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/ailab-kyunghee/kg-llava
공식 코드https://github.com/aimagelab/alfie
공식 코드https://github.com/aimagelab/hwd
공식 코드HWD: A Novel Evaluation Score for Styled Handwritten Text Generation
https://github.com/aimagelab/meshed-memory-transformer
공식 코드https://github.com/aimagelab/safe-clip
공식 코드Safe-CLIP: Removing NSFW Concepts from Vision-and-Language Models
https://github.com/aimagelab/vatr
공식 코드https://github.com/aiot-mlsys-lab/efficient-llms-survey
공식 코드https://github.com/aiot-mlsys-lab/efficientllms
공식 코드https://github.com/aiphes/discoscore
공식 코드DiscoScore: Evaluating Text Generation with BERT and Discourse Coherence
https://github.com/airesearch-group/neuralmark
공식 코드https://github.com/aisecure/AdvCodec
공식 코드T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted Attack
https://github.com/aistairc/market-reporter
공식 코드Prompting for Numerical Sequences: A Case Study on Market Comment Generation
https://github.com/aistairc/rotowire-modified
공식 코드https://github.com/aistairc/sports-reporter
공식 코드https://github.com/aitorormazabal/paraphrasing-from-parallel
공식 코드https://github.com/aix-group/ceval-counterfactual-generation-benchmark
공식 코드CEval: A Benchmark for Evaluating Counterfactual Text Generation
https://github.com/akabircs/belin
공식 코드BeliN: A Novel Corpus for Bengali Religious News Headline Generation using Contextual Feature Fusion
https://github.com/akalikong/ilora
공식 코드Customizing Language Models with Instance-wise LoRA for Sequential Recommendation
https://github.com/akoksal/longform
공식 코드LongForm: Effective Instruction Tuning with Reverse Instructions
https://github.com/akskuchi/dhm-visual-storytelling
공식 코드https://github.com/akskuchi/groovist
공식 코드GROOViST: A Metric for Grounding Objects in Visual Storytelling
https://github.com/alanagiasi/emoplmsynth
공식 코드https://github.com/alanaqrawi/stca
공식 코드Well, that escalated quickly: The Single-Turn Crescendo Attack (STCA)
https://github.com/albertkx/detoxifying-lms
공식 코드Detoxifying Language Models Risks Marginalizing Minority Voices
https://github.com/alex2awesome/source-exploration
공식 코드https://github.com/alexa/commonsense-dialogues
공식 코드Commonsense-Focused Dialogues for Response Generation: An Empirical Study
https://github.com/alexa/schema-guided-nlg
공식 코드https://github.com/alexanderswerdlow/unidisc
공식 코드https://github.com/alexandervnikitin/kernel-language-entropy
공식 코드Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similarities
https://github.com/alexandra-chron/umt-lmu-wmt2020
공식 코드The LMU Munich System for the WMT 2020 Unsupervised Machine Translation Shared Task
https://github.com/alexkoulakos/explain-then-predict
공식 코드Enhancing adversarial robustness in Natural Language Inference using explanations
https://github.com/alexlecu/llmkgraph
공식 코드https://github.com/alexverine/pr-4-llm
공식 코드Exploring Precision and Recall to assess the quality and diversity of LLMs
https://github.com/algoprog/icat
공식 코드https://github.com/algoprog/syntod
공식 코드Simulating Task-Oriented Dialogues with State Transition Graphs and Large Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.