Entrospect: Information-Theoretic Self-Reflection Elicits Better Response Refinement of Small Language Models (2025)

Abstract: Self-reflection helps de-hallucinate Large Language Models (LLMs). However, the effectiveness of self-reflection remains insufficiently validated in the context of Small Language Models (SLMs), which exhibit limited semantic capacities. In particular, we demonstrate that the conventional self-reflection paradigm, such as Self-Refine, fails to deliver robust response refinement for models with parameter sizes of 10 billion or smaller, even when compared to generations elicited through Chain-of-Thought (CoT) prompting. To improve SLMs’ self-reflection, we redesign Self-Refine and introduce Entrospect (ENTROpy-aware IntroSPECTion), an information-theoretic framework based on prompt engineering.We evaluated Entrospect using accuracy and average time consumption metrics to comprehensively assess its precision and computational efficiency. Experiments conducted across four distinct SLMs and four baseline methods demonstrate that Entrospect achieves state-of-the-art performance on validation tasks. Notably, under identical model and data settings, Entrospect delivers a remarkable improvement of up to 36.2 in reasoning accuracy while enhancing computational efficiency by as much as 10 times compared to its predecessor, Self-Refine.

## introduction large language models have advanced rapidly, impacting many fields with improved natural language generation @xcite . however, their tendency to produce hallucinations-especially counterfactual ones-poses a critical challenge to reliability @xcite . hallucinations occur when models generate factually incorrect or nonsensical outputs, undermining their trustworthiness and hindering real-world adoption. addressing this issue is essential for improving their practical utility and acceptance @xcite . to address these challenges, self-reflection has been proposed as a solution to counterfactual hallucinations, particularly for black-box models with inaccessible parameters @xcite . however, its effectiveness is limited in small language models (slms), which often lack sufficient semantic capabilities, inducing frequent occurences of imperfect feedback, encompassing the self-reflected revision suggestions. given the widespread use of slms in resource-constrained environments @xcite , this limitation is particularly significant. in such cases, self-reflection may fail to consistently assist in the corrections of outputs, highlighting the need for more robust and scalable approaches. given the challenges of applying self-reflection to slms, a key question arises: how might we construct a framework that effectively integrates selfreflection to improve the precision of slm outputs, all while preserving the computational efficiency? in response to this challenge, we propose entrospect 1 , an information-theoretic framework predicated on self-refine that lessens the dependency on explicit semantic outputs from the model. contrary to self-refine's equal consideration of all revision suggestions, entrospect employs an unsupervised mechanism to identify the most effective revision candidate, minimizing the impact of inferior ones, as illustrated in figure 1 . specifically, entrospect is implemented with an optimal revision suggestion selector (orss) inspired by @xcite and @xcite , the orss intervenes between the "reflect" and the "refine" stages that are tightly connected in the self-refine's pipeline. it evaluates revision suggestions generated through self-reflection and identifies the one that minimizes the semantic uncertainty in the 1 https://github.com/henryyantq/entrospect model's refinement of the prior response, where low-quality suggestions conceivably ruining the successive procedures are ruled out. this selective approach distinguishes entrospect from its predecessors, enhancing both the quality and reliability of the refined responses. architecturally, entrospect retains the simplicity and efficiency of self-refine, operating as a parameter-free, recurrent finite-state machine (fsm) where modules are interconnected through purpose-specific prompts. this design ensures computational efficiency while maintaining the flexibility to adapt to diverse conversational ai tasks. figure 2 summarizes the multifaceted contributions of entrospect, the central focus of this study. we evaluated entrospect on natural language reasoning tasks, including the math dataset @xcite for math reasoning and halueval @xcite for hallucination detection. the results show entrospect outperforms baselines like zero-shot, few-shot, chain-of-thought (cot), and self-refine. these findings underscore two critical advances: ## conclusion this paper introduces entrospect, an optimized self-refine framework that leverages an information-theoretic optimal revision suggestion selector to provide optimal revision suggestions during the self-reflection stage while eliminating ineffective ones for efficient refinement of initial responses from slms. besides, the convergence of entrospect is made possible with a dedicated semantic similarity-determined stopping condition. through our holistic evaluations, entrospect claimed superior performance relative to the baseline methods on both of our reasoning tasks across four slms of diverse parameter sizes, obtaining a maximum 36.2% reasoning accuracy uplift and at most 10 times the computational efficiency exclusively over its antecedent, self-refine. we aspire for this study to inspire further advancements in small language models research and furnishes new perspectives for information-theoretic prompt engineering.