1 code implementation • 17 Aug 2023 • Muhammad Khalifa, Lajanugen Logeswaran, Moontae Lee, Honglak Lee, Lu Wang
The standard approach for ICL is to prompt the LM with concatenated demonstrations followed by the test input.
1 code implementation • 24 May 2023 • Muhammad Khalifa, Lajanugen Logeswaran, Moontae Lee, Honglak Lee, Lu Wang
In the context of multi-step reasoning, language models (LMs) probabilities are often miscalibrated -- solutions with high probabilities are not always correct.
no code implementations • 16 Mar 2023 • Anthony Z. Liu, Lajanugen Logeswaran, Sungryull Sohn, Honglak Lee
Planning is an important capability of artificial agents that perform long-horizon tasks in real-world environments.
no code implementations • 17 Feb 2023 • Lajanugen Logeswaran, Sungryull Sohn, Yunseok Jang, Moontae Lee, Honglak Lee
This work explores the problem of generating task graphs of real-world activities.
no code implementations • 17 Feb 2023 • Yunseok Jang, Sungryull Sohn, Lajanugen Logeswaran, Tiange Luo, Moontae Lee, Honglak Lee
Real-world tasks consist of multiple inter-dependent subtasks (e. g., a dirty pan needs to be washed before it can be used for cooking).
1 code implementation • 7 Feb 2023 • Joel Jang, Seungone Kim, Seonghyeon Ye, Doyoung Kim, Lajanugen Logeswaran, Moontae Lee, Kyungjae Lee, Minjoon Seo
Recently, Language Models (LMs) instruction-tuned on multiple tasks, also known as multitask-prompted fine-tuning (MT), have shown the capability to generalize to unseen tasks.
1 code implementation • 4 Oct 2022 • Joel Jang, Dongkeun Yoon, Sohee Yang, Sungmin Cha, Moontae Lee, Lajanugen Logeswaran, Minjoon Seo
Pretrained Language Models (LMs) memorize a vast amount of knowledge during initial pretraining, including information that may violate the privacy of personal lives and identities.
no code implementations • NAACL 2022 • Lajanugen Logeswaran, Yao Fu, Moontae Lee, Honglak Lee
Pre-trained large language models have shown successful progress in many language understanding benchmarks.
2 code implementations • 25 May 2022 • Muhammad Khalifa, Lajanugen Logeswaran, Moontae Lee, Honglak Lee, Lu Wang
To alleviate the need for a large number of labeled question-document pairs for retriever training, we propose PromptRank, which relies on large language models prompting for multi-hop path reranking.
no code implementations • 17 Dec 2020 • Lajanugen Logeswaran, Ann Lee, Myle Ott, Honglak Lee, Marc'Aurelio Ranzato, Arthur Szlam
In the simplest setting, we append a token to an input sequence which represents the particular task to be undertaken, and show that the embedding of this token can be optimized on the fly given few labeled examples.
3 code implementations • ACL 2019 • Lajanugen Logeswaran, Ming-Wei Chang, Kenton Lee, Kristina Toutanova, Jacob Devlin, Honglak Lee
First, we show that strong reading comprehension models pre-trained on large unlabeled data can be used to generalize to unseen entities.
1 code implementation • NeurIPS 2018 • Lajanugen Logeswaran, Honglak Lee, Samy Bengio
We propose an adversarial loss to enforce generated samples to be attribute compatible and realistic.
6 code implementations • ICLR 2018 • Lajanugen Logeswaran, Honglak Lee
In this work we propose a simple and efficient framework for learning sentence representations from unlabelled data.
2 code implementations • 8 Nov 2016 • Lajanugen Logeswaran, Honglak Lee, Dragomir Radev
Modeling the structure of coherent texts is a key NLP problem.
40 code implementations • 17 May 2016 • Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, Honglak Lee
Automatic synthesis of realistic images from text would be interesting and useful, but current AI systems are still far from this goal.