OmniSearch Breaks Multimodal Retrieval Barriers with Intelligent Dynamic Planning
Discover OmniSearch: a groundbreaking framework with adaptive dynamic planning, solving complex multimodal problems with precise and efficient retrieval.
With the widespread adoption of Multimodal Large Language Models (MLLM), these models often experience "hallucination" when handling complex problems, generating content that is inconsistent with factual information.
Multimodal Retrieval-Augmented Generation (mRAG) technology aims to address this issue by retrieving information from external knowledge bases. However, existing mRAG methods heavily rely on predefined retrieval processes, making it difficult to meet the complex and dynamic knowledge demands of real-world scenarios.
To solve this problem, Alibaba DAMO Academy's RAG team has developed OmniSearch, the industry’s first multimodal retrieval-augmented generation framework with adaptive planning capabilities.
OmniSearch dynamically breaks down complex problems, adjusting the retrieval strategy based on the current context and retrieval results. Simulating human problem-solving behavior significantly improves retrieval efficiency and the accuracy of generated content.
OmniSearch: A New Era for Multimodal Retrieval
Limitations of Traditional mRAG
Existing mRAG methods typically use fixed retrieval processes. When faced with complex multimodal problems, models lack the flexibility to adapt their retrieval strategies, leading to two major issues:
Non-adaptive Retrieval: Retrieval strategies cannot adjust based on intermediate steps or new discoveries, making it difficult to fully understand or validate multimodal inputs, resulting in incomplete information acquisition.
Overloaded Retrieval: Single queries are overly relied upon during retrieval, making it challenging to acquire the key knowledge needed to solve a problem. This often results in excessive irrelevant information, increasing inference difficulty.
OmniSearch addresses these limitations through innovative solutions.
By simulating human thinking processes, OmniSearch dynamically decomposes complex multimodal problems into subproblems and formulates specific retrieval steps and strategies for each. This ensures precise answers.