Lightweight Latent Verifiers for Efficient Meta-Generation Strategies (2504.16760v1)

Abstract: Verifiers are auxiliary models that assess the correctness of outputs generated by base LLMs. They play a crucial role in many strategies for solving reasoning-intensive problems with LLMs. Typically, verifiers are LLMs themselves, often as large (or larger) than the base model they support, making them computationally expensive. In this work, we introduce a novel lightweight verification approach, LiLaVe, which reliably extracts correctness signals from the hidden states of the base LLM. A key advantage of LiLaVe is its ability to operate with only a small fraction of the computational budget required by traditional LLM-based verifiers. To demonstrate its practicality, we couple LiLaVe with popular meta-generation strategies, like best-of-n or self-consistency. Moreover, we design novel LiLaVe-based approaches, like conditional self-correction or conditional majority voting, that significantly improve both accuracy and efficiency in generation tasks with smaller LLMs. Our work demonstrates the fruitfulness of extracting latent information from the hidden states of LLMs, and opens the door to scalable and resource-efficient solutions for reasoning-intensive applications.

Lightweight Latent Verifiers for Efficient Meta-Generation Strategies

This paper presents a novel approach to enhancing the efficiency and accuracy of LLMs in reasoning-intensive tasks through a lightweight latent verifier named LiLaVe. Verifiers are auxiliary models assessing the correctness of outputs from base LLMs, and traditionally, they are large models themselves, posing significant computational overhead. LiLaVe addresses this limitation by extracting correctness signals from the hidden states of the base LLM, offering a more resource-efficient solution.

Methodology

LiLaVe operates by analyzing the internal hidden states of an LLM during the generation process. The authors trained an efficient classifier, using the Gradient Boosted Decision Trees (specifically, XGBoost) to predict the correctness of a model's output based on these hidden states. These classifiers are trained on a modest number of instances (5,000 per benchmark), significantly lowering the resource burden compared to LLM-based verifiers, which require extensive datasets of up to 250,000 examples.

The core innovation involves integrating hidden state information across various layers and tokens into a lightweight verifier. By doing so, LiLaVe reduces the need for the computationally demanding LLM-based verifiers that traditionally accompany generative models.

Experimental Evaluation

The authors evaluated LiLaVe across several mathematical reasoning benchmarks, demonstrating its competitive performance compared to larger LLM-based verifiers and conventional baseline methods. The experiments showed that LiLaVe substantially outperforms other verifiers, achieving high AUC scores indicative of its robustness in predicting correctness.

1. Hidden State Extraction: The paper explored the optimal layers and token positions for extracting hidden states, concluding that meaningful correctness signals could be retrieved from the suffix of decoded sequences and even the initial tokens.
2. Scaling with Temperature: The verifier's performance increased with the sampling temperature used during training, indicating its ability to adapt to diverse generational settings for the base LLM.
3. Meta-Generation Strategies: The paper tested several meta-generation strategies using LiLaVe, including best-of-$n$, weighted majority voting, conditional majority voting, and conditional self-correction. Conditional strategies, in particular, showed significant promise in enhancing accuracy while maintaining computational efficiency.

Implications and Future Directions

The introduction of LiLaVe presents a shift towards more scalable and resource-efficient approaches in enhancing LLM reasoning tasks. The theoretical and practical implications extend to better test-time performance without extensive compute resources, directly impacting fields relying on LLMs for complex problem-solving.

Moreover, the paper opens avenues for integrating verifier-conditioned decoding and improving the robustness of the correctness signal extraction methodology. The potential for LiLaVe to adapt across different datasets and models suggests further exploration into cross-model verifiers, potentially optimizing the verification process through ensemble models or advanced inferences.

Conclusion

LiLaVe significantly contributes to improving the efficiency and accuracy of LLM-based meta-generation strategies by minimizing the computational overhead associated with traditional verifier models. By leveraging hidden state analysis, the verifier advances the scope of reasoning-intensive tasks, paving the way for more adaptive and efficient AI applications in the field of large-scale language processing. Future research should explore the integration of LiLaVe into dynamic decoding scenarios and assess further improvements through adaptive meta-generative strategies.