On the Intrinsic Self-Correction Capability of LLMs: Uncertainty and Latent Concept

• URL: http://arxiv.org/abs/2406.02378v2
• Date: Thu, 07 Nov 2024 20:30:58 GMT
• Title: On the Intrinsic Self-Correction Capability of LLMs: Uncertainty and Latent Concept
• Authors: Guangliang Liu, Haitao Mao, Bochuan Cao, Zhiyu Xue, Xitong Zhang, Rongrong Wang, Jiliang Tang, Kristen Johnson,
• Abstract summary: Large Language Models (LLMs) are able to improve their responses when instructed to do so, a capability known as self-correction. In intrinsic self-correction is evident in various applications, but how and why it is effective remains unknown. We show that intrinsic self-correction can be progressively improved, allowing it to approach a converged state.
• Score: 36.27550578296276
• License:
• Abstract: Large Language Models (LLMs) are able to improve their responses when instructed to do so, a capability known as self-correction. When instructions provide only the task's goal without specific details about potential issues in the response, LLMs must rely on their internal knowledge to improve response quality, a process referred to as intrinsic self-correction. The empirical success of intrinsic self-correction is evident in various applications, but how and why it is effective remains unknown. In this paper, we unveil that intrinsic self-correction can be progressively improved, allowing it to approach a converged state. Our findings are verified in: (1) the scenario of multi-round question answering, by comprehensively demonstrating that intrinsic self-correction can progressively introduce performance gains through iterative interactions, ultimately converging to stable performance; and (2) the context of intrinsic self-correction for enhanced morality, in which we provide empirical evidence that iteratively applying instructions reduces model uncertainty towards convergence, which then leads to convergence of both the calibration error and self-correction performance, ultimately resulting in a stable state of intrinsic self-correction. Furthermore, we introduce a mathematical formulation and a simulation task indicating that the latent concepts activated by self-correction instructions drive the reduction of model uncertainty. Based on our experimental results and analysis of the convergence of intrinsic self-correction, we reveal its underlying mechanism: consistent injected instructions reduce model uncertainty which yields converged, improved performance.

Related papers

• ReVISE: Learning to Refine at Test-Time via Intrinsic Self-Verification [53.80183105328448]
  Refine via Intrinsic Self-Verification (ReVISE) is an efficient framework that enables LLMs to self-correct their outputs through self-verification. Our experiments on various reasoning tasks demonstrate that ReVISE achieves efficient self-correction and significantly improves reasoning performance.
  arXiv  Detail & Related papers  (2025-02-20T13:50:02Z)
• Bridging Internal Probability and Self-Consistency for Effective and Efficient LLM Reasoning [53.25336975467293]
  We present the first theoretical error decomposition analysis of methods such as perplexity and self-consistency. Our analysis reveals a fundamental trade-off: perplexity methods suffer from substantial model error due to the absence of a proper consistency function. We propose Reasoning-Pruning Perplexity Consistency (RPC), which integrates perplexity with self-consistency, and Reasoning Pruning, which eliminates low-probability reasoning paths.
  arXiv  Detail & Related papers  (2025-02-01T18:09:49Z)
• Confidence v.s. Critique: A Decomposition of Self-Correction Capability for LLMs [34.203575667558454]
  Large Language Models (LLMs) can correct their self-generated responses, but a decline in accuracy after self-correction is also witnessed. We decompose the self-correction capability into confidence (being confident to correct answers) and critique (turning wrong answers to correct) capabilities. Our strategy outperforms vanilla SFT in both capabilities and achieves much higher accuracy after self-correction.
  arXiv  Detail & Related papers  (2024-12-27T08:09:11Z)
• Is Moral Self-correction An Innate Capability of Large Language Models? A Mechanistic Analysis to Self-correction [5.271054803267951]
  We aim to answer two fundamental questions for moral self-correction. We examine how different self-correction components interact to intervene the embedded morality within hidden states. We propose a validation framework, self-distinguish, that requires effective self-correction.
  arXiv  Detail & Related papers  (2024-10-27T16:52:21Z)
• Confidence Matters: Revisiting Intrinsic Self-Correction Capabilities of Large Language Models [23.42725642076256]
  Large Language Models (LLMs) have catalyzed an increasing interest in their self-correction capabilities. This paper presents a comprehensive investigation into the intrinsic self-correction of LLMs. We develop an "If-or-Else" (IoE) prompting framework, designed to guide LLMs in assessing their own "confidence"
  arXiv  Detail & Related papers  (2024-02-19T21:38:02Z)
• Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-Evaluation [71.91287418249688]
  Large language models (LLMs) often struggle with factual inaccuracies, even when they hold relevant knowledge. We leverage the self-evaluation capability of an LLM to provide training signals that steer the model towards factuality. We show that the proposed self-alignment approach substantially enhances factual accuracy over Llama family models across three key knowledge-intensive tasks.
  arXiv  Detail & Related papers  (2024-02-14T15:52:42Z)
• N-Critics: Self-Refinement of Large Language Models with Ensemble of Critics [5.516095889257118]
  We propose a self-correction mechanism for Large Language Models (LLMs) to mitigate issues such as toxicity and fact hallucination. This method involves refining model outputs through an ensemble of critics and the model's own feedback.
  arXiv  Detail & Related papers  (2023-10-28T11:22:22Z)
• Improving the Reliability of Large Language Models by Leveraging Uncertainty-Aware In-Context Learning [76.98542249776257]
  Large-scale language models often face the challenge of "hallucination" We introduce an uncertainty-aware in-context learning framework to empower the model to enhance or reject its output in response to uncertainty.
  arXiv  Detail & Related papers  (2023-10-07T12:06:53Z)
• Large Language Models Cannot Self-Correct Reasoning Yet [78.16697476530994]
  Large Language Models (LLMs) have emerged as a groundbreaking technology with their unparalleled text generation capabilities. Concerns persist regarding the accuracy and appropriateness of their generated content. A contemporary methodology, self-correction, has been proposed as a remedy to these issues.
  arXiv  Detail & Related papers  (2023-10-03T04:56:12Z)
• Robustness and Accuracy Could Be Reconcilable by (Proper) Definition [109.62614226793833]
  The trade-off between robustness and accuracy has been widely studied in the adversarial literature. We find that it may stem from the improperly defined robust error, which imposes an inductive bias of local invariance. By definition, SCORE facilitates the reconciliation between robustness and accuracy, while still handling the worst-case uncertainty.
  arXiv  Detail & Related papers  (2022-02-21T10:36:09Z)

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.