Chain of Evidences and Evidence to Generate: Prompting for Context Grounded and Retrieval Augmented Reasoning
Abstract: While chain-of-thoughts (CoT) prompting has revolutionized how LLMs perform reasoning tasks, its current methods and variations (e.g, Self-consistency, ReACT, Reflexion, Tree-of-Thoughts (ToT), Cumulative Reasoning (CR) etc.,) suffer from limitations like limited context grounding, hallucination/inconsistent output generation, and iterative sluggishness. To overcome these challenges, we introduce a novel mono/dual-step prompting framework built upon two unique strategies \textbf{Chain of Evidences (CoE)} and \textbf{Evidence to Generate (E2G)}. Instead of unverified reasoning claims, our innovative approaches leverage the power of "evidence for decision making" by first focusing exclusively on the thought sequences explicitly mentioned in the context which then serve as extracted evidence, guiding the LLM's output generation process with greater precision and efficiency. This simple yet potent approach unlocks the full potential of chain-of-thoughts prompting, facilitating faster, more reliable, and contextually aware reasoning in LLMs. Our framework consistently achieves remarkable results across various knowledge-intensive reasoning and generation tasks, surpassing baseline approaches with state-of-the-art LLMs. For instance, (i) on the LogiQA benchmark using GPT-4, CoE achieves a new state-of-the-art accuracy of 53.8\%, surpassing CoT by 18\%, ToT by 11\%, and CR by 9\%; (ii) CoE with PaLM-2 outperforms the variable-shot performance of Gemini Ultra by 0.9 F1 points, achieving an F1 score of 83.3 on DROP.
Paper Type: Long
Research Area: Generation
Research Area Keywords: Retrieval Augmented Generation
Contribution Types: Model analysis & interpretability, NLP engineering experiment
Languages Studied: English
Submission Number: 5522
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