Semantic Space Grounded Weighted Decoding for Multi-Attribute Controllable Dialogue Generation
Controlling chatbot utterance generation with multiple attributes such as personalities, emotions and dialogue acts is a practically useful but under-studied problem. We propose a novel framework called DASC that possesses strong controllability with a weighted decoding paradigm, while improving generation quality with the grounding in an attribute semantics space. Generation with multiple attributes is then intuitively implemented with an interpolation of multiple attribute embeddings, which results in substantial reduction in the model sizes. Experiments show that DASC can achieve high control accuracy in generation task with the simultaneous control of 3 aspects while also producing interesting and reasonably sensible responses, even in an out-of-distribution robustness test.
Code (1)
Tasks
AttributeChatbotDialogue GenerationSimilar Papers 제목 키워드 기반
Breaking the Illusion: When Positive Meets Negative in Multimodal Decoding
Vision-Language Models (VLMs) are frequently undermined by object hallucination, generating content that contradicts visual reality, due to an over-reliance on linguistic priors. We introduce Positive-and-Negative Decodi…
The Hidden Life of Tokens: Reducing Hallucination of Large Vision-Language Models via Visual Information Steering
Large Vision-Language Models (LVLMs) can reason effectively over both textual and visual inputs, but they tend to hallucinate syntactically coherent yet visually ungrounded contents. In this paper, we investigate the int…
HallucinationRevisit What You See: Disclose Language Prior in Vision Tokens for Efficient Guided Decoding of LVLMs
Large Vision-Language Models (LVLMs) have demonstrated remarkable performance across various multimodal tasks by integrating visual perception with language understanding. However, conventional decoding strategies of LVL…
HallucinationObject HallucinationText GenerationVisual GroundingSequence-Level Certainty Reduces Hallucination In Knowledge-Grounded Dialogue Generation
In this work, we propose sequence-level certainty as a common theme over hallucination in Knowledge Grounded Dialogue Generation (KGDG). We explore the correlation between the level of hallucination in model responses an…
Dialogue GenerationHallucinationText GenerationDOPRA: Decoding Over-accumulation Penalization and Re-allocation in Specific Weighting Layer
In this work, we introduce DOPRA, a novel approach designed to mitigate hallucinations in multi-modal large language models (MLLMs). Unlike existing solutions that typically involve costly supplementary training data or …