https://github.com/jiaqisjtu/faitheval-fflm
공식 코드Zero-shot Faithfulness Evaluation for Text Summarization with Foundation Language Model
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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>
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Zero-shot Faithfulness Evaluation for Text Summarization with Foundation Language Model
In-sample Curriculum Learning by Sequence Completion for Natural Language Generation
Reducing Sensitivity on Speaker Names for Text Generation from Dialogues
Gorgeous: Create Your Desired Character Facial Makeup from Any Ideas
A Neural Conversation Generation Model via Equivalent Shared Memory Investigation
Language Detoxification with Attribute-Discriminative Latent Space
Hooks in the Headline: Learning to Generate Headlines with Controlled Styles
Document-Level Text Generation with Minimum Bayes Risk Decoding using Optimal Transport
PSYDIAL: Personality-based Synthetic Dialogue Generation using Large Language Models
Self-supervised Graph Masking Pre-training for Graph-to-Text Generation
VLIS: Unimodal Language Models Guide Multimodal Language Generation
Attention Is Indeed All You Need: Semantically Attention-Guided Decoding for Data-to-Text NLG
Balancing LoRA Performance and Efficiency with Simple Shard Sharing
Balancing LoRA Performance and Efficiency with Simple Shard Sharing
ConMe: Rethinking Evaluation of Compositional Reasoning for Modern VLMs
Text Generation: A Systematic Literature Review of Tasks, Evaluation, and Challenges
PREADD: Prefix-Adaptive Decoding for Controlled Text Generation
Language Model Sentence Completion with a Parser-Driven Rhetorical Control Method
PELMS: Pre-training for Effective Low-Shot Multi-Document Summarization
How Large Language Models are Transforming Machine-Paraphrased Plagiarism
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.