Search Results for author:
Found 21 papers, 11 papers with code
Traffic Light or Light Traffic? Investigating Phrasal Semantics in Large Language Models
Phrases are fundamental linguistic units through which humans convey semantics.
xGen-VideoSyn-1: High-fidelity Text-to-Video Synthesis with Compressed Representations
We present xGen-VideoSyn-1, a text-to-video (T2V) generation model capable of producing realistic scenes from textual descriptions.
Unlocking Anticipatory Text Generation: A Constrained Approach for Large Language Models Decoding
Large Language Models (LLMs) have demonstrated a powerful ability for text generation.
XGen-7B Technical Report
Most open-source LLMs, on the other hand, are limited in their ability to support longer sequence lengths, which is a key requirement for many tasks that require inference over an input context.
Efficiently Aligned Cross-Lingual Transfer Learning for Conversational Tasks using Prompt-Tuning
Our results demonstrate the strong and efficient modeling ability of NLI-based classifiers and the large cross-lingual transfer improvements achieved by our aligned prompts, particularly in few-shot settings.
AugTriever: Unsupervised Dense Retrieval by Scalable Data Augmentation
In this study, we aim to develop unsupervised methods for improving dense retrieval models.
Prompt-Tuning Can Be Much Better Than Fine-Tuning on Cross-lingual Understanding With Multilingual Language Models
Pre-trained multilingual language models show significant performance gains for zero-shot cross-lingual model transfer on a wide range of natural language understanding (NLU) tasks.
CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis
To democratize this, we train and release a family of large language models up to 16. 1B parameters, called CODEGEN, on natural language and programming language data, and open source the training library JAXFORMER.
Ranked #96 on
Code Generation
on HumanEval
Learning Energy-Based Approximate Inference Networks for Structured Applications in NLP
In this dissertation, we discuss the concept of the energy function and structured models with different energy functions.
An Exploration of Arbitrary-Order Sequence Labeling via Energy-Based Inference Networks
Many tasks in natural language processing involve predicting structured outputs, e. g., sequence labeling, semantic role labeling, parsing, and machine translation.
An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language Models
Recent work has shown that pre-trained language models such as BERT improve robustness to spurious correlations in the dataset.
ENGINE: Energy-Based Inference Networks for Non-Autoregressive Machine Translation
We propose to train a non-autoregressive machine translation model to minimize the energy defined by a pretrained autoregressive model.
Improving Joint Training of Inference Networks and Structured Prediction Energy Networks
Deep energy-based models are powerful, but pose challenges for learning and inference (Belanger and McCallum, 2016).
Generating Diverse Story Continuations with Controllable Semantics
We propose a simple and effective modeling framework for controlled generation of multiple, diverse outputs.
Benchmarking Approximate Inference Methods for Neural Structured Prediction
One approach is to perform gradient descent with respect to the output structure directly (Belanger and McCallum, 2016).
Quality Signals in Generated Stories
We study the problem of measuring the quality of automatically-generated stories.
Learning Approximate Inference Networks for Structured Prediction
Prior work used gradient descent for inference, relaxing the structured output to a set of continuous variables and then optimizing the energy with respect to them.
Pay Attention to the Ending:Strong Neural Baselines for the ROC Story Cloze Task
We consider the ROC story cloze task (Mostafazadeh et al., 2016) and present several findings.
Learning to Embed Words in Context for Syntactic Tasks
We present models for embedding words in the context of surrounding words.
Network Inference by Learned Node-Specific Degree Prior
We propose a novel method for network inference from partially observed edges using a node-specific degree prior.
