Contrastive Learning from AI Reviews (CLAIR): A new approach to address underspecification in AI model alignment with Anchored Preference Optimization (APO)

The development of artificial intelligence (ai), particularly large language models (LLMs), is focused on aligning these models with human preferences to improve their effectiveness and safety. This alignment is critical to fine-tuning ai interactions with users, ensuring that the responses generated are accurate and aligned with human expectations and values. Achieving this requires a combination of preference data, which informs the model about desirable outcomes, and alignment goals that guide the training process. These elements are crucial to improving model performance and its ability to meet user expectations.

A major challenge in ai model alignment lies in the problem of underspecification, where the relationship between preference data and training goals is not clearly defined. This lack of clarity can lead to suboptimal performance, as the model may need help to learn effectively from the data provided. Underspecification occurs when the preference pairs used to train the model contain differences irrelevant to the desired outcome. These spurious differences complicate the learning process, making it difficult for the model to focus on the aspects that really matter. Current alignment methods often need to more adequately account for the relationship between model performance and preference data, which can lead to a degradation of the model’s capabilities.

Existing methods for aligning LLMs, such as those based on contrastive learning objectives and preference pair datasets, have made significant progress but need to be revised. These methods typically involve generating two outputs from the model and using a judge, another ai model, or a human to select the preferred output. However, this approach can result in inconsistent preference signals, as the criteria for choosing the preferred response may only sometimes be clear or consistent. This inconsistency in the learning signal can hamper the model’s ability to effectively improve during training, as the model may only sometimes receive clear guidance on how to adjust its outputs to better align with human preferences.

Researchers from Ghent University – imec, Stanford University and Contextual ai have presented two innovative methods to address these challenges: Contrastive Learning from ai Reviews (CLAIR) and Anchored Preference Optimization (APO)CLAIR is a new data creation method designed to generate minimally contrasting preference pairs by slightly revising a model’s output to create a preferred response. This method ensures that the contrast between the winning and losing outputs is minimal but significant, providing a more accurate learning signal for the model. On the other hand, APO is a family of alignment objectives that offer greater control over the training process. By explicitly taking into account the relationship between the model and the preference data, APO ensures that the alignment process is more stable and efficient.

The CLAIR method works by first generating a losing outcome from the target model and then using a stronger model, such as GPT-4-turbo, to revise this outcome into a winning one. This revision process is designed to make only minimal changes, ensuring that the contrast between the two outcomes is focused on the most relevant aspects. This approach differs significantly from traditional methods, which may rely on a judge selecting the preferred outcome from two independently generated responses. By creating preference pairs with minimal but significant contrasts, CLAIR provides a clearer and more effective learning signal for the model during training.

Anchored preference optimization (APO) complements CLAIR by offering fine-grained control over the alignment process. APO adjusts the probability of winning or losing outcomes based on the model’s performance relative to the preference data. For example, the APO-zero variant increases the probability of winning outcomes and decreases the probability of losing outcomes, which is particularly useful when model outcomes are generally less desirable than winning outcomes. In contrast, the APO-down variant decreases the probability of winning or losing outcomes, which can be beneficial when model outcomes are already better than the preferred responses. This level of control allows researchers to tailor the alignment process more precisely to the specific needs of the model and data.

The effectiveness of CLAIR and APO was demonstrated by aligning the Llama-3-8B-Instruct model using a variety of datasets and alignment targets. The results were significant: CLAIR, combined with the APO-zero target, yielded a 7.65% performance improvement on the MixEval-Hard benchmark, which measures model accuracy on a variety of complex queries. This improvement represents a substantial step toward closing the performance gap between Llama-3-8B-Instruct and GPT-4-turbo, reducing the difference by 45%. These results highlight the importance of minimally contrasting preference pairs and custom alignment targets for improving ai model performance.

In conclusion, CLAIR and APO offer a more effective approach to aligning LLMs with human preferences, addressing the challenges of underspecification and providing finer control over the training process. Their success in improving the performance of the Llama-3-8B-Instruct model underscores their potential to improve the ai ​​model alignment process more broadly.

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