Rethinking LLM Evaluation: Beyond Benchmarks

Large language models (LLMs) are often evaluated based on their performance on standardized benchmarks. Yet, accuracy alone doesn’t tell the full story of a model's capabilities. The current reliance on leaderboards presents challenges such as data contamination and a narrow scope of tasks. The real issue? Benchmarks capture what models output, not how they process information or deal with uncertainty.

Introducing Latent Performance Profiling

Enter Latent Performance Profiling (LPP), a novel approach that focuses on the intrinsic aspects of LLMs. LPP derives diagnostics from hidden activations and output distributions, providing a task-agnostic framework. This method reveals scale-independent traits, offering a more nuanced view of models' internal workings. Unlike static accuracy scores, LPP provides stable, architecture-sensitive signatures, allowing for interpretable comparisons between models of similar sizes.

In an extensive empirical analysis covering eight LLMs, ranging from 0.5B to 14B parameters, LPP uncovered contrasting latent profiles among models with similar benchmark scores. Some models differed in entropy or adaptability, revealing hidden vulnerabilities that traditional evaluations miss.

Why It Matters

Why should we care about LPP? Because relying solely on benchmark scores is like judging a book by its cover. The paper's key contribution is its ability to reveal what lies beneath the surface. This approach can lead to more reliable model selection, safety assessments, and evaluations beyond surface-level accuracy.

LPP enables the design of synthetic probes for uncertainty and symbolic reasoning, aligning with intrinsic metrics while sidestepping leaderboard bias. This shift in evaluation methodology is essential as LLMs become more ingrained in real-world applications where reliability and understanding matter.

The Takeaway

Shouldn't we ask ourselves if we're truly understanding what these models can do? LPP offers a roadmap to move beyond superficial assessments. By reporting LPP alongside traditional benchmarks, we gain a deeper, interpretable understanding of model behavior. In a world where LLMs are increasingly impactful, this approach provides a foundation for more informed decisions about their use and deployment.