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Found 10 papers, 7 papers with code
SpeechAlign: Aligning Speech Generation to Human Preferences
However, the integration of human feedback to align speech outputs to human preferences is often neglected.
LLM can Achieve Self-Regulation via Hyperparameter Aware Generation
HAG extends the current paradigm in the text generation process, highlighting the feasibility of endowing the LLMs with self-regulate decoding strategies.
Can AI Assistants Know What They Don't Know?
To answer this question, we construct a model-specific "I don't know" (Idk) dataset for an assistant, which contains its known and unknown questions, based on existing open-domain question answering datasets.
SpeechGPT-Gen: Scaling Chain-of-Information Speech Generation
It comprises an autoregressive model based on LLM for semantic information modeling and a non-autoregressive model employing flow matching for perceptual information modeling.
Agent Alignment in Evolving Social Norms
Agents based on Large Language Models (LLMs) are increasingly permeating various domains of human production and life, highlighting the importance of aligning them with human values.
SpeechTokenizer: Unified Speech Tokenizer for Speech Large Language Models
Therefore, we propose SpeechTokenizer, a unified speech tokenizer for speech large language models.
Multijugate Dual Learning for Low-Resource Task-Oriented Dialogue System
Dialogue data in real scenarios tend to be sparsely available, rendering data-starved end-to-end dialogue systems trained inadequately.
SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities
Multi-modal large language models are regarded as a crucial step towards Artificial General Intelligence (AGI) and have garnered significant interest with the emergence of ChatGPT.
Mitigating Negative Style Transfer in Hybrid Dialogue System
As the functionality of dialogue systems evolves, hybrid dialogue systems that accomplish user-specific goals and participate in open-topic chitchat with users are attracting growing attention.
Contrast and Generation Make BART a Good Dialogue Emotion Recognizer
Meanwhile, we utilize an auxiliary response generation task to enhance the model's ability of handling context information, thereby forcing the model to recognize emotions with similar semantics in diverse contexts.
Ranked #11 on
Emotion Recognition in Conversation
on EmoryNLP
