https://github.com/ysw1021/scope
공식 코드Controlled Text Generation for Black-box Language Models via Score-based Progressive Editor
Papers with Code 아카이브
Text Generation
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- 평가표
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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/ythuang02/AppealCase
공식 코드AppealCase: A Dataset and Benchmark for Civil Case Appeal Scenarios
https://github.com/ytongxie/s4m
공식 코드Act Like a Radiologist: Radiology Report Generation across Anatomical Regions
https://github.com/ytongxie/x-rgen
공식 코드Act Like a Radiologist: Radiology Report Generation across Anatomical Regions
https://github.com/yu-fangxu/cold-attack
공식 코드COLD-Attack: Jailbreaking LLMs with Stealthiness and Controllability
https://github.com/yuanhy1997/seqdiffuseq
공식 코드SeqDiffuSeq: Text Diffusion with Encoder-Decoder Transformers
https://github.com/yueyu1030/ReGen
공식 코드ReGen: Zero-Shot Text Classification via Training Data Generation with Progressive Dense Retrieval
https://github.com/yuh-zha/alignscore
공식 코드AlignScore: Evaluating Factual Consistency with a Unified Alignment Function
https://github.com/yukw777/tglg
공식 코드Temporally-Grounded Language Generation: A Benchmark for Real-Time Vision-Language Models
https://github.com/yul091/dgslow
공식 코드White-Box Multi-Objective Adversarial Attack on Dialogue Generation
https://github.com/yuliangyan0807/llm-fingerprint
공식 코드DuFFin: A Dual-Level Fingerprinting Framework for LLMs IP Protection
https://github.com/yumeng5/supergen
공식 코드Generating Training Data with Language Models: Towards Zero-Shot Language Understanding
https://github.com/yunan-wang33/sdml
공식 코드Multimodal Fake News Detection: MFND Dataset and Shallow-Deep Multitask Learning
https://github.com/yunx-z/situated_gen
공식 코드SituatedGen: Incorporating Geographical and Temporal Contexts into Generative Commonsense Reasoning
https://github.com/yunzhusong/AAAI20-PORLHG
공식 코드Attractive or Faithful? Popularity-Reinforced Learning for Inspired Headline Generation
https://github.com/yunzhusong/NAACL2022-REFLECT
공식 코드Improving Multi-Document Summarization through Referenced Flexible Extraction with Credit-Awareness
https://github.com/yuweihao/mm-vet
공식 코드MM-Vet v2: A Challenging Benchmark to Evaluate Large Multimodal Models for Integrated Capabilities
https://github.com/yuxiaoleemarks/cma
공식 코드Concept Matching with Agent for Out-of-Distribution Detection
https://github.com/yx-lu/benchmarking-llms--judgments-with-no-gold-standard
공식 코드https://github.com/yxli2123/loftq
공식 코드LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models
https://github.com/yxuansu/NAG-BERT
공식 코드Non-Autoregressive Text Generation with Pre-trained Language Models
https://github.com/yxuansu/contrastive_search_is_what_you_need
공식 코드Contrastive Search Is What You Need For Neural Text Generation
https://github.com/yxuansu/contrastive_search_versus_contrastive_decoding
공식 코드An Empirical Study On Contrastive Search And Contrastive Decoding For Open-ended Text Generation
https://github.com/yxuansu/few-shot-table-to-text-generation
공식 코드https://github.com/yxuansu/magic
공식 코드Language Models Can See: Plugging Visual Controls in Text Generation
https://github.com/yxuansu/plangen
공식 코드Plan-then-Generate: Controlled Data-to-Text Generation via Planning
https://github.com/yxuansu/simctg
공식 코드https://github.com/yychai74/ld-vae
공식 코드Compositional Generalization for Multi-label Text Classification: A Data-Augmentation Approach
https://github.com/yzhihao/mcscset
공식 코드MCSCSet: A Specialist-annotated Dataset for Medical-domain Chinese Spelling Correction
https://github.com/yzhjessica/cdvqa
공식 코드https://github.com/yzpang/gold-off-policy-text-gen-iclr21
공식 코드https://github.com/zacharyhorvitz/tinystyler
공식 코드TinyStyler: Efficient Few-Shot Text Style Transfer with Authorship Embeddings
https://github.com/zake7749/WSDM-Cup-2019
공식 코드https://github.com/zalandoresearch/first_order_gan
공식 코드https://github.com/zanchangtong/csr4mbart
공식 코드https://github.com/zayne-sprague/deductive_additivity_for_planning_of_natural_language_proofs
공식 코드Deductive Additivity for Planning of Natural Language Proofs
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