Fig. 5: LLM agents framework and performance evaluation.
From: Wearable intelligent throat enables natural speech in stroke patients with dysarthria
a Schematic of the IT’s LLM agents: Token Synthesis Agent (left) directly synthesizes sentences from neural network token labels, while Sentence Expansion Agent (right) enhances outputs with contextual and emotional inputs. b Effect of prompt length on word error rate (WER) and sentence error rate (SER) with optimal performance observed at medium lengths. c Influence of example-based few-shot learning on WER and SER, showing a significant reduction when examples are provided. d Impact of constrained decoding on WER and SER, demonstrating improved accuracy and sentence structure. e Contribution of objective information, word, and emotion labels on key user metrics, including fluency, satisfaction, core meaning, and emotional accuracy (evaluated through ablation experiments). f Radar plot comparing performance across various configurations (Token-only, Context-aware, Chain-of-Thought (CoT), and CoT with personalized demonstration) on fluency, personalization, core meaning, satisfaction, completeness, and emotion accuracy. Error bars indicate mean ± s.d.
