LADDER, a proposed mix of chain-of-thought prompting, mixture-of-experts layers, and linear projections, reportedly improves LLM creativity and diversity, but the evidence is thin and partly contradictory.
Concept-Based Embeddings for Natural Language Processing
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abstract
In this work, we focus on effectively leveraging and integrating information from concept-level as well as word-level via projecting concepts and words into a lower dimensional space while retaining most critical semantics. In a broad context of opinion understanding system, we investigate the use of the fused embedding for several core NLP tasks: named entity detection and classification, automatic speech recognition reranking, and targeted sentiment analysis.
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Breaking Thought Patterns: A Multi-Dimensional Reasoning Framework for LLMs
LADDER, a proposed mix of chain-of-thought prompting, mixture-of-experts layers, and linear projections, reportedly improves LLM creativity and diversity, but the evidence is thin and partly contradictory.