AlpacaEval: Revolutionizing Model Evaluation with LLM-Based Automatic Tools
Snippet: "Evaluation of instruction-following models (e.g., ChatGPT) typically requires human interactions. This is time-consuming, expensive, and hard to replicate. AlpacaEval in an LLM-based automatic evaluation that is fast, cheap, replicable, and validated against 20K human annotations. It is particularly useful for model development. Although we improved over prior automatic evaluation pipelines, there are still fundamental limitations like the preference for longer outputs. AlpacaEval provides the following:
Leaderboard: a leaderboard of common models on the AlpacaEval evaluation set. Caution: Automatic evaluators (e.g. GPT-4) may be biased towards models that generate longer outputs and/or that were fine-tuned on the model underlying the evaluator (e.g. GPT-4).
Automatic evaluator: an automatic evaluator that has high agreement with humans (validated on 20K annotations). We evaluate a model by measuring the fraction of times a powerful LLM (e.g. GPT-4) prefers the outputs from that model over outputs from a reference model. Our evaluators enable caching and output randomization by default.
Toolkit for building automatic evaluators: a simple interface for building advanced automatic evaluators (e.g. with caching, batching, or multi-annotators) and analyzing them (quality, price, speed, statistical power, bias, variance etc).
Human evaluation data: 20K human preferences between a given and reference model on the AlpacaFarm evaluation set. 2.5K of these are cross-annotations (4 humans annotating the same 650 examples).
AlpacaEval dataset: a simplification of AlpacaFarm's evaluation set, where "instructions" and "inputs" are merged into one field, and reference outputs are longer. Details here.
When to use and not use AlpacaEval?
When to use AlpacaEval? Our automatic evaluator is a quick and cheap proxy for human evaluation of simple instruction-following tasks. It is useful if you have to run many evaluations quickly, e.g., during model development.
When not to use AlpacaEval? As any other automatic evaluator, AlpacaEval should not replace human evaluation in high-stake decision-making, e.g., to decide on model release. In particular, AlpacaEval is limited by the fact that (1) the instructions in the eval set might not be representative of advanced usage of LLMs; (2) automatic evaluators may have biases such as favoring style over factuality of the answer; and (3) AlpacaEval does not measure the risks that a model could cause. Details in limitations."
ShellLM
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AlpacaEval: Revolutionizing Model Evaluation with LLM-Based Automatic Tools
Snippet: "Evaluation of instruction-following models (e.g., ChatGPT) typically requires human interactions. This is time-consuming, expensive, and hard to replicate. AlpacaEval in an LLM-based automatic evaluation that is fast, cheap, replicable, and validated against 20K human annotations. It is particularly useful for model development. Although we improved over prior automatic evaluation pipelines, there are still fundamental limitations like the preference for longer outputs. AlpacaEval provides the following:
When to use and not use AlpacaEval?
When to use AlpacaEval? Our automatic evaluator is a quick and cheap proxy for human evaluation of simple instruction-following tasks. It is useful if you have to run many evaluations quickly, e.g., during model development.
When not to use AlpacaEval? As any other automatic evaluator, AlpacaEval should not replace human evaluation in high-stake decision-making, e.g., to decide on model release. In particular, AlpacaEval is limited by the fact that (1) the instructions in the eval set might not be representative of advanced usage of LLMs; (2) automatic evaluators may have biases such as favoring style over factuality of the answer; and (3) AlpacaEval does not measure the risks that a model could cause. Details in limitations."
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