Combining feedback enhancement with knowledge graphs in large language models

G-HiRel: Enhancing the Adaption to Knowledge Updating for Large Language Model Reasoning

An LLM reasoning framework with hierarchical relational retrieval for large-scale knowledge updating, named G-HiRel, which achieves superiority in terms of accuracy and interpretability and handles the knowledge inconsistency between the KG and LLM to obtain entity independence.

KGP-QG: Multi-hop Reasoning with Knowledge Graphs in LLMs

K-GRASP: Tacit Knowledge Externalisation with Language Models and Knowledge Graphs

The externalisation of tacit knowledge, defined as practical, experiential knowledge that is difficult to formalise, represents one of the greatest challenges faced by institutions that rely on the expertise of their members. Due to its subjective and contextual nature, this type of knowledge resists capture through traditional methods such as interviews and observations, which are costly, difficult to standardise, and limited in reusability. This work proposes an architecture called K-GRASP (Knowledge Graph-based Retrieval-Augmented Structured Prompting), which combines the representational power of knowledge graphs (KG) with the probabilistic reasoning capabilities of Large Language Models (LLMs) to address this challenge. The proposed solution is divided into two phases: (i) a capture phase, in which an interviewer LLM conducts structured sessions with experts and converts their responses into RDF triples stored in a semantic graph, and (ii) a retrieval phase, in which a consultative LLM uses Retrieval-Augmented Generation (RAG) to translate natural language questions into SPARQL queries (SPARQL Protocol and RDF Query Language), retrieve relevant subgraphs, and generate contextualized responses. The architecture enables the systematic, reusable, and accessible codification of tacit knowledge, allowing for its large-scale preservation and dissemination. By integrating LLMs with formal representation structures, K-GRASP offers a robust, scalable, and interpretable solution to a historically complex problem in knowledge management. In the capture phase, prompts can be shaped to elicit concrete cases, boundary conditions, and decision rationales. At the same time, responses are mapped to an agreed-upon vocabulary to reduce drift across sessions. The graph can also retain provenance and scope cues (e.g., source expert, date, and stated assumptions), which may support later review and incremental refinement. In the retrieval phase, returning both the synthesised answer and the underlying triples can make the consultation more transparent and highlight gaps or ambiguities, as well as operational concerns such as access control, privacy, and versioning.

Graph-Assisted Large Language Models: A Perspective on Mitigating Intrinsic Limitations

Large language models (LLMs) have made progress in knowledge-intensive tasks, reasoning and planning, and collaborative problem solving, yet they exhibit intrinsic limitations such as knowledge cutoff, single-threaded reasoning that hinders finer-grained branch and aggregation, and rigid collaboration mechanisms that struggle to coordinate specialized capabilities. Graphs, with their ability to represent relational knowledge and complex dependencies, offer a natural means to address these limitations: they provide structured, high-density knowledge for augmenting or correcting LLMs’ generation; enable revisitable inference by organizing intermediate steps as graphs; and support dynamic coordination among experts or agents in collaborative settings. Motivated by these developments, we present the first systematic survey of graph-assisted LLMs from the perspective of how graph structures mitigate LLMs’ limitations. We introduce a taxonomy spanning Graph-Assisted Knowledge Augmentation, Graph-Assisted Reasoning and Planning, and Graph-Assisted LLM Collaboration , and analyze representative methods, summarize common design patterns, and outline open challenges and future directions for advancing LLMs with graph-based enhancements. The collected papers are available in link here.

Augmenting Datasets for Fine-Tuning Large Language Models Using Semantic Variations

This study explores a semantic variation methodology to augment training data by generating question-answer pairs with explicit control over semantic similarity, and shows that semantically controlled augmentation improves domain-specific knowledge acquisition while preserving consistency.

RAGU: A Multi-Step GraphRAG Engine with a Compact Domain-Adapted LLM

Graph retrieval-augmented generation (GraphRAG) enhances large language models with structured knowledge, yet existing systems construct knowledge graphs in a single extraction pass, producing noisy entities and brittle retrieval. RAGU, an open-source modular GraphRAG engine, addresses this by separating extraction from consolidation: entities and relations pass through two-stage typed extraction, DBSCAN-backed deduplication, LLM summarization, and Leiden community detection. A key insight motivates a compact extractor: the skills an in-pipeline LLM needs - comprehension, extraction, reasoning over context - are language skills that grow only weakly with model size, unlike factual world knowledge. Accordingly, we train Meno-Lite-0.1, a 7B model optimized for language skills, which outperforms Qwen2.5-32B on knowledge-graph construction (+12.5% relative harmonic mean) and matches it on English GraphRAG tasks. On GraphRAG-Bench (Medical), RAGU retrieves the most complete context at every factoid level (evidence recall up to 0.84 vs. $\leq$0.76) and overtakes HippoRAG2 on synthesis tasks; on multi-hop factoid QA, the apparent HippoRAG2 advantage is shown to be largely an answer-format artifact. RAGU is installable via $\texttt{pip install graph_ragu}$, runs on a single GPU, and is released under MIT. The source code is publicly available at https://github.com/RaguTeam/RAGU, and the Meno-Lite-0.1 model can be obtained from https://huggingface.co/bond005/meno-lite-0.1.