Automatic Identification and Presentation of Twitter Content for Planned Events. - Hila Becker; Feiyang Chen; Dan Iter; Mor Naaman; Luis Gravano (ICWSM, 2011)

Identifying content for planned events across social media sites. - Hila Becker; Dan Iter; Mor Naaman; Luis Gravano (WSDM, 2012)

Socratic Learning. - Rose Yu; Paroma Varma; Dan Iter; Christopher De Sa; Christopher Ré (CoRR, 2016)

Flipper: A Systematic Approach to Debugging Training Sets. - Paroma Varma; Dan Iter; Christopher De Sa; Christopher Ré (HILDA@SIGMOD, 2017)

Automatic Detection of Incoherent Speech for Diagnosing Schizophrenia. - Dan Iter; Jong Yoon; Dan Jurafsky (CLPsych@NAACL-HTL, 2018)

FrameIt: Ontology Discovery for Noisy User-Generated Text. - Dan Iter; Alon Y. Halevy; Wang-Chiew Tan (NUT@EMNLP, 2018)

From Laptop to Lambda: Outsourcing Everyday Jobs to Thousands of Transient Functional Containers. - Sadjad Fouladi; Francisco Romero; Dan Iter; Qian Li 027; Shuvo Chatterjee; Christos Kozyrakis; Matei Zaharia; Keith Winstein (USENIX ATC, 2019)

Outsourcing Everyday Jobs to Thousands of Cloud Functions with gg. - Sadjad Fouladi; Francisco Romero; Dan Iter; Qian Li 027; Alex Ozdemir; Shuvo Chatterjee; Matei Zaharia; Christos Kozyrakis; Keith Winstein (login Usenix Mag., 2019)

Pretraining with Contrastive Sentence Objectives Improves Discourse Performance of Language Models. - Dan Iter; Kelvin Guu; Larry Lansing; Dan Jurafsky (ACL, 2020)

Focus on what matters: Applying Discourse Coherence Theory to Cross Document Coreference. - William Held; Dan Iter; Dan Jurafsky (EMNLP (1), 2021)

On the Complementarity of Data Selection and Fine Tuning for Domain Adaptation. - Dan Iter; David Grangier (CoRR, 2021)

The Trade-offs of Domain Adaptation for Neural Language Models. - David Grangier; Dan Iter (ACL (1), 2022)

Pre-finetuning methods for domain and task adaptation, with applications to discourse and translation. - Dan Iter (2022)

In-Context Demonstration Selection with Cross Entropy Difference. - Dan Iter; Reid Pryzant; Ruochen Xu; Shuohang Wang; Yang Liu; Yichong Xu; Chenguang Zhu 001 (EMNLP (Findings), 2023)

G-Eval: NLG Evaluation using Gpt-4 with Better Human Alignment. - Yang Liu; Dan Iter; Yichong Xu; Shuohang Wang; Ruochen Xu; Chenguang Zhu (EMNLP, 2023)

The Shifted and The Overlooked: A Task-oriented Investigation of User-GPT Interactions. - Siru Ouyang; Shuohang Wang; Yang Liu; Ming Zhong 005; Yizhu Jiao; Dan Iter; Reid Pryzant; Chenguang Zhu 001; Heng Ji 001; Jiawei Han 001 (EMNLP, 2023)

Automatic Prompt Optimization with "Gradient Descent" and Beam Search. - Reid Pryzant; Dan Iter; Jerry Li 001; Yin Tat Lee; Chenguang Zhu 001; Michael Zeng 001 (EMNLP, 2023)

LMGQS: A Large-scale Dataset for Query-focused Summarization. - Ruochen Xu; Song Wang 012; Yang Liu 124; Shuohang Wang; Yichong Xu; Dan Iter; Pengcheng He; Chenguang Zhu 001; Michael Zeng 001 (EMNLP (Findings), 2023)

InheritSumm: A General, Versatile and Compact Summarizer by Distilling from GPT. - Yichong Xu; Ruochen Xu; Dan Iter; Yang Liu 124; Shuohang Wang; Chenguang Zhu 001; Michael Zeng 001 (EMNLP (Findings), 2023)

Auto-Instruct: Automatic Instruction Generation and Ranking for Black-Box Language Models. - Zhihan Zhang 001; Shuohang Wang; Wenhao Yu 002; Yichong Xu; Dan Iter; Qingkai Zeng 001; Yang Liu 124; Chenguang Zhu 001; Meng Jiang 001 (EMNLP (Findings), 2023)

Generate rather than Retrieve: Large Language Models are Strong Context Generators. - Wenhao Yu 002; Dan Iter; Shuohang Wang; Yichong Xu; Mingxuan Ju; Soumya Sanyal 001; Chenguang Zhu 001; Michael Zeng 001; Meng Jiang 001 (ICLR, 2023)

G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment. - Yang Liu 124; Dan Iter; Yichong Xu; Shuohang Wang; Ruochen Xu; Chenguang Zhu 001 (CoRR, 2023)

LMGQS: A Large-scale Dataset for Query-focused Summarization. - Ruochen Xu; Song Wang 012; Yang Liu 124; Shuohang Wang; Yichong Xu; Dan Iter; Chenguang Zhu 001; Michael Zeng 001 (CoRR, 2023)

The Shifted and The Overlooked: A Task-oriented Investigation of User-GPT Interactions. - Siru Ouyang; Shuohang Wang; Yang Liu 124; Ming Zhong 005; Yizhu Jiao; Dan Iter; Reid Pryzant; Chenguang Zhu 001; Heng Ji 001; Jiawei Han 001 (CoRR, 2023)

How Does In-Context Learning Help Prompt Tuning? - Simeng Sun; Yang Liu 124; Dan Iter; Chenguang Zhu 001; Mohit Iyyer (EACL (Findings), 2024)

PEARL: Prompting Large Language Models to Plan and Execute Actions Over Long Documents. - Simeng Sun; Yang Liu 124; Shuohang Wang; Dan Iter; Chenguang Zhu 001; Mohit Iyyer (EACL (1), 2024)

Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone. - Marah I Abdin; Sam Ade Jacobs; Ammar Ahmad Awan; Jyoti Aneja; Ahmed Awadallah 001; Hany Awadalla; Nguyen Bach; Amit Bahree; Arash Bakhtiari; Harkirat S. Behl; Alon Benhaim; Misha Bilenko; et al. (CoRR, 2024)