Rethinking LLM-as-a-Judge: Representation-as-a-Judge with Small Language Models via Semantic Capacity Asymmetry

Large language models (LLMs) are widely used as reference-free evaluators via prompting, but this "LLM-as-a-Judge" paradigm is costly, opaque, and sensitive to prompt design. In this work, we investigate whether smaller models can serve as efficient evaluators by leveraging internal representations instead of surface generation. We uncover a consistent empirical pattern: small LMs, despite with weak generative ability, encode rich evaluative signals in their hidden states. This motivates us to propose the Semantic Capacity Asymmetry Hypothesis: evaluation requires significantly less semantic capacity than generation and can be grounded in intermediate representations, suggesting that evaluation does not necessarily need to rely on large-scale generative models but can instead leverage latent features from smaller ones. Our findings motivate a paradigm shift from LLM-asa-Judge to Representation-as-a-Judge, a decoding-free evaluation strategy that probes internal model structure rather than relying on prompted output. We instantiate this paradigm through INSPECTOR, a probing-based framework that predicts aspect-level evaluation scores from small model representations. Experiments on reasoning benchmarks (GSM8K, MATH, GPQA) show that INSPEC-TOR substantially outperforms prompting-based small LMs and closely approximates full LLM judges, while offering a more efficient, reliable, and interpretable alternative for scalable evaluation. The code and data are available at: https: //github.com/zhuochunli/Representation-as-a-judge INTRODUCTION Large language models (LLMs) have demonstrated remarkable capabilities in generation, reasoning, and alignment tasks (Achiam et al., 2023; Touvron et al., 2023) . A growing number of works leverage the paradigm of LLM-as-a-Judge, wherein powerful LLMs are prompted to assess the quality of generated outputs without access to ground-truth references (Chang et al., 2024; Prasad et al., 2023) . This approach has achieved strong empirical results in reference-free evaluation across domains such as summarization and complex reasoning (He et al., 2023; Zhang et al., 2024). However, this prompt-based evaluation paradigm has important limitations. First, it requires autoregressive decoding, making it computationally expensive even for single-point evaluations. Second, it relies on large proprietary models (e.g., GPT-4), whose internal mechanisms remain opaque and unverifiable. Lastly, its effectiveness depends heavily on prompt engineering, raising concerns about reproducibility, robustness, and scaling (