Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry

Autor: Zimmermann, Yoel, Bazgir, Adib, Afzal, Zartashia, Agbere, Fariha, Ai, Qianxiang, Alampara, Nawaf, Al-Feghali, Alexander, Ansari, Mehrad, Antypov, Dmytro, Aswad, Amro, Bai, Jiaru, Baibakova, Viktoriia, Biswajeet, Devi Dutta, Bitzek, Erik, Bocarsly, Joshua D., Borisova, Anna, Bran, Andres M, Brinson, L. Catherine, Calderon, Marcel Moran, Canalicchio, Alessandro, Chen, Victor, Chiang, Yuan, Circi, Defne, Charmes, Benjamin, Chaudhary, Vikrant, Chen, Zizhang, Chiu, Min-Hsueh, Clymo, Judith, Dabhadkar, Kedar, Daelman, Nathan, Datar, Archit, Evans, Matthew L., Fard, Maryam Ghazizade, Fisicaro, Giuseppe, Gangan, Abhijeet Sadashiv, George, Janine, Gonzalez, Jose D. Cojal, Götte, Michael, Gupta, Ankur K., Harb, Hassan, Hong, Pengyu, Ibrahim, Abdelrahman, Ilyas, Ahmed, Imran, Alishba, Ishimwe, Kevin, Issa, Ramsey, Jablonka, Kevin Maik, Jones, Colin, Josephson, Tyler R., Juhasz, Greg, Kapoor, Sarthak, Kang, Rongda, Khalighinejad, Ghazal, Khan, Sartaaj, Klawohn, Sascha, Kuman, Suneel, Ladines, Alvin Noe, Leang, Sarom, Lederbauer, Magdalena, Liao, Sheng-Lun Mark, Liu, Hao, Liu, Xuefeng, Lo, Stanley, Madireddy, Sandeep, Maharana, Piyush Ranjan, Maheshwari, Shagun, Mahjoubi, Soroush, Márquez, José A., Mills, Rob, Mohanty, Trupti, Mohr, Bernadette, Moosavi, Seyed Mohamad, Moßhammer, Alexander, Naghdi, Amirhossein D., Naik, Aakash, Narykov, Oleksandr, Näsström, Hampus, Nguyen, Xuan Vu, Ni, Xinyi, O'Connor, Dana, Olayiwola, Teslim, Ottomano, Federico, Ozhan, Aleyna Beste, Pagel, Sebastian, Parida, Chiku, Park, Jaehee, Patel, Vraj, Patyukova, Elena, Petersen, Martin Hoffmann, Pinto, Luis, Pizarro, José M., Plessers, Dieter, Pradhan, Tapashree, Pratiush, Utkarsh, Puli, Charishma, Qin, Andrew, Rajabi, Mahyar, Ricci, Francesco, Risch, Elliot, Ríos-García, Martiño, Roy, Aritra, Rug, Tehseen, Sayeed, Hasan M, Scheidgen, Markus, Schilling-Wilhelmi, Mara, Schloz, Marcel, Schöppach, Fabian, Schumann, Julia, Schwaller, Philippe, Schwarting, Marcus, Sharlin, Samiha, Shen, Kevin, Shi, Jiale, Si, Pradip, D'Souza, Jennifer, Sparks, Taylor, Sudhakar, Suraj, Talirz, Leopold, Tang, Dandan, Taran, Olga, Terboven, Carla, Tropin, Mark, Tsymbal, Anastasiia, Ueltzen, Katharina, Unzueta, Pablo Andres, Vasan, Archit, Vinchurkar, Tirtha, Vo, Trung, Vogel, Gabriel, Völker, Christoph, Weinreich, Jan, Yang, Faradawn, Zaki, Mohd, Zhang, Chi, Zhang, Sylvester, Zhang, Weijie, Zhu, Ruijie, Zhu, Shang, Janssen, Jan, Foster, Ian, Blaiszik, Ben

Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hybrid locations, resulting in 34 team submissions. The submissions spann

Externí odkaz: http://arxiv.org/abs/2411.15221

1-2-3-Go! Policy Synthesis for Parameterized Markov Decision Processes via Decision-Tree Learning and Generalization

Autor: Azeem, Muqsit, Chakraborty, Debraj, Kanav, Sudeep, Kretinsky, Jan, Mohagheghi, Mohammadsadegh, Mohr, Stefanie, Weininger, Maximilian

Despite the advances in probabilistic model checking, the scalability of the verification methods remains limited. In particular, the state space often becomes extremely large when instantiating parameterized Markov decision processes (MDPs) even wit

Externí odkaz: http://arxiv.org/abs/2410.18293

'It Explains What I am Currently Going Through Perfectly to a Tee': Understanding User Perceptions on LLM-Enhanced Narrative Interventions

Autor: Bhattacharjee, Ananya, Xu, Sarah Yi, Rao, Pranav, Zeng, Yuchen, Meyerhoff, Jonah, Ahmed, Syed Ishtiaque, Mohr, David C, Liut, Michael, Mariakakis, Alex, Kornfield, Rachel, Williams, Joseph Jay

Stories about overcoming personal struggles can effectively illustrate the application of psychological theories in real life, yet they may fail to resonate with individuals' experiences. In this work, we employ large language models (LLMs) to create

Externí odkaz: http://arxiv.org/abs/2409.16732

Constraints on $f(R)$ gravity from tSZE-selected SPT galaxy clusters and weak lensing mass calibration from DES and HST

Autor: Vogt, S. M. L., Bocquet, S., Davies, C. T., Mohr, J. J., Schmidt, F., Ruan, C. -Z., Li, B., Hernández-Aguayo, C., Grandis, S., Bleem, L. E., Klein, M., Schrabback, T., Aguena, M., Brooks, D., Burke, D. L., Campos, A., Rosell, A. Carnero, Carretero, J., Costanzi, M., da Costa, L. N., Pereira, M. E. S., De Vicente, J., Doel, P., Everett, S., Ferrero, I., Frieman, J., García-Bellido, J., Gatti, M., Giannini, G., Gruen, D., Gruendl, R. A., Hinton, S. R., Hollowood, D. L., Lee, S., Lima, M., Marshall, J. L., Mena-Fernández, J., Miquel, R., Myles, J., Paterno, M., Pieres, A., Malagón, A. A. Plazas, Reichardt, C. L., Romer, A. K., Samuroff, S., Sarkar, A., Sanchez, E., Sevilla-Noarbe, I., Smith, M., Suchyta, E., Swanson, M. E. C., Tarle, G., Vikram, V., Weaverdyck, N., Weller, J.

We present constraints on the $f(R)$ gravity model using a sample of 1,005 galaxy clusters in the redshift range $0.25 - 1.78$ that have been selected through the thermal Sunyaev-Zel'dovich effect (tSZE) from South Pole Telescope (SPT) data and subje

Externí odkaz: http://arxiv.org/abs/2409.13556

jina-embeddings-v3: Multilingual Embeddings With Task LoRA

Autor: Sturua, Saba, Mohr, Isabelle, Akram, Mohammad Kalim, Günther, Michael, Wang, Bo, Krimmel, Markus, Wang, Feng, Mastrapas, Georgios, Koukounas, Andreas, Wang, Nan, Xiao, Han

We introduce jina-embeddings-v3, a novel text embedding model with 570 million parameters, achieves state-of-the-art performance on multilingual data and long-context retrieval tasks, supporting context lengths of up to 8192 tokens. The model include

Externí odkaz: http://arxiv.org/abs/2409.10173