https://github.com/sagarsj42/legal_clause_gen_from_topic_keywords
공식 코드Graph-based Keyword Planning for Legal Clause Generation from Topics
Papers with Code 아카이브
Text Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 25
- 평가표
- 155
- 보존된 평가 기록
- 54
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/sagarsj42/strategies_for_clause_recommendation
공식 코드https://github.com/saidul-islam98/DataNarrative
공식 코드DataNarrative: Automated Data-Driven Storytelling with Visualizations and Texts
https://github.com/salesforce/CASPI
공식 코드Causal-aware Safe Policy Improvement for Task-oriented dialogue
https://github.com/salesforce/WikiSQL
공식 코드SeqGenSQL -- A Robust Sequence Generation Model for Structured Query Language
https://github.com/salesforce/diversesumm
공식 코드https://github.com/salesforce/fewshot_absa
공식 코드A Generative Language Model for Few-shot Aspect-Based Sentiment Analysis
https://github.com/salesforce/lavis
공식 코드https://github.com/salesforce/nnd_evaluation
공식 코드Near-Negative Distinction: Giving a Second Life to Human Evaluation Datasets
https://github.com/salesforce/ulip
공식 코드ULIP-2: Towards Scalable Multimodal Pre-training for 3D Understanding
https://github.com/salt-nlp/bound-cap-llm
공식 코드Bounding the Capabilities of Large Language Models in Open Text Generation with Prompt Constraints
https://github.com/samin-mehdizadeh/llama-persian-adaptation
공식 코드Extending LLMs to New Languages: A Case Study of Llama and Persian Adaptation
https://github.com/saranya-venkatraman/multi_llm_story_writing
공식 코드CollabStory: Multi-LLM Collaborative Story Generation and Authorship Analysis
https://github.com/sashank06/CCNLG-emotion
공식 코드Emotional Neural Language Generation Grounded in Situational Contexts
https://github.com/sberbank-ai/StackMix-OCR
공식 코드StackMix and Blot Augmentations for Handwritten Text Recognition
https://github.com/scb-10x/typhoon2-audio
공식 코드Typhoon 2: A Family of Open Text and Multimodal Thai Large Language Models
https://github.com/schmiflo/crf-generation
공식 코드https://github.com/scoopmatic/finnish-hockey-news-generation-paper
공식 코드Template-free Data-to-Text Generation of Finnish Sports News
https://github.com/scotthlee/nrc
공식 코드https://github.com/scutcyr/CPED
공식 코드CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AI
https://github.com/scylj1/llm4dpcg
공식 코드Can Large Language Models Generate High-quality Patent Claims?
https://github.com/seanie12/CLAPS
공식 코드Contrastive Learning with Adversarial Perturbations for Conditional Text Generation
https://github.com/seanie12/Info-HCVAE
공식 코드https://github.com/sebastianGehrmann/bottom-up-summary
공식 코드https://github.com/sebastianGehrmann/diverse_ensembling
공식 코드End-to-End Content and Plan Selection for Data-to-Text Generation
https://github.com/sebischair/llm-kg-d2t
공식 코드A Comparative Analysis of Conversational Large Language Models in Knowledge-Based Text Generation
https://github.com/seemdog/DAHL
공식 코드https://github.com/seermer/RTGen
공식 코드RTGen: Generating Region-Text Pairs for Open-Vocabulary Object Detection
https://github.com/sefazeng/llm-ref
공식 코드https://github.com/sejoonoh/atr
공식 코드Adversarial Text Rewriting for Text-aware Recommender Systems
https://github.com/selbeltagy/arragexperiments
공식 코드https://github.com/selimfirat/bilkent-turkish-writings-dataset
공식 코드Evaluating Morphological Compositional Generalization in Large Language Models
https://github.com/semerekiros/distractgpt
공식 코드https://github.com/sensellm/fim-se
공식 코드Empowering Character-level Text Infilling by Eliminating Sub-Tokens
https://github.com/seq-to-mind/semi-style-transfer
공식 코드https://github.com/seraphinatarrant/plan-write-revise
공식 코드Plan, Write, and Revise: an Interactive System for Open-Domain Story Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.