Similarity is Not All You Need: Endowing Retrieval Augmented Generation with Multi Layered Thoughts
(2405.19893)Abstract
In recent years, LLMs have made remarkable achievements in various domains. However, the untimeliness and cost of knowledge updates coupled with hallucination issues of LLMs have curtailed their applications in knowledge intensive tasks, where retrieval augmented generation (RAG) can be of help. Nevertheless, existing retrieval augmented models typically use similarity as a bridge between queries and documents and follow a retrieve then read procedure. In this work, we argue that similarity is not always the panacea and totally relying on similarity would sometimes degrade the performance of retrieval augmented generation. To this end, we propose MetRag, a Multi layEred Thoughts enhanced Retrieval Augmented Generation framework. To begin with, beyond existing similarity oriented thought, we embrace a small scale utility model that draws supervision from an LLM for utility oriented thought and further come up with a smarter model by comprehensively combining the similarity and utility oriented thoughts. Furthermore, given the fact that the retrieved document set tends to be huge and using them in isolation makes it difficult to capture the commonalities and characteristics among them, we propose to make an LLM as a task adaptive summarizer to endow retrieval augmented generation with compactness-oriented thought. Finally, with multi layered thoughts from the precedent stages, an LLM is called for knowledge augmented generation. Extensive experiments on knowledge-intensive tasks have demonstrated the superiority of MetRag.
Overview
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The MetRag framework introduces a novel approach to enhance retrieval-augmented generation by integrating similarity-based and utility-based retrieval mechanisms.
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The framework employs task-adaptive summarization using a LLM to produce compact and relevant document summaries.
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Extensive experiments demonstrate the superiority of MetRag over traditional methods, showing significant improvements in retrieval efficacy and performance on knowledge-intensive tasks.
An Evaluation of MetRag: Multi–layered Thoughts Enhanced Retrieval-Augmented Generation Framework
In this paper, the authors address critical limitations associated with the use of LLMs in knowledge-intensive tasks. Due to the untimeliness and cost of updating the knowledge base of LLMs, paired with the inherent issue of hallucinations, the performance of these models can suffer considerably in tasks requiring extensive, up-to-date knowledge. To mitigate these issues, the authors propose a novel framework named MetRag, which employs a multi-layered thoughts approach to enhance retrieval-augmented generation (RAG).
Key Contributions
Combination of Similarity and Utility-Oriented Thoughts: The authors introduce an innovative way to enhance retrieval systems by combining similarity-based and utility-based retrieval mechanisms. Traditional similarity-based retrieval often falls short by merely considering semantic closeness, sometimes ignoring the more utility-oriented aspects critical for accurate knowledge retrieval. By integrating an LLM as an external supervisor, the proposed utility model surpasses mere similarity to capture documents that provide significant information relevant to the input query.
Task-Adaptive Summarization: One of the notable enhancements introduced in MetRag is the use of an LLM as a task-adaptive summarizer. This allows the system to distill large sets of retrieved documents into more compact and relevant summaries. By addressing the inherent challenge of using documents in isolation, which often leads to redundancy or degrade performance, this summarization model enhances the efficiency and accuracy of the retrieval-augmented generation process.
Knowledge-Augmented Generation: Finally, the multi-layered thoughts gathered through the stages of similarity, utility, and compactness are utilized for knowledge-augmented generation. This results in an output that is not only contextually relevant but also more accurate, thanks to the comprehensively distilled information.
Experimental Validation
The authors conducted extensive experiments across several knowledge-intensive tasks, including datasets like Natural Questions (NQ), TriviaQA, HotpotQA, and PopQA. The results demonstrate the superiority of MetRag over traditional retrieval-augmented generation methods and several strong baselines.
Key findings include:
- Retrieval efficacy showed significant improvements over non-retrieval baselines.
- Performance on long-tail queries witnessed notable benefits, emphasizing the value of retrieval in cases where pre-trained knowledge models may falter.
- Greater instruction-following ability, evidenced by improved F1 scores, illustrates the enhanced summarization and response accuracy attributable to task-adaptive summarization and multi-layered thought processes.
Breakdown of Innovations
Similarity and Utility Models
By utilizing BART embeddings for similarity assessment and LLMs for utility evaluation, the MetRag framework leverages both semantic closeness and relevance to the query. The integration strategy ensures that the documents providing the highest utility are included in the final set used for generation tasks, balancing between stability and relevance.
Task-Adaptive Summarizer
The summarizer is fine-tuned using teacher-student mechanisms, with a small-scale model distilled from a larger, sophisticated teacher model like GPT-4. Further alignment is achieved using a reward model to ensure that generated summaries align well with the specific tasks, improving both efficiency and output quality.
Implications and Future Work
The MetRag framework's enhanced performance in retrieval-augmented generation tasks has far-reaching implications for the development of more reliable, efficient, and accurate LLM applications. By advocating for a balanced approach combining similarity, utility, and succinct summarization, this research opens new avenues for enhancing LLMs in knowledge-intensive domains.
Potential future directions could include:
- Scalability and adaptation tests across even broader domains and languages.
- Real-time performance evaluation to further refine the retrieval process, particularly in dynamic knowledge environments.
- Investigations into the stability of the utility model across varying scales and types of LLMs to optimize its reliability and performance.
Conclusion
MetRag presents a significant stride in the domain of retrieval-augmented generation, addressing pressing issues in traditional LLM applications through a nuanced, multi-layered approach. By synthesizing similarity, utility, and compactness thoughts into a cohesive framework, MetRag not only enhances the accuracy and efficiency of LLMs but also sets a new benchmark for future research in this area. The extensive experimental results affirm its efficacy and encourage further exploration into multi-model and multi-strategy integrations in AI research.
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