Enhancing Reliability in Community Question Answering with an Expert-Oriented RAG System.
| Authors | Seyyede Zahra Aftabi & Saeed Farzi |
| Year | 2026 |
| Venue | EACL 2026 |
| Paper | View on ACL Anthology |
| PDF | Download |
Abstract
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Engineering Breakdown
Plain English
This paper presents an expert-oriented RAG (Retrieval-Augmented Generation) system designed to improve the reliability of answers in community question-answering platforms. The system appears to focus on leveraging expert knowledge or expertise signals to make retrieved context and generated responses more trustworthy, rather than relying solely on raw community-sourced answers.
Key Engineering Insight
The core insight is that RAG systems for Q&A need to be explicitly designed around expertise signals — filtering or ranking retrieved documents by expert credibility rather than just relevance — which fundamentally changes how you architect your retrieval and ranking pipeline.
Why It Matters for Engineers
Community Q&A platforms (Stack Overflow, Reddit, Yahoo Answers) suffer from unreliable answers mixed with quality ones. Production systems need to surface trustworthy answers reliably; this work directly addresses that by showing how to build RAG that incorporates expert signals, reducing hallucinations and wrong answers users actually see.
Research Context
Standard RAG systems retrieve based on semantic relevance, but community Q&A introduced the problem of 'relevance without reliability' — an answer can be topically related but factually wrong. This paper advances the field by showing that explicitly modeling and incorporating expert reputation or expertise into the RAG retrieval step improves end-to-end system reliability, building on recent RAG improvements but with a domain-specific focus on Q&A platforms.
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