Search Results for author: Lajanugen Logeswaran

Found 15 papers, 10 papers with code

Exploring Demonstration Ensembling for In-context Learning

1 code implementation17 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.

Discriminator-Guided Multi-step Reasoning with Language Models

1 code implementation24 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.

A Picture is Worth a Thousand Words: Language Models Plan from Pixels

no code implementations16 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.

Multimodal Subtask Graph Generation from Instructional Videos

no code implementations17 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).

Graph Generation

Exploring the Benefits of Training Expert Language Models over Instruction Tuning

1 code implementation7 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.

Knowledge Unlearning for Mitigating Privacy Risks in Language Models

1 code implementation4 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.

Language Modelling

Few-shot Reranking for Multi-hop QA via Language Model Prompting

2 code implementations25 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.

Open-Domain Question Answering Passage Re-Ranking +2

Few-shot Sequence Learning with Transformers

no code implementations17 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.

Few-Shot Learning

Zero-Shot Entity Linking by Reading Entity Descriptions

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.

Entity Linking Reading Comprehension

An efficient framework for learning sentence representations

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.

General Classification Representation Learning

Generative Adversarial Text to Image Synthesis

40 code implementations17 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.

Adversarial Text

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