We compile 129 public LLM prompt datasets with more than 1.22TB and more than 673M instances and organise them into a unified taxonomy. We use seven datasets for detailed analysis and identify lexical, syntactic, and semantic patterns that distinguish prompts from general text. We evaluate these features in prompt filtering, source domain routing, and elicited response quality analysis. A 63 dimensional linguistic feature set that can be extracted on a CPU achieves over 91% of the F1 of GPU sentence embeddings and reduces latency per request by nearly half. In cross dataset routing, sentence embeddings reach 0.74 Macro-F1 and a structural feature subset reaches 0.61, compared with the chance level of 0.20. Structural routing features remain negatively associated with elicited response quality after prompt length is controlled on UltraFeedback. The apparent positive association of type to token ratio becomes much smaller under the same control. These findings support prompt processing pipelines that combine efficient structural features with robust semantic routing. Our catalogue, annotations, and code are given at https://github.com/ymzhang-cs/prompt-dataset-analysis.

Chain-of-thought (CoT) reasoning boosts large language models’ (LLMs) performance on complex tasks but faces two key limitations: a lack of reliability when solely relying on LLM-generated reasoning chains and interference from natural language reasoning steps with the models’ inference process, also known as the inference logic of LLMs. To address these issues, we propose CoT-RAG, a novel reasoning framework with three key designs: (i) Knowledge Graph-driven CoT Generation, featuring knowledge graphs to modulate reasoning chain generation of LLMs, thereby enhancing reasoning credibility; (ii) Learnable Knowledge Case-aware RAG, which incorporates retrieval-augmented generation (RAG) into knowledge graphs to retrieve relevant sub-cases and sub-descriptions, providing LLMs with learnable information; (iii) Pseudo-Program Prompting Execution, which promotes greater logical rigor by guiding LLMs to execute reasoning tasks as pseudo-programs. Evaluations on nine public datasets spanning three reasoning tasks reveal significant accuracy gains—ranging from 4.0% to 44.3%–over state-of-the-art methods. Furthermore, tests on four domain-specific datasets demonstrate exceptional accuracy and efficient execution, underscoring its practical applicability and scalability. Our code and data are available at https://github.com/hustlfy123/CoT-RAG.

Large language models (LLMs) have demonstrated remarkable performance on question-answering (QA) tasks because of their superior capabilities in natural language understanding and generation. However, LLM-based QA struggles with complex QA tasks due to poor reasoning capacity, outdated knowledge, and hallucinations. Several recent works synthesize LLMs and knowledge graphs (KGs) for QA to address the above challenges. In this survey, we propose a new structured taxonomy that categorizes the methodology of synthesizing LLMs and KGs for QA according to the categories of QA and the KG’s role when integrating with LLMs. We systematically survey state-of-the-art methods in synthesizing LLMs and KGs for QA and compare and analyze these approaches in terms of strength, limitations, and KG requirements. We then align the approaches with QA and discuss how these approaches address the main challenges of different complex QA. Finally, we summarize the advancements, evaluation metrics, and benchmark datasets and highlight open challenges and opportunities.