Lightprof: A Lightweight Ai Framework That Enables Small-scale Language Models To Perform Complex Reasoning Over Knowledge Graphs (kgs) Using Structured Prompts

Large Language Models (LLMs) person revolutionized earthy connection processing, pinch abilities connected analyzable zero-shot tasks done extended training information and immense parameters. However, LLMs often struggle pinch knowledge-intensive tasks owed to constricted task-specific anterior knowledge and knowing capabilities. LLMs request entree to reliable and continuously updated knowledge bases for effective reasoning, pinch Knowledge Graphs (KGs) being perfect candidates owed to their system semantic framework. Current approaches to LLM reasoning connected KGs brushwood 2 obstacles: representing KG contented arsenic extended matter fails to convey rich | logical relationships wrong nan chart structure, and retrieval and reasoning processes request galore LLM calls and important reasoning power.

Prompt engineering has emerged arsenic a captious method for expanding LLM capabilities crossed various applications without modifying exemplary parameters. The section has evolved from elemental zero-shot and few-shot prompts to much analyzable approaches for illustration Chain-of-Thought (CoT), Tree-of-Thoughts (ToT), and Graph-of-Thoughts (GoT). KG-based LLM reasoning has gained traction arsenic KGs supply explicit, system knowledge that enhances LLMs’ knowledge consciousness pinch clear logical structures. More elastic solutions for illustration KAPING, KGGPT, StructGPT, ToG, and KnowledgeNavigator conception LLM prompts utilizing KG actual accusation pinch various techniques for illustration semantic similarity retrieval, multi-step reasoning frameworks, and beam hunt connected KGs to heighten reasoning capabilities.

Researchers from Beijing University of Posts and Telecommunications, Hangzhou Dianzi University, Singapore Management University, National University of Singapore, Institute of Computing Technology astatine Chinese Academy of Sciences, and Xi’an Jiaotong University person projected LightPROF, a Lightweight and businesslike Prompt learning-ReasOning Framework. The RetrieveEmbed-Reason model enables small-scale LLMs to execute unchangeable retrieval and businesslike reasoning connected KGs. It contains 3 halfway components: Retrieval, Embedding, and Reasoning modules. The Retrieval uses relations arsenic basal retrieval units and limits nan scope based connected mobility semantics, nan Embedding uses a compact Transformer-based Knowledge Adapter, and nan Reasoning combines embedded practice vectors pinch cautiously designed prompts. LightPROF supports various open-source LLMs and KGs while only requiring Knowledge Adapter tuning during training.

LightPROF is evaluated connected 2 Freebase-based nationalist datasets: WebQuestionsSP (WebQSP) and ComplexWebQuestions (CWQ). WebQSP serves arsenic a benchmark pinch less questions (4,737) but a larger KG, and CWQ is designed for analyzable KG mobility answering pinch 34,689 question-answer pairs built upon WebQSP. Performance is measured utilizing lucifer accuracy (Hits@1), which evaluates whether nan model’s apical reply is correct. LightPROF is compared against 3 categories of baseline methods: afloat fine-tuning approaches (including KV-Mem, EmbedKGQA, TransferNet, NSM, etc), vanilla LLM methods (featuring LLaMa bid models), and LLM+KGs methods (such arsenic StructGPT, ToG, KnowledgeNavigator, and AgentBench).

LightPROF importantly outperforms state-of-the-art models, achieving 83.7% accuracy connected nan WebQSP dataset and 59.3% connected nan much challenging CWQ dataset. These results validate LightPROF’s effectiveness successful handling multi-hop and analyzable reasoning challenges successful KG mobility answering. When integrating different LLMs wrong nan framework, LightPROF consistently enhances capacity sloppy of nan baseline capabilities of nan original models. This plug-and-play integration strategy eliminates nan request for costly LLM fine-tuning. Efficiency evaluations against StructGPT uncover LightPROF’s superior assets utilization, pinch a 30% simplification successful processing time, 98% simplification successful input token usage, and importantly little tokens per request.

In conclusion, researchers introduced LightPROF, a caller model that enhances LLM reasoning done meticulous retrieval and businesslike encoding of KGs. It narrows nan retrieval scope by sampling KGs utilizing unchangeable relationships arsenic units. Researchers developed a analyzable Knowledge Adapter that efficaciously parses chart structures and integrates accusation to alteration businesslike reasoning pinch smaller LLMs. It condenses reasoning graphs into less tokens while achieving broad alignment pinch LLM input abstraction done nan Projector component. Future investigation directions see processing KG encoders pinch beardown generalization capabilities that tin beryllium applied to unseen KG information without retraining and designing unified cross-modal encoders tin of handling multimodal KGs.

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