Evaluating the Effectiveness of Reward Modeling of Generative AI Programs
New analysis evaluating the effectiveness of reward modeling throughout Reinforcement Studying from Human Suggestions (RLHF): “SEAL: Systematic Error Evaluation for Worth ALignment.” The paper introduces quantitative metrics for evaluating the effectiveness of modeling and aligning human values:
Summary: Reinforcement Studying from Human Suggestions (RLHF) goals to align language fashions (LMs) with human values by coaching reward fashions (RMs) on binary preferences and utilizing these RMs to fine-tune the bottom LMs. Regardless of its significance, the interior mechanisms of RLHF stay poorly understood. This paper introduces new metrics to guage the effectiveness of modeling and aligning human values, specifically function imprint, alignment resistance and alignment robustness. We categorize alignment datasets into goal options (desired values) and spoiler options (undesired ideas). By regressing RM scores towards these options, we quantify the extent to which RMs reward them a metric we time period function imprint. We outline alignment resistance because the proportion of the choice dataset the place RMs fail to match human preferences, and we assess alignment robustness by analyzing RM responses to perturbed inputs. Our experiments, using open-source elements just like the Anthropic choice dataset and OpenAssistant RMs, reveal important imprints of goal options and a notable sensitivity to spoiler options. We noticed a 26% incidence of alignment resistance in parts of the dataset the place LM-labelers disagreed with human preferences. Moreover, we discover that misalignment typically arises from ambiguous entries inside the alignment dataset. These findings underscore the significance of scrutinizing each RMs and alignment datasets for a deeper understanding of worth alignment.
Posted on September 11, 2024 at 7:03 AM •
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