Kavli Affiliate: Dan Luo
| First 5 Authors: Parshin Shojaee, Sai Sree Harsha, Dan Luo, Akash Maharaj, Tong Yu
| Summary:
Recent advancements in Large Language Models and Retrieval-Augmented
Generation have boosted interest in domain-specific question-answering for
enterprise products. However, AI Assistants often face challenges in
multi-product QA settings, requiring accurate responses across diverse domains.
Existing multi-domain RAG-QA approaches either query all domains
indiscriminately, increasing computational costs and LLM hallucinations, or
rely on rigid resource selection, which can limit search results. We introduce
MKP-QA, a novel multi-product knowledge-augmented QA framework with
probabilistic federated search across domains and relevant knowledge. This
method enhances multi-domain search quality by aggregating query-domain and
query-passage probabilistic relevance. To address the lack of suitable
benchmarks for multi-product QAs, we also present new datasets focused on three
Adobe products: Adobe Experience Platform, Target, and Customer Journey
Analytics. Our experiments show that MKP-QA significantly boosts multi-product
RAG-QA performance in terms of both retrieval accuracy and response quality.
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