https://github.com/derekkk/mocha-emnlp22
공식 코드MOCHA: A Multi-Task Training Approach for Coherent Text Generation from Cognitive Perspective
Papers with Code 아카이브
Text Generation
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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:
<span style="color:grey; opacity: 0.6">( Image credit: Adversarial Ranking for Language Generation )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/dersuchendee/knowledge-augmented-generation
공식 코드Enhancing multimodal analogical reasoning with Logic Augmented Generation
https://github.com/dev-chauhan/PQG-pytorch
공식 코드Revisiting Paraphrase Question Generator using Pairwise Discriminator
https://github.com/dhfbk/variationist
공식 코드Variationist: Exploring Multifaceted Variation and Bias in Written Language Data
https://github.com/dhg-wei/decap
공식 코드DeCap: Decoding CLIP Latents for Zero-Shot Captioning via Text-Only Training
https://github.com/dialogue-evaluation/Russian-News-Clustering-and-Headline-Generation
공식 코드https://github.com/dialogue-evaluation/ruatd
공식 코드Findings of the The RuATD Shared Task 2022 on Artificial Text Detection in Russian
https://github.com/dianaglzrico/neural-visual-storyteller
공식 코드https://github.com/diandyu/attribute_alignment
공식 코드Attribute Alignment: Controlling Text Generation from Pre-trained Language Models
https://github.com/dice-group/lola
공식 코드LOLA -- An Open-Source Massively Multilingual Large Language Model
https://github.com/diegma/graph-2-text
공식 코드Deep Graph Convolutional Encoders for Structured Data to Text Generation
https://github.com/digo/steering-response-style-and-topic
공식 코드Steering Output Style and Topic in Neural Response Generation
https://github.com/dipta007/semeval24-task8
공식 코드https://github.com/divyanshuaggarwal/indic-semparse
공식 코드Evaluating Inter-Bilingual Semantic Parsing for Indian Languages
https://github.com/dkalpakchi/swectrl-mini
공식 코드https://github.com/dkalpakchi/swequad-mc
공식 코드https://github.com/dlr-sc/style-vectors-for-steering-llms
공식 코드https://github.com/dm2-nd/edmem
공식 코드https://github.com/dmg-illc/nlg-uncertainty-probes
공식 코드https://github.com/dmis-lab/olaph
공식 코드OLAPH: Improving Factuality in Biomedical Long-form Question Answering
https://github.com/doctordream/mdia
공식 코드MDIA: A Benchmark for Multilingual Dialogue Generation in 46 Languages
https://github.com/domenicrosati/longform-evaluation-model-editing
공식 코드https://github.com/domenicrosati/synscipass
공식 코드SynSciPass: detecting appropriate uses of scientific text generation
https://github.com/donahowe/theatergen
공식 코드TheaterGen: Character Management with LLM for Consistent Multi-turn Image Generation
https://github.com/dong-river/corrpus
공식 코드CoRRPUS: Code-based Structured Prompting for Neurosymbolic Story Understanding
https://github.com/dortsur/heavywater_simplexwater
공식 코드HeavyWater and SimplexWater: Watermarking Low-Entropy Text Distributions
https://github.com/dpfried/pragmatic-instructions
공식 코드Unified Pragmatic Models for Generating and Following Instructions
https://github.com/dqxiu/ParaSCI
공식 코드ParaSCI: A Large Scientific Paraphrase Dataset for Longer Paraphrase Generation
https://github.com/dreasysnail/INSET
공식 코드https://github.com/dreasysnail/POINTER
공식 코드POINTER: Constrained Progressive Text Generation via Insertion-based Generative Pre-training
https://github.com/dreasysnail/RetGen
공식 코드RetGen: A Joint framework for Retrieval and Grounded Text Generation Modeling
https://github.com/drivetosouth/safedialbench-dataset
공식 코드https://github.com/drndr/bert_ent_attack
공식 코드https://github.com/drsy/pins
공식 코드Pruning Pre-trained Language Models with Principled Importance and Self-regularization
https://github.com/duyguceylan/pix2video
공식 코드https://github.com/dvlab-research/LLMGA
공식 코드LLMGA: Multimodal Large Language Model based Generation Assistant
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.