From Lists to Emojis: How Format Bias Affects Model Alignment
This work addresses the problem of format biases affecting model alignment and evaluation in AI, highlighting a critical issue for researchers and practitioners, though it is incremental as it extends beyond known verbosity bias.
The paper investigates format biases in reinforcement learning from human feedback (RLHF), showing that preference models and large language models (LLMs) exhibit biases towards patterns like lists and emojis, which can be exploited to achieve higher rankings on benchmarks such as AlpacaEval, with biases injectable with less than 1% of biased data.
In this paper, we study format biases in reinforcement learning from human feedback (RLHF). We observe that many widely-used preference models, including human evaluators, GPT-4, and top-ranking models on the RewardBench benchmark, exhibit strong biases towards specific format patterns, such as lists, links, bold text, and emojis. Furthermore, large language models (LLMs) can exploit these biases to achieve higher rankings on popular benchmarks like AlpacaEval and LMSYS Chatbot Arena. One notable example of this is verbosity bias, where current preference models favor longer responses that appear more comprehensive, even when their quality is equal to or lower than shorter, competing responses. However, format biases beyond verbosity remain largely underexplored in the literature. In this work, we extend the study of biases in preference learning beyond the commonly recognized length bias, offering a comprehensive analysis of a wider range of format biases. Additionally, we show that with a small amount of biased data (less than 1%), we can inject significant bias into the reward model. Moreover, these format biases can also be easily exploited by downstream alignment algorithms, such as best-of-n sampling and online iterative DPO, as it is usually easier to manipulate the format than to improve the quality of responses. Our findings emphasize the need to disentangle format and content both for designing alignment algorithms and evaluating models.