面向语言生成鲁棒自适应的软对齐目标
Soft Alignment Objectives for Robust Adaptation of Language Generation
- Masaryk University（马萨里克大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出基于词元语义相似度的软对齐训练目标，在保持领域自适应质量的同时缓解灾难性遗忘，并以可忽略的计算开销探索了词元级与序列级目标之间的折中方案。
AI中文摘要：
领域自适应使生成式语言模型能够解决因其应用领域偏移而产生的特定缺陷。然而，通过在领域内数据上进一步训练的传统自适应方法会迅速削弱模型泛化到其他领域的能力，使得自适应后模型的开放式部署容易出错。本文提出了基于预测词元与参考词元之间语义相似度的新型训练目标。我们的结果表明：（1）通过基于词元语义相似度构建训练目标，避免单一正确预测这一常见假设，可以缓解领域自适应过程中的灾难性遗忘，同时（2）保持自适应质量，（3）计算成本增加可忽略不计。在更广泛的背景下，基于连续词元相似度的目标开创了对高效但朴素的精确匹配词元级目标与表达力强但计算和资源密集的序列级目标之间中间地带的探索。
英文摘要：
Domain adaptation allows generative language models to address specific flaws caused by the domain shift of their application. However, the traditional adaptation by further training on in-domain data rapidly weakens the model's ability to generalize to other domains, making the open-ended deployments of the adapted models prone to errors. This work introduces novel training objectives built upon a semantic similarity of the predicted tokens to the reference. Our results show that (1) avoiding the common assumption of a single correct prediction by constructing the training target from tokens' semantic similarity can mitigate catastrophic forgetting during domain adaptation, while (2) preserving the quality of the adaptation, (3) with negligible additions to compute costs. In the broader context, the objectives grounded in a continuous token similarity pioneer the exploration of the middle ground between the efficient but na\"ıve exact-match token-level objectives and expressive but computationally- and resource-intensive sequential objectives.
