Self-reasoning AI // Multi-step approach, looks like Agentic RAG
Relevant context (RAG) increases accuracy — no doubt! Plus multi-hop approach, but hallucinations are possible anyway!
The Retrieval-Augmented Language Model (RALM) has shown remarkable performance on knowledge-intensive tasks by incorporating external knowledge during inference, which mitigates the factual hallucinations inherited in large language models (LLMs). Despite these advancements, challenges persist in the implementation of RALMs, particularly concerning their reliability and traceability. To be specific, the irrelevant document retrieval may result in unhelpful response generation or even deteriorate the performance of LLMs, while the lack of proper citations in generated outputs complicates efforts to verify the trustworthiness of the models. To this end, we propose a novel selfreasoning framework aimed at improving the reliability and traceability of RALMs, whose core idea is to leverage reasoning trajectories generated by the LLM itself. The framework involves constructing self-reason trajectories with three processes: a relevance-aware process, an evidence-aware selective process, and a trajectory analysis process. We have evaluated our framework across four public datasets (two short-form QA datasets, one long-form QA dataset, and one fact verification dataset) to demonstrate the superiority of our method, which can outperform existing state-of-art models and can achieve comparable performance with GPT-4, while only using 2,000 training samples.
RALMs can effectively enhance the performance of LLMs in handling knowledge-intensive tasks. Despite their effectiveness, notable concerns about their reliability and traceability persist. To address these limitations, we propose a novel SELFREASONING framework to improve the performance of RALMs by using reasoning trajectories generated by the LLM itself. It is comprised of a relevance-aware process, an evidence-aware selective process, and a trajectory analysis process. We conduct extensive experiments on four public datasets to demonstrate the superiority of our framework over existing state-of-the-art models.
In this work, we mainly focus on improving the performance of RALMs with a self-reasoning framework on the task of open domain question answering and fact verification. Although we believe our framework can involve the distribution of realworld user questions, as we have evaluated in four public datasets, we acknowledge that we have not explored more challenging scenarios, such as multihop reasoning, code generation, and arithmetic reasoning. In future work, more challenging reasoning tasks, such as arithmetic reasoning, should be explored for the self-reasoning framework. We believe that our framework can effectively mitigate factual hallucinations in LLMs and improve the robustness of RALMs. However, there is still a risk that our method might generate hallucinations.
