Philippe Laban
Philippe Laban
Research Scientist, Salesforce AI Research
Verified email at - Homepage
Cited by
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SummaC: Re-visiting NLI-based models for inconsistency detection in summarization
P Laban, T Schnabel, PN Bennett, MA Hearst
Transactions of the Association for Computational Linguistics 10, 163-177, 2022
Understanding factual errors in summarization: Errors, summarizers, datasets, error detectors
L Tang, T Goyal, AR Fabbri, P Laban, J Xu, S Yavuz, W Kryściński, ...
arXiv preprint arXiv:2205.12854, 2022
The summary loop: Learning to write abstractive summaries without examples
P Laban, A Hsi, J Canny, MA Hearst
arXiv preprint arXiv:2105.05361, 2021
Keep it simple: Unsupervised simplification of multi-paragraph text
P Laban, T Schnabel, P Bennett, MA Hearst
arXiv preprint arXiv:2107.03444, 2021
Mixqg: Neural question generation with mixed answer types
L Murakhovs' ka, CS Wu, P Laban, T Niu, W Liu, C Xiong
arXiv preprint arXiv:2110.08175, 2021
Art or artifice? large language models and the false promise of creativity
T Chakrabarty, P Laban, D Agarwal, S Muresan, CS Wu
Proceedings of the CHI Conference on Human Factors in Computing Systems, 1-34, 2024
newsLens: building and visualizing long-ranging news stories
P Laban, MA Hearst
Proceedings of the Events and Stories in the News Workshop, 1-9, 2017
Can transformer models measure coherence in text? re-thinking the shuffle test
P Laban, L Dai, L Bandarkar, MA Hearst
arXiv preprint arXiv:2107.03448, 2021
What's the latest? A question-driven news chatbot
P Laban, J Canny, MA Hearst
arXiv preprint arXiv:2105.05392, 2021
Did you read the instructions? rethinking the effectiveness of task definitions in instruction learning
F Yin, J Vig, P Laban, S Joty, C Xiong, CSJ Wu
arXiv preprint arXiv:2306.01150, 2023
Llms as factual reasoners: Insights from existing benchmarks and beyond
P Laban, W Kryściński, D Agarwal, AR Fabbri, C Xiong, S Joty, CS Wu
arXiv preprint arXiv:2305.14540, 2023
Xgen-7b technical report
E Nijkamp, T Xie, H Hayashi, B Pang, C Xia, C Xing, J Vig, S Yavuz, ...
arXiv preprint arXiv:2309.03450, 2023
News headline grouping as a challenging nlu task
P Laban, L Bandarkar, MA Hearst
arXiv preprint arXiv:2105.05391, 2021
Quiz design task: Helping teachers create quizzes with automated question generation
P Laban, CS Wu, L Murakhovs' ka, W Liu, C Xiong
arXiv preprint arXiv:2205.01730, 2022
SUMMEDITS: measuring LLM ability at factual reasoning through the lens of summarization
P Laban, W Kryściński, D Agarwal, AR Fabbri, C Xiong, S Joty, CS Wu
Proceedings of the 2023 Conference on Empirical Methods in Natural Language …, 2023
Newspod: Automatic and interactive news podcasts
P Laban, E Ye, S Korlakunta, J Canny, M Hearst
Proceedings of the 27th International Conference on Intelligent User …, 2022
Embrace divergence for richer insights: A multi-document summarization benchmark and a case study on summarizing diverse information from news articles
KH Huang, P Laban, AR Fabbri, PK Choubey, S Joty, C Xiong, CS Wu
arXiv preprint arXiv:2309.09369, 2023
Long sequence modeling with xgen: A 7b llm trained on 8k input sequence length
E Nijkamp, T Xie, H Hayashi, B Pang, C Xia, C Xing, J Vig, S Yavuz, ...
Salesforce AI Research Blog, 2023
MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents
L Tang, P Laban, G Durrett
arXiv preprint arXiv:2404.10774, 2024
Are you sure? challenging llms leads to performance drops in the flipflop experiment
P Laban, L Murakhovs' ka, C Xiong, CS Wu
arXiv preprint arXiv:2311.08596, 2023
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