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Analyzing Ranking of Large Language Models with Prediction-Powered Framework


Core Concepts
The author introduces a statistical framework to measure uncertainty in rankings constructed using pairwise comparisons by humans and large language models. The framework provides rank-sets for each model under comparison, ensuring coverage guarantees for the true ranking consistent with human preferences.
Abstract
Large language models are evaluated based on alignment with human preferences through pairwise comparisons. A statistical framework is introduced to quantify uncertainty in rankings constructed using human and model preferences. The framework ensures coverage guarantees for the true ranking positions of models. Key Points: Large language models are ranked based on alignment with human preferences. Pairwise comparisons by humans and models are used to construct rankings. A statistical framework quantifies uncertainty in these rankings. Rank-sets provide possible ranking positions for each model. Coverage guarantees ensure consistency with true human preferences.
Stats
One of the most popular ways to elicit human preferences utilizes pairwise comparisons between the outputs provided by different models to the same inputs. Given a small set of pairwise comparisons by humans and a large set of pairwise comparisons by a model, our framework provides a rank-set—a set of possible ranking positions—for each of the models under comparison. Our framework measures uncertainty using rank-sets—sets of possible ranking positions that each model can take.
Quotes
"Large language models are often ranked according to their level of alignment with human preferences." "One of the most popular paradigms to rank a set of LLMs according to their level of alignment with human preferences utilizes pairwise comparisons." "Our framework measures uncertainty using rank-sets—sets of possible ranking positions that each model can take."

Key Insights Distilled From

by Ivi Chatzi,E... at arxiv.org 02-29-2024

https://arxiv.org/pdf/2402.17826.pdf
Prediction-Powered Ranking of Large Language Models

Deeper Inquiries

How can real-world data validate the statistical framework introduced

Real-world data can validate the statistical framework introduced by comparing the rankings generated using the framework with actual human preferences. By collecting pairwise comparisons from both humans and a strong large language model, researchers can assess how closely the rankings align with each other. If there is consistency between the rankings derived from real-world data and those produced by the framework, it would indicate that the statistical approach effectively captures human preferences. Additionally, conducting experiments with diverse datasets across various domains can further validate the robustness and generalizability of the framework.

What implications does mismatched distributions between human and model preferences have on the validity of rankings

Mismatched distributions between human and model preferences can significantly impact the validity of rankings derived from large language models (LLMs). When there is a discrepancy in these distributions, it raises concerns about whether LLMs accurately reflect human preferences. This mismatch could lead to biased or inaccurate rankings, undermining the reliability of evaluations based on these models. It highlights potential limitations in using LLMs as proxies for human judgment and emphasizes the importance of understanding and addressing such discrepancies to ensure trustworthy ranking outcomes.

How might exploring other measures of uncertainty beyond rank-sets enhance our understanding

Exploring other measures of uncertainty beyond rank-sets could offer valuable insights into evaluating large language models (LLMs) more comprehensively. By considering alternative approaches to quantify uncertainty in rankings, researchers can gain a deeper understanding of factors influencing model performance and alignment with human preferences. For instance, incorporating probabilistic methods or Bayesian techniques may provide richer information on uncertainties associated with LLM evaluations. These additional measures could enhance decision-making processes regarding model selection, deployment, and improvement strategies by offering nuanced perspectives on ranking reliability under varying conditions or scenarios.
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