Papers with DRI

2 papers
Self-chats from Large Language Models Make Small Emotional Support Chatbot Better (2024.acl-long)

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Challenge: Large Language Models (LLMs) have shown strong generalization abilities to excel in various tasks, including emotion support conversations.
Approach: They propose an iterative expansion framework to prompt large teacher model to curate an expansive emotion support dialogue dataset.
Outcome: The proposed model outperforms the teacher model in some cases . the proposed model is based on an iterative expansion framework and is available on github.com/pandazzh2020/ExTES.
Analyzing and Mitigating Inconsistency in Discrete Speech Tokens for Neural Codec Language Models (2025.acl-long)

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Challenge: Recent advances in Large Language Models (LLMs) have demonstrated significant strides in generating high-quality speech . discretizing speech by neural audio codecs often results in sequences that differ from text sequences .
Approach: They quantitatively analyze the Discrete Representation Inconsistency phenomenon within popular audio tokenizers such as EnCodec.
Outcome: The proposed method mitigates the DRI phenomenon within popular audio tokenizers such as EnCodec.

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