New Methods Estimate LLM Distribution Differences Efficiently.
Summary
This paper introduces efficient methods for estimating the total variation (TV) distance between two autoregressive distributions, like those from different LLM inference engines, under various access models (sample, logit, noisy logit). The techniques offer a robust way to quantify differences in model outputs, even when KL divergence is infinite, and are empirically validated.
Why it matters
For professionals deploying and evaluating LLMs, accurately measuring the divergence between model outputs from different inference engines or optimization strategies is crucial for quality control, debugging, and ensuring consistent user experiences.
How to implement this in your domain
- 1Utilize the proposed TV distance estimation methods to compare the output distributions of different LLM inference engines or deployment configurations.
- 2Integrate these estimation techniques into your LLM evaluation pipelines to detect subtle shifts in model behavior due to optimizations or hardware changes.
- 3Develop monitoring tools that track TV distance over time to ensure consistency and identify potential regressions in deployed LLMs.
- 4Apply these methods to validate the impact of quantization or custom kernels on the statistical properties of your LLM outputs.
Who benefits
Key takeaways
- LLM deployment choices can significantly alter output distributions, even with identical weights.
- Total variation distance is a robust metric for comparing LLM distributions, unlike KL divergence.
- Efficient algorithms are presented for estimating TV distance under various access models.
- This work provides practical tools for ensuring consistency and quality in LLM deployments.
Original post by Eric Price, Kevin Tian, Zhiyang Xun, Yusong Zhu
"arXiv:2607.19510v1 Announce Type: new Abstract: Modern LLM deployments use a number of implementation choices and inference optimizations (e.g., batching, custom kernels, and quantization) on top of fixed weights, so two engines serving "the same model" can produce meaningfully d…"
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Originally posted by Eric Price, Kevin Tian, Zhiyang Xun, Yusong Zhu on X · view source
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