https://github.com/matejsprogar/agitb
공식 코드AGITB: A Signal-Level Benchmark for Evaluating Artificial General Intelligence
Papers with Code 아카이브
Response Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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A task where an agent should play the $DE$ role and generate a text to respond to a $P$ message.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/matthewkkai/milp
공식 코드Personalized LLM Response Generation with Parameterized Memory Injection
https://github.com/mayank31398/hybridtod
공식 코드Joint Reasoning on Hybrid-knowledge sources for Task-Oriented Dialog
https://github.com/mayo-radiology-informatics-lab/conflare
공식 코드https://github.com/medicinetoken/medical-graph-rag
공식 코드https://github.com/microsoft/DialoGPT
공식 코드DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation
https://github.com/microsoft/MASS
공식 코드MASS: Masked Sequence to Sequence Pre-training for Language Generation
https://github.com/microsoft/ProphetNet
공식 코드Joint Generator-Ranker Learning for Natural Language Generation
https://github.com/microsoft/advnlg
공식 코드Joint Generator-Ranker Learning for Natural Language Generation
https://github.com/microsoft/dataflow2text
공식 코드https://github.com/microsoft/rebel
공식 코드https://github.com/microsoft/unilm
공식 코드Unified Language Model Pre-training for Natural Language Understanding and Generation
https://github.com/minsik-ai/pk-icr
공식 코드PK-ICR: Persona-Knowledge Interactive Context Retrieval for Grounded Dialogue
https://github.com/morning-dews/GSN-Dialogues
공식 코드https://github.com/muditchaudhary/CUHK-DSTC9
공식 코드https://github.com/muktac5/ciir-liverag
공식 코드CIIR@LiveRAG 2025: Optimizing Multi-Agent Retrieval Augmented Generation through Self-Training
https://github.com/multifacetednlp/zs-tod
공식 코드https://github.com/muyeby/AMR-Dialogue
공식 코드https://github.com/naver-ai/korean-safety-benchmarks
공식 코드https://github.com/ndaheim/dialdoc-sharedtask-21
공식 코드Cascaded Span Extraction and Response Generation for Document-Grounded Dialog
https://github.com/ndaheim/noisy_channel_model
공식 코드Controllable Factuality in Document-Grounded Dialog Systems Using a Noisy Channel Model
https://github.com/neukg/kat-tslf
공식 코드A Three-Stage Learning Framework for Low-Resource Knowledge-Grounded Dialogue Generation
https://github.com/nhattlm95/tm_kg_dialogue
공식 코드Enhancing Knowledge Retrieval with Topic Modeling for Knowledge-Grounded Dialogue
https://github.com/nidryen-zh/questionerawareresponder
공식 코드Personalized LLM for Generating Customized Responses to the Same Query from Different Users
https://github.com/nikitacs16/horovod_gcn_pointer_generator
공식 코드On Incorporating Structural Information to improve Dialogue Response Generation
https://github.com/nlp-rl/semtextuallogue-loss
공식 코드https://github.com/nlp-waseda/expr-exper-emo
공식 코드Building a Dialogue Corpus Annotated with Expressed and Experienced Emotions
https://github.com/nlpcode/mcmcxlnet
공식 코드Show Me How To Revise: Improving Lexically Constrained Sentence Generation with XLNet
https://github.com/nouhadziri/THRED
공식 코드Augmenting Neural Response Generation with Context-Aware Topical Attention
https://github.com/nu-dialogue/jmultiwoz
공식 코드JMultiWOZ: A Large-Scale Japanese Multi-Domain Task-Oriented Dialogue Dataset
https://github.com/nuochenpku/comedy
공식 코드https://github.com/nuochenpku/penguin
공식 코드Natural Response Generation for Chinese Reading Comprehension
https://github.com/nusnlp/prs
공식 코드Preference-Guided Reflective Sampling for Aligning Language Models
https://github.com/octaviaguo/chattychef
공식 코드Improved Instruction Ordering in Recipe-Grounded Conversation
https://github.com/osu-nlp-group/tacobot
공식 코드Roll Up Your Sleeves: Working with a Collaborative and Engaging Task-Oriented Dialogue System
https://github.com/phlippe/P2_Net
공식 코드Diversifying Task-oriented Dialogue Response Generation with Prototype Guided Paraphrasing
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.