Title: FineKB: Domain-Adaptive Issue Summarization and Cluster-Aware Retrieval for Support Knowledge Bases
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Knowledge base retrieval, Large language models, Domain adaptation, Summarization, Clustering, Information retrieval, Enterprise support, Weak supervision, Fine-tuning

Abstract: Retrieving relevant knowledge base (KB) articles for enterprise support cases is challenging, as noisy and verbose case descriptions often exhibit low similarity to KB content. We present FineKB, a domain-adaptive system that leverages large language models (LLMs), parameter-efficient fine-tuning, and cluster-aware retrieval to improve case-to-KB matching. FineKB consists of three key components: (i) a dataset builder that generates pseudo-labeled issue summaries from historical case logs for weak supervision; (ii) a domain-adaptive issue summarizer, fine-tuned with parameter-efficient methods, to produce concise and terminology-aligned summaries; and (iii) a cluster-aware retrieval engine that constructs per-KB case clusters and indexes them with multi-centroid representations to capture diverse sub-problems. Experiments on large-scale enterprise support data demonstrate that FineKB boosts retrieval performance from under 24% Recall@3 in the KB content-only baseline to over 66% Recall@3 with cluster-aware indexing. Moreover, lightweight 8B fine-tuned models match the retrieval performance of a 70B LLM, demonstrating the efficiency of domain adaptation. These results highlight that FineKB effectively bridges the gap between noisy case descriptions and structured KB articles, offering a scalable and generalizable framework for enterprise support.

TL;DR: FineKB leverages domain-adapted LLM issue summarization and clustering-based retrieval to bridge noisy case descriptions with KB articles, improving retrieval accuracy at top-3 from 24% to 66%.