https://github.com/qingruzhang/adalora
공식 코드AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning
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/qisun123/genrdk
공식 코드https://github.com/qiyao-wang/autopatent
공식 코드AutoPatent: A Multi-Agent Framework for Automatic Patent Generation
https://github.com/qkaren/Counterfactual-StoryRW
공식 코드https://github.com/qkaren/cold_decoding
공식 코드COLD Decoding: Energy-based Constrained Text Generation with Langevin Dynamics
https://github.com/qkaren/unsup_gen_for_cms_reasoning
공식 코드https://github.com/qqw-ing/rag-reasonalignment
공식 코드AlignRAG: Leveraging Critique Learning for Evidence-Sensitive Retrieval-Augmented Reasoning
https://github.com/qtli/EmpDG
공식 코드EmpDG: Multiresolution Interactive Empathetic Dialogue Generation
https://github.com/qtli/coeval
공식 코드Exploring the Reliability of Large Language Models as Customized Evaluators for Diverse NLP Tasks
https://github.com/qtli/eib
공식 코드https://github.com/qtli/eventplanfortextgen
공식 코드https://github.com/qtli/kemp
공식 코드https://github.com/quehry/hellobench
공식 코드HelloBench: Evaluating Long Text Generation Capabilities of Large Language Models
https://github.com/question406/learningtofocus
공식 코드Controlling the Focus of Pretrained Language Generation Models
https://github.com/qwenlm/qwen2.5
공식 코드https://github.com/qywu/memformers
공식 코드Stateful Memory-Augmented Transformers for Efficient Dialogue Modeling
https://github.com/qzx7/mindthetime
공식 코드Mind the Gap Between Conversations for Improved Long-Term Dialogue Generation
https://github.com/r1047/air-decoding
공식 코드Air-Decoding: Attribute Distribution Reconstruction for Decoding-Time Controllable Text Generation
https://github.com/r9729104234/Emotion-Aware-Transformer-Encoder-for-Empathetic-Dialogue-Generation
공식 코드Emotion-Aware Transformer Encoder for Empathetic Dialogue Generation
https://github.com/radi-cho/rstod
공식 코드Efficient Task-Oriented Dialogue Systems with Response Selection as an Auxiliary Task
https://github.com/rahular/varta
공식 코드Vārta: A Large-Scale Headline-Generation Dataset for Indic Languages
https://github.com/rainavyas/prepend_acoustic_attack
공식 코드Muting Whisper: A Universal Acoustic Adversarial Attack on Speech Foundation Models
https://github.com/raingo/TGIF-Release
공식 코드TGIF: A New Dataset and Benchmark on Animated GIF Description
https://github.com/rajammanabrolu/KG-A2C
공식 코드Graph Constrained Reinforcement Learning for Natural Language Action Spaces
https://github.com/rajammanabrolu/StoryRealization
공식 코드https://github.com/rajammanabrolu/WorldGeneration
공식 코드Bringing Stories Alive: Generating Interactive Fiction Worlds
https://github.com/rajdeep345/ControllableSumm
공식 코드Read what you need: Controllable Aspect-based Opinion Summarization of Tourist Reviews
https://github.com/rali-udem/JSrealB
공식 코드https://github.com/ramakanth-pasunuru/QmdsCnnIr
공식 코드Data Augmentation for Abstractive Query-Focused Multi-Document Summarization
https://github.com/ramber1836/tasd
공식 코드https://github.com/raosudha89/GYAFC-corpus
공식 코드https://github.com/rashad101/dialokg
공식 코드DialoKG: Knowledge-Structure Aware Task-Oriented Dialogue Generation
https://github.com/rashad101/rome
공식 코드RoMe: A Robust Metric for Evaluating Natural Language Generation
https://github.com/ratishsp/centrum
공식 코드Multi-Document Summarization with Centroid-Based Pretraining
https://github.com/ratishsp/data2text-entity-py
공식 코드https://github.com/ratishsp/data2text-macro-plan-py
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.