https://github.com/by2299/mese
공식 코드Improving Conversational Recommendation Systems' Quality with Context-Aware Item Meta Information
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
A task where an agent should play the $DE$ role and generate a text to respond to a $P$ message.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Improving Conversational Recommendation Systems' Quality with Context-Aware Item Meta Information
TOOL-ED: Enhancing Empathetic Response Generation with the Tool Calling Capability of LLM
Towards Robust Personalized Dialogue Generation via Order-Insensitive Representation Regularization
Fact Finder -- Enhancing Domain Expertise of Large Language Models by Incorporating Knowledge Graphs
CoMAE: A Multi-factor Hierarchical Framework for Empathetic Response Generation
Don't Lose Yourself! Empathetic Response Generation via Explicit Self-Other Awareness
Evaluating Dialogue Generation Systems via Response Selection
Few shot chain-of-thought driven reasoning to prompt LLMs for open ended medical question answering
LexRAG: Benchmarking Retrieval-Augmented Generation in Multi-Turn Legal Consultation Conversation
Improving Response Quality with Backward Reasoning in Open-domain Dialogue Systems
Focus-Constrained Attention Mechanism for CVAE-based Response Generation
Survey on Abstractive Text Summarization: Dataset, Models, and Metrics
Exemplars-guided Empathetic Response Generation Controlled by the Elements of Human Communication
Search-Engine-augmented Dialogue Response Generation with Cheaply Supervised Query Production
A Unified Multi-task Learning Framework for Multi-goal Conversational Recommender Systems
Diverse and Faithful Knowledge-Grounded Dialogue Generation via Sequential Posterior Inference
Steering Output Style and Topic in Neural Response Generation
Grammar Control in Dialogue Response Generation for Language Learning Chatbots
Rescue: Ranking LLM Responses with Partial Ordering to Improve Response Generation
PSLM: Parallel Generation of Text and Speech with LLMs for Low-Latency Spoken Dialogue Systems
INSCIT: Information-Seeking Conversations with Mixed-Initiative Interactions
Leveraging Explicit Reasoning for Inference Integration in Commonsense-Augmented Dialogue Models
Schema-Guided Semantic Accuracy: Faithfulness in Task-Oriented Dialogue Response Generation
EM Pre-training for Multi-party Dialogue Response Generation
Stepwise Verification and Remediation of Student Reasoning Errors with Large Language Model Tutors
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.