1 code implementation • 24 Jan 2024 • Jing Yu Koh, Robert Lo, Lawrence Jang, Vikram Duvvur, Ming Chong Lim, Po-Yu Huang, Graham Neubig, Shuyan Zhou, Ruslan Salakhutdinov, Daniel Fried
Through extensive quantitative and qualitative analysis, we identify several limitations of text-only LLM agents, and reveal gaps in the capabilities of state-of-the-art multimodal language agents.
1 code implementation • 25 Jul 2023 • Shuyan Zhou, Frank F. Xu, Hao Zhu, Xuhui Zhou, Robert Lo, Abishek Sridhar, Xianyi Cheng, Tianyue Ou, Yonatan Bisk, Daniel Fried, Uri Alon, Graham Neubig
Building upon our environment, we release a set of benchmark tasks focusing on evaluating the functional correctness of task completions.
1 code implementation • 5 Jun 2023 • Andrew Stange, Robert Lo, Abishek Sridhar, Kousik Rajesh
In this project we attempt to make slot-based models with an image reconstruction objective competitive with those that use a feature reconstruction objective on real world datasets.
no code implementations • 2 Jun 2023 • Robert Lo, Arnhav Datar, Abishek Sridhar
Deep generative models for Natural Language data offer a new angle on the problem of graph synthesis: by optimizing differentiable models that directly generate graphs, it is possible to side-step expensive search procedures in the discrete and vast space of possible graphs.
3 code implementations • 23 May 2023 • Abishek Sridhar, Robert Lo, Frank F. Xu, Hao Zhu, Shuyan Zhou
Large language models (LLMs) struggle on processing complicated observations in interactive decision making tasks.