https://github.com/hqsiswiliam/SPT
공식 코드Selective Prompting Tuning for Personalized Conversations with LLMs
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Dialogue generation is the task of "understanding" natural language inputs - within natural language processing in order to produce output. The systems are usually intended for conversing with humans, for instance back and forth dialogue with a conversation agent like a chatbot. Some example benchmarks for this task (see others such as Natural Language Understanding) include FusedChat and Ubuntu DIalogue Corpus (UDC). Models can be evaluated via metrics such as BLEU, ROUGE, and METEOR albeit with challenges in terms of weak correlation with human judgement, that may be addressed by new ones like UnSupervised and Reference-free (USR) and Metric for automatic Unreferenced dialog evaluation (MaUde).
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Selective Prompting Tuning for Personalized Conversations with LLMs
Learning Retrieval Augmentation for Personalized Dialogue Generation
Personalized Dialogue Generation with Persona-Adaptive Attention
PanGu-Bot: Efficient Generative Dialogue Pre-training from Pre-trained Language Model
Measuring and Improving Semantic Diversity of Dialogue Generation
Conversations Are Not Flat: Modeling the Dynamic Information Flow across Dialogue Utterances
SDialog: A Python Toolkit for Synthetic Dialogue Generation and Analysis
NLG Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklist
Dialogue Planning via Brownian Bridge Stochastic Process for Goal-directed Proactive Dialogue
Instruct Once, Chat Consistently in Multiple Rounds: An Efficient Tuning Framework for Dialogue
Follow Me: Conversation Planning for Target-driven Recommendation Dialogue Systems
Target-constrained Bidirectional Planning for Generation of Target-oriented Proactive Dialogue
A Knowledge Plug-and-Play Test Bed for Open-domain Dialogue Generation
Skeleton-to-Response: Dialogue Generation Guided by Retrieval Memory
CASE: Aligning Coarse-to-Fine Cognition and Affection for Empathetic Response Generation
Language Detoxification with Attribute-Discriminative Latent Space
PSYDIAL: Personality-based Synthetic Dialogue Generation using Large Language Models
Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation
ZipVoice-Dialog: Non-Autoregressive Spoken Dialogue Generation with Flow Matching
DecompEval: Evaluating Generated Texts as Unsupervised Decomposed Question Answering
Semi-Supervised Variational Reasoning for Medical Dialogue Generation
CTRLStruct: Dialogue Structure Learning for Open-Domain Response Generation
User-Centric Conversational Recommendation with Multi-Aspect User Modeling
A Survey on Non-Autoregressive Generation for Neural Machine Translation and Beyond
MedDG: An Entity-Centric Medical Consultation Dataset for Entity-Aware Medical Dialogue Generation
Improving Multi-turn Emotional Support Dialogue Generation with Lookahead Strategy Planning
TegTok: Augmenting Text Generation via Task-specific and Open-world Knowledge
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.