RAD-Bench: Benchmarking LLM Capabilities in Retrieval-Augmented Dialogues

Forum topic · 小凯 · 2026-07-05

Summary

RAD-Bench (arXiv:2409.12558, September 2024) is a benchmark from MediaTek Research authors that evaluates how well large language models perform in retrieval-augmented dialogues. The work identifies two key LLM capabilities in this setting: dialogue ability, i.e., appropriately deciding when external retrieval is needed and generating responses that incorporate retrieved documents during information-seeking conversations, and benchmark creation ability, i.e., generating high-quality multi-turn dialogue benchmarks automatically. RAD-Bench distinguishes between pursuit-type (information-seeking) and chit-chat-type dialogue turns, and measures whether models can correctly classify retrieval necessity and produce grounded answers when documents are supplied. The paper analyzes both commercial API models and open-source LLMs, providing insights into where current models fall short in real retrieval-augmented conversational systems. It is relevant to research on RAG evaluation, dialogue systems, and automatic benchmark construction. Note: the forum post itself is largely template metadata; quantitative results should be verified against the original PDF.

RAD-Bench: Evaluating Large Language Models Capabilities in Retrieval Augmented Dialogues

Source: arXiv:2409.12558 (September 2024)

Authors: Tzu-Lin Kuo, Feng-Ting Liao, Mu-Wei Hsieh, Fu-Chieh Chang, Po-Chun Hsu, Da-Shan Shiu

Overview

RAD-Bench is a benchmark for assessing large language models in retrieval-augmented dialogue settings. The paper focuses on two capabilities:

Dialogue ability — whether an LLM can determine when external retrieval is necessary (pursuit vs. chit-chat turns) and generate responses grounded in retrieved documents during multi-turn information-seeking conversations.
Benchmark creation ability — whether an LLM can automatically produce high-quality multi-turn retrieval-augmented dialogue benchmarks, reducing the cost of manual benchmark curation.

Key points

Retrieval-augmented dialogue requires distinguishing information-seeking (pursuit) turns from chit-chat turns, since retrieval is only useful for the former.
RAD-Bench evaluates models on retrieval-necessity classification and grounded response generation given retrieved documents.
The benchmark also tests whether models can serve as generators of new evaluation data, enabling scalable benchmark construction.
The study covers both proprietary and open-source LLMs, highlighting gaps in current models' ability to decide when to retrieve and to stay faithful to retrieved content.

Context

The work belongs to the broader evaluation literature for retrieval-augmented generation (RAG), alongside resources such as:

Evaluation of Retrieval-Augmented Generation: A Survey
ARES: An Automated Evaluation Framework for RAG
A Dataset of Information-Seeking Questions and Answers Anchored in Research Abstracts

Notes

> The original forum post is largely metadata/template content; the abstract text was not reproduced in full. Quantitative results and experimental details should be verified against the original paper PDF.