Text Generation
1495 papers with code • 21 benchmarks • 150 datasets
Text Generation is the task of generating text with the goal of appearing indistinguishable to human-written text. This task is more formally known as "natural language generation" in the literature.
Text generation can be addressed with Markov processes or deep generative models like LSTMs. Recently, some of the most advanced methods for text generation include BART, GPT and other GAN-based approaches. Text generation systems are evaluated either through human ratings or automatic evaluation metrics like METEOR, ROUGE, and BLEU.
Further readings:
( Image credit: Adversarial Ranking for Language Generation )
Benchmarks
These leaderboards are used to track progress in Text Generation
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Libraries
Use these libraries to find Text Generation models and implementations
GLUE
SST
MultiNLI
QNLI
WikiText-2
MRPC
MMLU
GSM8K
TriviaQA
CoLA
Subtasks
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Dialogue Generation
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Data-to-Text Generation
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Multi-Document Summarization
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Text Style Transfer
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Text Style Transfer
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Story Generation
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Paraphrase Generation
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Spelling Correction
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Table-to-Text Generation
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Headline Generation
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Conditional Text Generation
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Visual Storytelling
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Text Infilling
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Distractor Generation
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News Generation
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Question-Answer-Generation
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Story Completion
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Code Documentation Generation
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Concept-To-Text Generation
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Paper generation
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Sonnet Generation
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Profile Generation
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Fact-based Text Editing
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Rules-of-thumb Generation
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Molecular description generation
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Natural Language Landmark Navigation Instructions Generation
Latest papers with no code
Related Work and Citation Text Generation: A Survey
To convince readers of the novelty of their research paper, authors must perform a literature review and compose a coherent story that connects and relates prior works to the current work.
A Survey on Retrieval-Augmented Text Generation for Large Language Models
Retrieval-Augmented Generation (RAG) merges retrieval methods with deep learning advancements to address the static limitations of large language models (LLMs) by enabling the dynamic integration of up-to-date external information.
Modeling Low-Resource Health Coaching Dialogues via Neuro-Symbolic Goal Summarization and Text-Units-Text Generation
Health coaching helps patients achieve personalized and lifestyle-related goals, effectively managing chronic conditions and alleviating mental health issues.
Generative Text Steganography with Large Language Model
In this paper, we explore a black-box generative text steganographic method based on the user interfaces of large language models, which is called LLM-Stega.
KG-CTG: Citation Generation through Knowledge Graph-guided Large Language Models
Citation Text Generation (CTG) is a task in natural language processing (NLP) that aims to produce text that accurately cites or references a cited document within a source document.
Unveiling LLM Evaluation Focused on Metrics: Challenges and Solutions
The overarching goal is to furnish researchers with a pragmatic guide for effective LLM evaluation and metric selection, thereby advancing the understanding and application of these large language models.
Gaining More Insight into Neural Semantic Parsing with Challenging Benchmarks
The Parallel Meaning Bank (PMB) serves as a corpus for semantic processing with a focus on semantic parsing and text generation.
Language Generation in the Limit
A computational agent is trying to learn to generate from this language; we say that the agent generates from L in the limit if after some finite point in the enumeration of L, the agent is able to produce new elements that come exclusively from L and that have not yet been presented by the adversary.
GraSAME: Injecting Token-Level Structural Information to Pretrained Language Models via Graph-guided Self-Attention Mechanism
Pretrained Language Models (PLMs) benefit from external knowledge stored in graph structures for various downstream tasks.
Less is More for Improving Automatic Evaluation of Factual Consistency
Assessing the factual consistency of automatically generated texts in relation to source context is crucial for developing reliable natural language generation applications.
