Self-Reflective AI: Self-RAG for Multi-AI-Agents explained

NEW Self Reflective Retrieval Augmented Generation - Self RAG explained.

The SELF-RAG framework aims to enhance the capabilities of large language models (LLMs) by integrating retrieval and self-critique mechanisms into the model's generation process.

The Self-Reflective Retrieval-Augmented Generation (SELF-RAG) framework aims to address the limitations inherent to current Retrieval-Augmented Generation (RAG) models, which often produce text without considering the relevance or necessity of the retrieved data. SELF-RAG introduces a novel on-demand retrieval mechanism along with "reflection tokens" that enable the model to self-evaluate and adapt its responses. The architecture is trained end-to-end and employs an arbitrary large language model (LLM) that outputs both task-related text and reflection tokens, which fall into two categories: retrieval and critique tokens. The retrieval tokens trigger the on-demand retriever, allowing for selective information extraction based on the contextual requirements of the task. Subsequently, critique tokens are used to perform an introspective assessment of the generated text in terms of its factual accuracy and overall quality, thereby allowing SELF-RAG to not only adapt its future responses but also to facilitate easier fact verification through citations.

Empirical evaluations show that SELF-RAG demonstrates significant performance improvements across various tasks when compared to state-of-the-art LLMs and other RAG-based methods. The framework supports a customizable decoding algorithm influenced by reflection token probabilities, offering adaptability for different downstream applications. This design ethos makes SELF-RAG a more versatile, robust, and accurate alternative for generating factually sound and contextually relevant text. Moreover, the architecture mitigates some of the existing issues in RAG models, such as the introduction of irrelevant or off-topic passages, by leveraging the self-reflective mechanism for more granular control over the retrieval and generation process.

ARXIV pre-print:
SELF-RAG: LEARNING TO RETRIEVE, GENERATE, AND CRITIQUE THROUGH SELF-REFLECTION

[uiixzrw]

Thanks for putting together the description and implications of the paper as well as demoing the code. I agree with your comments about keeping GNNs in mind for future development as it appears that all of the various forms of RAG will likely only take us so far. But, in the interim, it's great to see improvements like SELF-RAG. Along with these methods, are you familiar with David Shapiro's approach using SPRs (Sparse Priming Representations)? I'm wondering if it could be used as a compression strategy, reducing the number of tokens used per self reflective / critique step. Perhaps as part of the critique, we could also generate SPRs that could then be trained into the main model, thus reducing the number of times that the main model requested a retrieval action.

[stuartpatterson]

AGI will only use the present LLMs like an encyclopaedia. Great content, cheers!

[StoianAtanasov]

Thanks for the wonderful presentation.

[henkhbit]

Love Self RAG, because it's open source. I love especially your ice cream😂 in which Hilbert space can I buy strawberries with Riemann flavor with an Escher twist🤔

[matten_zero]

Self RAG + Dave Shapiro's SPR is THE WAY