https://github.com/asappresearch/constrained-dialogue-generation
공식 코드Long-term Control for Dialogue Generation: Methods and Evaluation
Papers with Code 아카이브
Chatbot
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Chatbot or conversational AI is a language model designed and implemented to have conversations with humans.
Source: Open Data Chatbot
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/baltaci-r/CulturedAgents
공식 코드Persona Inconstancy in Multi-Agent LLM Collaboration: Conformity, Confabulation, and Impersonation
https://github.com/bangliu/ACS-QG
공식 코드Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus
https://github.com/batra-mlp-lab/visdial-amt-chat
공식 코드https://github.com/behavioral-data/PARTNER
공식 코드https://github.com/believewhat/dr.noteaid
공식 코드NoteChat: A Dataset of Synthetic Doctor-Patient Conversations Conditioned on Clinical Notes
https://github.com/bernard-yang/meddiaamr
공식 코드Improving Medical Dialogue Generation with Abstract Meaning Representations
https://github.com/bill1235813/cancer-myth
공식 코드Cancer-Myth: Evaluating AI Chatbot on Patient Questions with False Presuppositions
https://github.com/bioimage-io/bioimageio-chatbot
공식 코드BioImage.IO Chatbot: A Community-Driven AI Assistant for Integrative Computational Bioimaging
https://github.com/bkane2/habitual-response-generation
공식 코드We are what we repeatedly do: Inducing and deploying habitual schemas in persona-based responses
https://github.com/blacksnail789521/LLM4TS
공식 코드LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters
https://github.com/blmoistawinde/dasc
공식 코드Semantic Space Grounded Weighted Decoding for Multi-Attribute Controllable Dialogue Generation
https://github.com/boychaboy/ricota
공식 코드RICoTA: Red-teaming of In-the-wild Conversation with Test Attempts
https://github.com/brunogilramirez/just_hear_me
공식 코드https://github.com/budzianowski/multiwoz
공식 코드Task-Optimized Adapters for an End-to-End Task-Oriented Dialogue System
https://github.com/caoyu-noob/Multi-GPT2
공식 코드Pretrained Language Models for Dialogue Generation with Multiple Input Sources
https://github.com/caoyu-noob/d3
공식 코드A Model-Agnostic Data Manipulation Method for Persona-based Dialogue Generation
https://github.com/caoyu-noob/embedding_augmentation
공식 코드Towards Efficiently Diversifying Dialogue Generation via Embedding Augmentation
https://github.com/casmlab/datachat
공식 코드DataChat: Prototyping a Conversational Agent for Dataset Search and Visualization
https://github.com/cciiplab/persona_extend
공식 코드Improving Personality Consistency in Conversation by Persona Extending
https://github.com/cemuluoglakci/hypotermqa
공식 코드HypoTermQA: Hypothetical Terms Dataset for Benchmarking Hallucination Tendency of LLMs
https://github.com/cfn-softbio/scibot
공식 코드https://github.com/chanliang/orig
공식 코드Towards Robust Personalized Dialogue Generation via Order-Insensitive Representation Regularization
https://github.com/chengfeng-dou/sptesting
공식 코드https://github.com/chenrj233/lmedr
공식 코드Learning to Memorize Entailment and Discourse Relations for Persona-Consistent Dialogues
https://github.com/chenxu05037/COSPLAY
공식 코드COSPLAY: Concept Set Guided Personalized Dialogue Generation Across Both Party Personas
https://github.com/chilab1/unipagpt-23
공식 코드Unipa-GPT: Large Language Models for university-oriented QA in Italian
https://github.com/chin-gyou/Div-Non-Conv
공식 코드Diversifying Dialogue Generation with Non-Conversational Text
https://github.com/cimhasgithub/mibot_acl2025
공식 코드https://github.com/cl-tohoku/eval-via-selection
공식 코드Evaluating Dialogue Generation Systems via Response Selection
https://github.com/clarin-pl/chatgpt-evaluation-01-2023
공식 코드https://github.com/cods-gcs/chatty-gen
공식 코드Dialogue Benchmark Generation from Knowledge Graphs with Cost-Effective Retrieval-Augmented LLMs
https://github.com/copenlu/cultural-name-bias
공식 코드https://github.com/csbl-br/awesome-compbio-chatgpt
공식 코드Ten Quick Tips for Harnessing the Power of ChatGPT/GPT-4 in Computational Biology
https://github.com/csebuetnlp/banglanlg
공식 코드https://github.com/declare-lab/MELD
공식 코드MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.