no code implementations • 23 Feb 2024 • Kinjal Basu, Ibrahim Abdelaziz, Subhajit Chaudhury, Soham Dan, Maxwell Crouse, Asim Munawar, Sadhana Kumaravel, Vinod Muthusamy, Pavan Kapanipathi, Luis A. Lastras
There is a growing need for Large Language Models (LLMs) to effectively use tools and external Application Programming Interfaces (APIs) to plan and complete tasks.
1 code implementation • 20 May 2023 • Yatin Nandwani, Vineet Kumar, Dinesh Raghu, Sachindra Joshi, Luis A. Lastras
PMI quantifies the extent to which the document influences the generated response -- with a higher PMI indicating a more faithful response.
no code implementations • SIGDIAL (ACL) 2022 • Qingyang Wu, Song Feng, Derek Chen, Sachindra Joshi, Luis A. Lastras, Zhou Yu
Collecting data for training dialog systems can be extremely expensive due to the involvement of human participants and need for extensive annotation.
2 code implementations • EMNLP 2020 • Song Feng, Hui Wan, Chulaka Gunasekara, Siva Sankalp Patel, Sachindra Joshi, Luis A. Lastras
We introduce doc2dial, a new dataset of goal-oriented dialogues that are grounded in the associated documents.
no code implementations • 24 Jun 2020 • Luis A. Lastras
In this article we introduce theory and algorithms for learning discrete representations that take on a lattice that is embedded in an Euclidean space.
no code implementations • NeurIPS Workshop Document_Intelligen 2019 • Song Feng, Kshitij Fadni, Q. Vera Liao, Luis A. Lastras
We introduce Doc2Dial, an end-to-end framework for generating conversational data grounded in business documents via crowdsourcing.
no code implementations • 12 Apr 2019 • Luis A. Lastras
One way to address this question is to find an upper bound on the probability (equivalently a lower bound on the negative log likelihood) that the model can assign to some data as one varies the prior and/or the likelihood function in a latent variable model.