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Mirror: A Multiple-perspective Self-Reflection Method for Knowledge-rich Reasoning (2402.14963v2)

Published 22 Feb 2024 in cs.CL and cs.AI

Abstract: While LLMs have the capability to iteratively reflect on their own outputs, recent studies have observed their struggles with knowledge-rich problems without access to external resources. In addition to the inefficiency of LLMs in self-assessment, we also observe that LLMs struggle to revisit their predictions despite receiving explicit negative feedback. Therefore, We propose Mirror, a Multiple-perspective self-reflection method for knowledge-rich reasoning, to avoid getting stuck at a particular reflection iteration. Mirror enables LLMs to reflect from multiple-perspective clues, achieved through a heuristic interaction between a Navigator and a Reasoner. It guides agents toward diverse yet plausibly reliable reasoning trajectory without access to ground truth by encouraging (1) diversity of directions generated by Navigator and (2) agreement among strategically induced perturbations in responses generated by the Reasoner. The experiments on five reasoning datasets demonstrate that Mirror's superiority over several contemporary self-reflection approaches. Additionally, the ablation study studies clearly indicate that our strategies alleviate the aforementioned challenges.

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Citations (2)
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Summary

  • The paper introduces Mirror, a framework that employs a Navigator and a Reasoner to iteratively improve LLM outputs, achieving over 15% performance gains on key tasks.
  • The method utilizes a modified Monte-Carlo Tree Search to explore diverse reasoning paths and validate outputs in unsupervised settings.
  • The framework bridges human-like self-reflection with algorithmic planning, offering actionable insights for applications in automated support and educational technologies.

Mirror: A Multiple-perspective Self-Reflection Method for Knowledge-rich Reasoning

Introduction

The paper "Mirror: A Multiple-perspective Self-Reflection Method for Knowledge-rich Reasoning" introduces a novel framework, Mirror, designed to enhance the reasoning capabilities of LLMs. It addresses the challenges these models face when iteratively refining responses, particularly in knowledge-rich tasks without external validation resources. The proposed method encourages reflection from multiple perspectives through a collaborative effort between two primary entities—Navigator and Reasoner—effectively guiding the reasoning process to improve both diversity and coherence.

Key Contributions

The principal challenges in improving LLMs for iterative reasoning involve their limitations in self-assessment and feedback generation. Without access to ground truth validation, LLMs struggle to evaluate and revise previous outputs effectively. The Mirror framework integrates several key strategies to tackle these limitations:

  1. Multiple-perspective Reflection: By mimicking human-like tutoring, the Navigator offers diverse directions based on constructed heuristic clues, which guide the Reasoner towards a more informed decision-making path.
  2. Intrinsically Motivated Planning in MCTS: Mirror utilizes Monte-Carlo Tree Search (MCTS) with unique adjustments that foster both diversity and consensus within the decision-making process. This approach rewards the exploration of novel, diverse solutions, avoiding stagnation at inefficient reasoning paths.

Implementation Details

Mirror's function relies on a few critical components:

  • State and Action Definition: At each reflection iteration, an LLM's possible states (responses) and actions (revised directions) are systematically explored.
  • Reward System: The Navigator evaluates the diversity of directions and the coherence among outputs. It maximizes a compound reward function that promotes both exploration of new directions and agreement consistency for validation.
  • Tree-based Search Strategy: Customized for unsupervised settings, the tree search is implemented such that it operates without access to ground truth labels. This aligns with the self-improvement goal to simulate various reflection paths efficiently. Figure 1

    Figure 1: An overview of Mirror. It facilitates diverse question-specific directions (represented by different colored dots in the action space) to encourage extensive reflection by the Reasoner.

Experimental Results

The experimental evaluation of Mirror spans multiple datasets, such as MMLU and FEVER, against recent self-reflection approaches. The results highlighted several enhancements:

  • Superior Performance: Mirror showed an average improvement of over 15% relative to competitive baselines without the use of labeled assessment data, confirming its robustness in varied reasoning contexts.
  • Coverage of Diverse Domains: Tests over diverse domains such as STEM and humanities demonstrated that Mirror effectively adapts, maintaining high performance across knowledge differentials.
  • Ablation Studies: These studies reinforced the enhanced performance through strategic diversity in direction generation and validated the critical role of integrated consistency measures in refining LLM outputs. Figure 2

    Figure 2: The Accuracy (acc) and the percentage of samples where the ground truth is included in the tree (ans-presence), with different sizes of search space (Num). Results for GPT-3.5 and Llama13B are shown.

Implications and Future Directions

The Mirror framework contributes to the broader efforts in developing LLMs capable of handling complex reasoning tasks autonomously. Its application extends beyond academic settings to any domain where iterative refinement of textual reasoning can be beneficial, such as automated customer support and educational technologies.

Possible future research directions include refining the consistency metrics for more precise self-assessments and exploring real-time applications for dynamic, context-aware reflection processes. The integration of fine-grained learning signals into the direction generation process could also enhance the adaptability of LLMs in various task settings.

Conclusion

Mirror stands out as a viable framework in advancing LLM reasoning abilities without external knowledge augmentation. It aligns the iterative process with human-like reflection, promoting thorough and effective knowledge processing through diverse, heuristic-driven self-assessments and validations. This approach not only optimizes current reasoning capabilities but also sets the stage for future enhancements of LLM functionalities in AI systems.

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