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Found 24 papers, 9 papers with code
LLM360: Towards Fully Transparent Open-Source LLMs
The recent surge in open-source Large Language Models (LLMs), such as LLaMA, Falcon, and Mistral, provides diverse options for AI practitioners and researchers.
SegMix: A Simple Structure-Aware Data Augmentation Method
We argue that the direct-adoption methods do not account for structures in NLP tasks.
SlimPajama-DC: Understanding Data Combinations for LLM Training
This paper aims to understand the impacts of various data combinations (e. g., web text, wikipedia, github, books) on the training of large language models using SlimPajama.
Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models
We release two open versions of the model -- the foundation Jais model, and an instruction-tuned Jais-chat variant -- with the aim of promoting research on Arabic LLMs.
ASDOT: Any-Shot Data-to-Text Generation with Pretrained Language Models
In the data disambiguation stage, we employ the prompted GPT-3 model to understand possibly ambiguous triples from the input data and convert each into a short sentence with reduced ambiguity.
Text Generation with Efficient (Soft) $Q$-Learning
We apply the approach to a wide range of text generation tasks, including learning from noisy/negative examples, adversarial attacks, and prompt generation.
Compression, Transduction, and Creation: A Unified Framework for Evaluating Natural Language Generation
Based on the nature of information change from input to output, we classify NLG tasks into compression (e. g., summarization), transduction (e. g., text rewriting), and creation (e. g., dialog).
Cross-document Event Identity via Dense Annotation
To this end, we design a new annotation workflow with careful quality control and an easy-to-use annotation interface.
Efficient (Soft) Q-Learning for Text Generation with Limited Good Data
We apply the approach to a wide range of novel text generation tasks, including learning from noisy/negative examples, adversarial attacks, and prompt generation.
A Data-Centric Framework for Composable NLP Workflows
Empirical natural language processing (NLP) systems in application domains (e. g., healthcare, finance, education) involve interoperation among multiple components, ranging from data ingestion, human annotation, to text retrieval, analysis, generation, and visualization.
A Two-Step Approach for Implicit Event Argument Detection
It remains a challenge to detect implicit arguments, calling for more future work of document-level modeling for this task.
Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation
The versatile toolkit also fosters technique sharing across different text generation tasks.
Automatic Event Salience Identification
Our analyses demonstrate that our neural model captures interesting connections between salience and discourse unit relations (e. g., scripts and frame structures).
Graph Based Decoding for Event Sequencing and Coreference Resolution
In this paper, we study two types of relation: Event Coreference and Event Sequencing.
Texar: A Modularized, Versatile, and Extensible Toolbox for Text Generation
The features make Texar particularly suitable for technique sharing and generalization across different text generation applications.
Graph-Based Decoding for Event Sequencing and Coreference Resolution
In this paper, we study two types of relation: Event Coreference and Event Sequencing.
Towards Better Text Understanding and Retrieval through Kernel Entity Salience Modeling
The salience model also improves ad hoc search accuracy, providing effective ranking features by modeling the salience of query entities in candidate documents.
Harnessing Deep Neural Networks with Logic Rules
Combining deep neural networks with structured logic rules is desirable to harness flexibility and reduce uninterpretability of the neural models.
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Unsupervised Ranking Model for Entity Coreference Resolution
Coreference resolution is one of the first stages in deep language understanding and its importance has been well recognized in the natural language processing community.
Supervised Within-Document Event Coreference using Information Propagation
Event coreference is an important task for full text analysis.
Detecting Subevent Structure for Event Coreference Resolution
First, we introduce a multiclass logistic regression model that can detect subevent relations in addition to full coreference.
