah, Ning Shang, Hiteshi Sharma, Xia Song, Masahiro Tanaka, Xin Wang, Rachel Ward, Guanhua Wang, Philipp Witte, Michael Wyatt, Can Xu, Jiahang Xu, Sonali Yadav, Fan Yang, Ziyi Yang, Donghan Yu, Chengruidong Zhang, Cyril Zhang, Jianwen Zhang, Li Lyna Zhang, Yi Zhang, Yue Zhang, Yunan Zhang, Xiren Zhou:
Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone. CoRR abs/2404.14219 (2024) - 2023
- [c17]Dan Iter, Reid Pryzant, Ruochen Xu, Shuohang Wang, Yang Liu, Yichong Xu, Chenguang Zhu:
In-Context Demonstration Selection with Cross Entropy Difference. EMNLP (Findings) 2023: 1150-1162 - [c16]Siru Ouyang, Shuohang Wang, Yang Liu, Ming Zhong, Yizhu Jiao, Dan Iter, Reid Pryzant, Chenguang Zhu, Heng Ji, Jiawei Han:
The Shifted and The Overlooked: A Task-oriented Investigation of User-GPT Interactions. EMNLP 2023: 2375-2393 - [c15]Yang Liu, Dan Iter, Yichong Xu, Shuohang Wang, Ruochen Xu, Chenguang Zhu:
G-Eval: NLG Evaluation using Gpt-4 with Better Human Alignment. EMNLP 2023: 2511-2522 - [c14]Reid Pryzant, Dan Iter, Jerry Li, Yin Tat Lee, Chenguang Zhu, Michael Zeng:
Automatic Prompt Optimization with "Gradient Descent" and Beam Search. EMNLP 2023: 7957-7968 - [c13]Zhihan Zhang, Shuohang Wang, Wenhao Yu, Yichong Xu, Dan Iter, Qingkai Zeng, Yang Liu, Chenguang Zhu, Meng Jiang:
Auto-Instruct: Automatic Instruction Generation and Ranking for Black-Box Language Models. EMNLP (Findings) 2023: 9850-9867 - [c12]Yichong Xu, Ruochen Xu, Dan Iter, Yang Liu, Shuohang Wang, Chenguang Zhu, Michael Zeng:
InheritSumm: A General, Versatile and Compact Summarizer by Distilling from GPT. EMNLP (Findings) 2023: 13879-13892 - [c11]Ruochen Xu, Song Wang, Yang Liu, Shuohang Wang, Yichong Xu, Dan Iter, Pengcheng He, Chenguang Zhu, Michael Zeng:
LMGQS: A Large-scale Dataset for Query-focused Summarization. EMNLP (Findings) 2023: 14764-14776 - [c10]Wenhao Yu, Dan Iter, Shuohang Wang, Yichong Xu, Mingxuan Ju, Soumya Sanyal, Chenguang Zhu, Michael Zeng, Meng Jiang:
Generate rather than Retrieve: Large Language Models are Strong Context Generators. ICLR 2023 - [i14]Simeng Sun, Yang Liu, Dan Iter, Chenguang Zhu, Mohit Iyyer:
How Does In-Context Learning Help Prompt Tuning? CoRR abs/2302.11521 (2023) - [i13]Yang Liu, Dan Iter, Yichong Xu, Shuohang Wang, Ruochen Xu, Chenguang Zhu:
G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment. CoRR abs/2303.16634 (2023) - [i12]Reid Pryzant, Dan Iter, Jerry Li, Yin Tat Lee, Chenguang Zhu, Michael Zeng:
Automatic Prompt Optimization with "Gradient Descent" and Beam Search. CoRR abs/2305.03495 (2023) - [i11]Yichong Xu, Ruochen Xu, Dan Iter, Yang Liu, Shuohang Wang, Chenguang Zhu, Michael Zeng:
InheritSumm: A General, Versatile and Compact Summarizer by Distilling from GPT. CoRR abs/2305.13083 (2023) - [i10]Ruochen Xu, Song Wang, Yang Liu, Shuohang Wang, Yichong Xu, Dan Iter, Chenguang Zhu, Michael Zeng:
LMGQS: A Large-scale Dataset for Query-focused Summarization. CoRR abs/2305.13086 (2023) - [i9]Dan Iter, Reid Pryzant, Ruochen Xu, Shuohang Wang, Yang Liu, Yichong Xu, Chenguang Zhu:
In-Context Demonstration Selection with Cross Entropy Difference. CoRR abs/2305.14726 (2023) - [i8]Siru Ouyang, Shuohang Wang, Yang Liu, Ming Zhong, Yizhu Jiao, Dan Iter, Reid Pryzant, Chenguang Zhu, Heng Ji, Jiawei Han:
The Shifted and The Overlooked: A Task-oriented Investigation of User-GPT Interactions. CoRR abs/2310.12418 (2023) - [i7]Zhihan Zhang, Shuohang Wang, Wenhao Yu, Yichong Xu, Dan Iter, Qingkai Zeng, Yang Liu, Chenguang Zhu, Meng Jiang:
Auto-Instruct: Automatic Instruction Generation and Ranking for Black-Box Language Models. CoRR abs/2310.13127 (2023) - 2022
- [b1]Dan Iter:
Pre-finetuning methods for domain and task adaptation, with applications to discourse and translation. Stanford University, USA, 2022 - [c9]David Grangier, Dan Iter:
The Trade-offs of Domain Adaptation for Neural Language Models. ACL (1) 2022: 3802-3813 - [i6]Wenhao Yu, Dan Iter, Shuohang Wang, Yichong Xu, Mingxuan Ju, Soumya Sanyal, Chenguang Zhu, Michael Zeng, Meng Jiang:
Generate rather than Retrieve: Large Language Models are Strong Context Generators. CoRR abs/2209.10063 (2022) - 2021
- [c8]William Held
, Dan Iter, Dan Jurafsky:
Focus on what matters: Applying Discourse Coherence Theory to Cross Document Coreference. EMNLP (1) 2021: 1406-1417 - [i5]Dan Iter, David Grangier:
On the Complementarity of Data Selection and Fine Tuning for Domain Adaptation. CoRR abs/2109.07591 (2021) - [i4]Dan Iter, David Grangier:
The Trade-offs of Domain Adaptation for Neural Language Models. CoRR abs/2109.10274 (2021) - [i3]William Held, Dan Iter, Dan Jurafsky:
Focus on what matters: Applying Discourse Coherence Theory to Cross Document Coreference. CoRR abs/2110.05362 (2021) - 2020
- [c7]Dan Iter, Kelvin Guu, Larry Lansing, Dan Jurafsky:
Pretraining with Contrastive Sentence Objectives Improves Discourse Performance of Language Models. ACL 2020: 4859-4870 - [i2]Dan Iter, Kelvin Guu, Larry Lansing, Dan Jurafsky:
Pretraining with Contrastive Sentence Objectives Improves Discourse Performance of Language Models. CoRR abs/2005.10389 (2020)
2010 – 2019
- 2019
- [j1]Sadjad Fouladi, Francisco Romero, Dan Iter, Qian Li, Alex Ozdemir, Shuvo Chatterjee, Matei Zaharia, Christos Kozyrakis, Keith Winstein:
Outsourcing Everyday Jobs to Thousands of Cloud Functions with gg. login Usenix Mag. 44(3) (2019) - [c6]Sadjad Fouladi, Francisco Romero, Dan Iter, Qian Li, Shuvo Chatterjee, Christos Kozyrakis, Matei Zaharia, Keith Winstein:
From Laptop to Lambda: Outsourcing Everyday Jobs to Thousands of Transient Functional Containers. USENIX ATC 2019: 475-488 - 2018
- [c5]Dan Iter, Jong Yoon, Dan Jurafsky:
Automatic Detection of Incoherent Speech for Diagnosing Schizophrenia. CLPsych@NAACL-HTL 2018: 136-146 - [c4]Dan Iter, Alon Y. Halevy, Wang-Chiew Tan:
FrameIt: Ontology Discovery for Noisy User-Generated Text. NUT@EMNLP 2018: 173-183 - 2017
- [c3]Paroma Varma, Dan Iter, Christopher De Sa, Christopher Ré:
Flipper: A Systematic Approach to Debugging Training Sets. HILDA@SIGMOD 2017: 5:1-5:5 - 2016
- [i1]Rose Yu, Paroma Varma, Dan Iter, Christopher De Sa, Christopher Ré:
Socratic Learning. CoRR abs/1610.08123 (2016) - 2012
- [c2]Hila Becker, Dan Iter, Mor Naaman, Luis Gravano:
Identifying content for planned events across social media sites. WSDM 2012: 533-542 - 2011
- [c1]Hila Becker, Feiyang Chen, Dan Iter, Mor Naaman, Luis Gravano:
Automatic Identification and Presentation of Twitter Content for Planned Events. ICWSM 2011
Coauthor Index
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last updated on 2024-09-18 01:09 CEST by the dblp team
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