Search Results for author:
Found 15 papers, 10 papers with code
Exploring Demonstration Ensembling for In-context Learning
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
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
Planning is an important capability of artificial agents that perform long-horizon tasks in real-world environments.
Unsupervised Task Graph Generation from Instructional Video Transcripts
This work explores the problem of generating task graphs of real-world activities.
Multimodal Subtask Graph Generation from Instructional Videos
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).
Exploring the Benefits of Training Expert Language Models over Instruction Tuning
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
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.
Few-shot Subgoal Planning with Language Models
Pre-trained large language models have shown successful progress in many language understanding benchmarks.
Few-shot Reranking for Multi-hop QA via Language Model Prompting
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.
Few-shot Sequence Learning with Transformers
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.
Zero-Shot Entity Linking by Reading Entity Descriptions
First, we show that strong reading comprehension models pre-trained on large unlabeled data can be used to generalize to unseen entities.
Content preserving text generation with attribute controls
We propose an adversarial loss to enforce generated samples to be attribute compatible and realistic.
An efficient framework for learning sentence representations
In this work we propose a simple and efficient framework for learning sentence representations from unlabelled data.
Sentence Ordering and Coherence Modeling using Recurrent Neural Networks
Modeling the structure of coherent texts is a key NLP problem.
Generative Adversarial Text to Image Synthesis
Automatic synthesis of realistic images from text would be interesting and useful, but current AI systems are still far from this goal.
