https://github.com/mqo00/rope
공식 코드What Should We Engineer in Prompts? Training Humans in Requirement-Driven LLM Use
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Chatbot or conversational AI is a language model designed and implemented to have conversations with humans.
Source: Open Data Chatbot
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
What Should We Engineer in Prompts? Training Humans in Requirement-Driven LLM Use
A Framework for Integrating Gesture Generation Models into Interactive Conversational Agents
Elastic Weight Removal for Faithful and Abstractive Dialogue Generation
Knowledge Enhanced Fine-Tuning for Better Handling Unseen Entities in Dialogue Generation
Bring Your Own Data! Self-Supervised Evaluation for Large Language Models
Evaluation of Large Language Models via Coupled Token Generation
A Three-Stage Learning Framework for Low-Resource Knowledge-Grounded Dialogue Generation
Boosting Big Brother: Attacking Search Engines with Encodings
G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment
KL Penalty Control via Perturbation for Direct Preference Optimization
A Qualitative Evaluation of Language Models on Automatic Question-Answering for COVID-19
PsyDI: Towards a Personalized and Progressively In-depth Chatbot for Psychological Measurements
IntellBot: Retrieval Augmented LLM Chatbot for Cyber Threat Knowledge Delivery
End-to-end Adversarial Learning for Generative Conversational Agents
End-to-end Adversarial Learning for Generative Conversational Agents
Layer-Level Self-Exposure and Patch: Affirmative Token Mitigation for Jailbreak Attack Defense
Social Biases in Knowledge Representations of Wikidata separates Global North from Global South
CausalDialogue: Modeling Utterance-level Causality in Conversations
InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning
SimPO: Simple Preference Optimization with a Reference-Free Reward
Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data
Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.