https://github.com/abecode/emo20q-web
공식 코드Emotion Twenty Questions Dialog System for Lexical Emotional Intelligence
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Emotional Intelligence (EI) is a measure of "The ability to monitor one’s own and others’ feelings, to discriminate among them, and to use this information to guide one’s thinking and action." (Salovey and Mayer, 1990). EI is further broken down into four branches: perceiving, using, understanding and managing emotions (Mayer & Salovey, 1997). Of particular relevance to language models that operate exclusively in the text modality is emotional understanding (EU). This is defined as the ability to interpret and analyse the language of emotions, to comprehend complex emotional states, and understand how these emotions can influence behaviour and decision-making.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Emotion Twenty Questions Dialog System for Lexical Emotional Intelligence
SAGE: Steering and Refining Dialog Generation with State-Action Augmentation
REALTALK: A 21-Day Real-World Dataset for Long-Term Conversation
EmoMeta: A Multimodal Dataset for Fine-grained Emotion Classification in Chinese Metaphors
EQ-Bench: An Emotional Intelligence Benchmark for Large Language Models
Sloth: scaling laws for LLM skills to predict multi-benchmark performance across families
TeleSparse: Practical Privacy-Preserving Verification of Deep Neural Networks
EmoBench: Evaluating the Emotional Intelligence of Large Language Models
RLVER: Reinforcement Learning with Verifiable Emotion Rewards for Empathetic Agents
EmoLLM: Multimodal Emotional Understanding Meets Large Language Models
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Language Model Council: Democratically Benchmarking Foundation Models on Highly Subjective Tasks
Scaling up Trustless DNN Inference with Zero-Knowledge Proofs
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Investigating Emotion-Color Association in Deep Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.