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/ictnlp/truthx
공식 코드TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful Space
https://github.com/idiap/drill
공식 코드https://github.com/idiap/sdialog
공식 코드SDialog: A Python Toolkit for Synthetic Dialogue Generation and Analysis
https://github.com/ieit-agi/mix-shannon
공식 코드https://github.com/iesl/interactive_LM
공식 코드Changing the Mind of Transformers for Topically-Controllable Language Generation
https://github.com/igarashiakatuki/cnmbert
공식 코드CNMBERT: A Model for Converting Hanyu Pinyin Abbreviations to Chinese Characters
https://github.com/iinemo/lm-polygraph
공식 코드Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph
https://github.com/iiosnail/nambert
공식 코드Unveiling the Impact of Multimodal Features on Chinese Spelling Correction: From Analysis to Design
https://github.com/iitmnlp/evaleval
공식 코드Perturbation CheckLists for Evaluating NLG Evaluation Metrics
https://github.com/imkett/ctg-latentaes
공식 코드An Overview on Controllable Text Generation via Variational Auto-Encoders
https://github.com/imkett/pcae
공식 코드PCAE: A Framework of Plug-in Conditional Auto-Encoder for Controllable Text Generation
https://github.com/imohammad12/grs
공식 코드GRS: Combining Generation and Revision in Unsupervised Sentence Simplification
https://github.com/imperialite/standardize-ctg
공식 코드Standardize: Aligning Language Models with Expert-Defined Standards for Content Generation
https://github.com/imperialite/uniform-complexity-textgen
공식 코드https://github.com/indexfziq/clseg
공식 코드https://github.com/indicodatasolutions/doc2dict
공식 코드https://github.com/inimah/metric-preference-checklist
공식 코드NLG Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklist
https://github.com/inseq-team/inseq
공식 코드Inseq: An Interpretability Toolkit for Sequence Generation Models
https://github.com/insundaycathy/event-causality-extraction
공식 코드https://github.com/intel/neural-compressor
공식 코드https://github.com/intellabs/fp8-emulation-toolkit
공식 코드https://github.com/inzva/fake-academic-paper-generation
공식 코드https://github.com/irenezihuili/cgprompt
공식 코드Leveraging Large Language Models for Concept Graph Recovery and Question Answering in NLP Education
https://github.com/isakzhang/absa-quad
공식 코드https://github.com/isekulic/usi
공식 코드Evaluating Mixed-initiative Conversational Search Systems via User Simulation
https://github.com/ishikasingh/Affective-text-gen
공식 코드Adapting a Language Model for Controlled Affective Text Generation
https://github.com/isi-nlp/stamp
공식 코드https://github.com/isle-dev/metriceval
공식 코드https://github.com/isomap/factedit
공식 코드https://github.com/ist-daslab/sparsefinetuning
공식 코드Sparse Fine-tuning for Inference Acceleration of Large Language Models
https://github.com/iwangjian/color4dial
공식 코드Dialogue Planning via Brownian Bridge Stochastic Process for Goal-directed Proactive Dialogue
https://github.com/iwangjian/midi-tuning
공식 코드Instruct Once, Chat Consistently in Multiple Rounds: An Efficient Tuning Framework for Dialogue
https://github.com/iwangjian/plan4recdial
공식 코드Follow Me: Conversation Planning for Target-driven Recommendation Dialogue Systems
https://github.com/iwangjian/trip
공식 코드Target-constrained Bidirectional Planning for Generation of Target-oriented Proactive Dialogue
https://github.com/iz2late/VIST-Character
공식 코드Detecting and Grounding Important Characters in Visual Stories
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.