Meta-Reflection: A Feedback-Free Reflection Learning Framework

• URL: http://arxiv.org/abs/2412.13781v1
• Date: Wed, 18 Dec 2024 12:20:04 GMT
• Title: Meta-Reflection: A Feedback-Free Reflection Learning Framework
• Authors: Yaoke Wang, Yun Zhu, Xintong Bao, Wenqiao Zhang, Suyang Dai, Kehan Chen, Wenqiang Li, Gang Huang, Siliang Tang, Yueting Zhuang,
• Abstract summary: We propose Meta-Reflection, a feedback-free reflection mechanism that requires only a single inference pass without external feedback.<n>Motivated by the human ability to remember and retrieve reflections from past experiences, Meta-Reflection integrates reflective insights into a codebook.<n>To thoroughly investigate and evaluate the practicality of Meta-Reflection in real-world scenarios, we introduce an industrial e-commerce benchmark named E-commerce Customer Intent Detection.
• Score: 57.14485943991588
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite the remarkable capabilities of large language models (LLMs) in natural language understanding and reasoning, they often display undesirable behaviors, such as generating hallucinations and unfaithful reasoning. A prevalent strategy to mitigate these issues is the use of reflection, which refines responses through an iterative process. However, while promising, reflection heavily relies on high-quality external feedback and requires iterative multi-agent inference processes, thus hindering its practical application. In this paper, we propose Meta-Reflection, a novel feedback-free reflection mechanism that necessitates only a single inference pass without external feedback. Motivated by the human ability to remember and retrieve reflections from past experiences when encountering similar problems, Meta-Reflection integrates reflective insights into a codebook, allowing the historical insights to be stored, retrieved, and used to guide LLMs in problem-solving. To thoroughly investigate and evaluate the practicality of Meta-Reflection in real-world scenarios, we introduce an industrial e-commerce benchmark named E-commerce Customer Intent Detection (ECID). Extensive experiments conducted on both public datasets and the ECID benchmark highlight the effectiveness and efficiency of our proposed approach.

Related papers

• ParamMem: Augmenting Language Agents with Parametric Reflective Memory [50.28529749962535]
  Self-reflection enables language agents to iteratively refine solutions, yet often produces repetitive outputs that limit reasoning performance.<n>We introduce ParamMem, a parametric memory module that encodes cross-sample reflection patterns into model parameters.<n>We propose ParamAgent, a reflection-based agent framework that integrates parametric memory with episodic and cross-sample memory.
  arXiv  Detail & Related papers  (2026-02-26T18:28:04Z)
• Learning from Trials and Errors: Reflective Test-Time Planning for Embodied LLMs [63.88783817420284]
  Embodied robots cannot reflect on what went wrong or why, turning deployment into a sequence of independent trials.<n>We introduce Reflective Test-Time Planning, which integrates two modes of reflection: textitreflection-in-action and textitreflection-on-action<n>We also include retrospective reflection, allowing the agent to re-evaluate earlier decisions and perform model updates with hindsight.
  arXiv  Detail & Related papers  (2026-02-24T18:55:18Z)
• LANPO: Bootstrapping Language and Numerical Feedback for Reinforcement Learning in LLMs [73.27182315028021]
  LANPO is a framework that cleanly separates the roles of feedback: language guides exploration, while numerical rewards drive optimization.<n>Our work provides a robust method for integrating historical experiences into the LLM RL loop, creating more effective and data-efficient learning agents.
  arXiv  Detail & Related papers  (2025-10-18T15:51:19Z)
• SAMULE: Self-Learning Agents Enhanced by Multi-level Reflection [14.40651157974557]
  SAMULE is a new framework for self-learning agents powered by a retrospective language model that is trained based on Multi-Level Reflection Synthesis.<n>It first synthesizes high-quality reflections across three complementary levels: Single-Trajectory Learning (micro-level) for detailed error correction; Intra-Task Learning (meso-level) to build error across multiple trials of the same task, and Inter-Task Learning (macro-level) to extract transferable insights based on same typed errors from diverse task failures.
  arXiv  Detail & Related papers  (2025-09-24T21:02:15Z)
• Unveiling the Latent Directions of Reflection in Large Language Models [3.396557052704669]
  We investigate reflection through the lens of latent directions in model activations.<n>New reflection-inducing instructions can be systematically identified, and reflective behavior can be directly enhanced or suppressed.<n>This work opens a path toward mechanistic understanding of reflective reasoning in large language models.
  arXiv  Detail & Related papers  (2025-08-23T11:05:15Z)
• ReflectEvo: Improving Meta Introspection of Small LLMs by Learning Self-Reflection [60.75785864719726]
  We present a novel pipeline, ReflectEvo, to demonstrate that small language models (SLMs) can enhance meta introspection through reflection learning.<n>We construct ReflectEvo-460k, a large-scale, comprehensive, self-generated reflection dataset with broadened instructions and diverse multi-domain tasks.
  arXiv  Detail & Related papers  (2025-05-22T10:03:05Z)
• Perception in Reflection [39.33505560810175]
  We present a perception in reflection paradigm designed to transcend the limitations of current large vision-language models. We propose Reflective Perception (RePer), a dual-model reflection mechanism that systematically alternates between policy and critic models.
  arXiv  Detail & Related papers  (2025-04-09T17:59:02Z)
• In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents [70.12342024019044]
  Large Language Models (LLMs) have made significant progress in open-ended dialogue, yet their inability to retain and retrieve relevant information limits their effectiveness. We propose Reflective Memory Management (RMM), a novel mechanism for long-term dialogue agents, integrating forward- and backward-looking reflections. RMM shows more than 10% accuracy improvement over the baseline without memory management on the LongMemEval dataset.
  arXiv  Detail & Related papers  (2025-03-11T04:15:52Z)
• Instruct-of-Reflection: Enhancing Large Language Models Iterative Reflection Capabilities via Dynamic-Meta Instruction [11.838351314880736]
  Instruct-of-Reflection (IoRT) is a novel and general reflection framework that leverages dynamic-meta instruction to enhance the iterative reflection capability of Large Language Models (LLMs) Our experiments demonstrate that IoRT achieves an average improvement of 10.1% over established baselines in mathematical and commonsense reasoning tasks.
  arXiv  Detail & Related papers  (2025-03-02T14:02:03Z)
• A Survey on All-in-One Image Restoration: Taxonomy, Evaluation and Future Trends [67.43992456058541]
  Image restoration (IR) refers to the process of improving visual quality of images while removing degradation, such as noise, blur, weather effects, and so on. Traditional IR methods typically target specific types of degradation, which limits their effectiveness in real-world scenarios with complex distortions. The all-in-one image restoration (AiOIR) paradigm has emerged, offering a unified framework that adeptly addresses multiple degradation types.
  arXiv  Detail & Related papers  (2024-10-19T11:11:09Z)
• Enhancing Sequential Recommendations through Multi-Perspective Reflections and Iteration [16.10791252542592]
  Sequence recommendation (SeqRec) aims to predict the next item a user will interact with by understanding user intentions and leveraging collaborative filtering information. Large language models (LLMs) have shown great promise in recommendation tasks through prompt-based, fixed reflection libraries, and fine-tuning techniques. MoRE introduces three reflectors for generating LLM-based reflections on explicit preferences, implicit preferences, and collaborative signals.
  arXiv  Detail & Related papers  (2024-09-10T09:58:55Z)
• Towards Flexible Interactive Reflection Removal with Human Guidance [75.38207315080624]
  Single image reflection removal is inherently ambiguous, as both the reflection and transmission components requiring separation may follow natural image statistics. Existing methods attempt to address the issue by using various types of low-level and physics-based cues as sources of reflection signals. In this paper, we aim to explore a novel flexible interactive reflection removal approach that leverages various forms of sparse human guidance.
  arXiv  Detail & Related papers  (2024-06-03T17:34:37Z)
• Pinning "Reflection" on the Agenda: Investigating Reflection in Human-LLM Co-Creation for Creative Coding [20.58817370147299]
  This study investigates situated, moment-to-moment reflection in creative coding under two prompting strategies.<n>Our mixed-method results reveal three distinct reflection types and show that T2 encourages more frequent, strategic, and generative reflection.
  arXiv  Detail & Related papers  (2024-02-15T07:00:06Z)
• Self-Contrast: Better Reflection Through Inconsistent Solving Perspectives [45.87069217634753]
  Research indicates without external feedback, Large Language Model's intrinsic reflection is unstable. Our investigation unveils that the key bottleneck is the quality of the self-evaluated feedback. We advocate Self-Contrast: It adaptively explores diverse solving perspectives tailored to the request, contrasts the differences, and summarizes these discrepancies into a checklist which could be used to re-examine and eliminate discrepancies.
  arXiv  Detail & Related papers  (2024-01-04T00:32:33Z)
• DRDT: Dynamic Reflection with Divergent Thinking for LLM-based Sequential Recommendation [53.62727171363384]
  We introduce a novel reasoning principle: Dynamic Reflection with Divergent Thinking. Our methodology is dynamic reflection, a process that emulates human learning through probing, critiquing, and reflecting. We evaluate our approach on three datasets using six pre-trained LLMs.
  arXiv  Detail & Related papers  (2023-12-18T16:41:22Z)
• Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection [74.51523859064802]
  We introduce a new framework called Self-Reflective Retrieval-Augmented Generation (Self-RAG) Self-RAG enhances an LM's quality and factuality through retrieval and self-reflection. It significantly outperforms state-of-the-art LLMs and retrieval-augmented models on a diverse set of tasks.
  arXiv  Detail & Related papers  (2023-10-17T18:18:32Z)
• Reflexion: Language Agents with Verbal Reinforcement Learning [44.85337947858337]
  Reflexion is a novel framework to reinforce language agents not by updating weights, but through linguistic feedback. It is flexible enough to incorporate various types (scalar values or free-form language) and sources (external or internally simulated) of feedback signals. For example, Reflexion achieves a 91% pass@1 accuracy on the HumanEval coding benchmark, surpassing the previous state-of-the-art GPT-4 that achieves 80%.
  arXiv  Detail & Related papers  (2023-03-20T18:08:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.