Understanding the Logic of Direct Preference Alignment through Logic

• URL: http://arxiv.org/abs/2412.17696v2
• Date: Thu, 27 Mar 2025 17:30:56 GMT
• Title: Understanding the Logic of Direct Preference Alignment through Logic
• Authors: Kyle Richardson, Vivek Srikumar, Ashish Sabharwal,
• Abstract summary: We propose a novel formalism for characterizing preference losses for single model and reference model based approaches.<n>We show how this formal view of preference learning sheds new light on both the size and structure of the DPA loss landscape.
• Score: 54.272600416107146
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent direct preference alignment algorithms (DPA), such as DPO, have shown great promise in aligning large language models to human preferences. While this has motivated the development of many new variants of the original DPO loss, understanding the differences between these recent proposals, as well as developing new DPA loss functions, remains difficult given the lack of a technical and conceptual framework for reasoning about the underlying semantics of these algorithms. In this paper, we attempt to remedy this by formalizing DPA losses in terms of discrete reasoning problems. Specifically, we ask: Given an existing DPA loss, can we systematically derive a symbolic program that characterizes its semantics? We propose a novel formalism for characterizing preference losses for single model and reference model based approaches, and identify symbolic forms for a number of commonly used DPA variants. Further, we show how this formal view of preference learning sheds new light on both the size and structure of the DPA loss landscape, making it possible to not only rigorously characterize the relationships between recent loss proposals but also to systematically explore the landscape and derive new loss functions from first principles. We hope our framework and findings will help provide useful guidance to those working on human AI alignment.

Related papers

• A Survey of Direct Preference Optimization [103.59317151002693]
  Large Language Models (LLMs) have demonstrated unprecedented generative capabilities. Their alignment with human values remains critical for ensuring helpful and harmless deployments. Direct Preference Optimization (DPO) has recently gained prominence as a streamlined alternative.
  arXiv  Detail & Related papers  (2025-03-12T08:45:15Z)
• PIPA: Preference Alignment as Prior-Informed Statistical Estimation [57.24096291517857]
  We introduce Pior-Informed Preference Alignment (PIPA), a unified, RL-free probabilistic framework. PIPA accommodates both paired and unpaired data, as well as answer and step-level annotations. By integrating different types of prior information, we developed two variations of PIPA: PIPA-M and PIPA-N.
  arXiv  Detail & Related papers  (2025-02-09T04:31:30Z)
• Many of Your DPOs are Secretly One: Attempting Unification Through Mutual Information [5.655057078073446]
  Post-alignment of large language models (LLMs) is critical in improving their utility, safety, and alignment with human intentions. Direct preference optimisation (DPO) has become one of the most widely used algorithms for achieving this alignment. This paper introduces a unifying framework inspired by mutual information, which proposes a new loss function with flexible priors.
  arXiv  Detail & Related papers  (2025-01-02T21:31:38Z)
• Provably Mitigating Overoptimization in RLHF: Your SFT Loss is Implicitly an Adversarial Regularizer [52.09480867526656]
  We identify the source of misalignment as a form of distributional shift and uncertainty in learning human preferences. To mitigate overoptimization, we first propose a theoretical algorithm that chooses the best policy for an adversarially chosen reward model. Using the equivalence between reward models and the corresponding optimal policy, the algorithm features a simple objective that combines a preference optimization loss and a supervised learning loss.
  arXiv  Detail & Related papers  (2024-05-26T05:38:50Z)
• Coherent Entity Disambiguation via Modeling Topic and Categorical Dependency [87.16283281290053]
  Previous entity disambiguation (ED) methods adopt a discriminative paradigm, where prediction is made based on matching scores between mention context and candidate entities. We propose CoherentED, an ED system equipped with novel designs aimed at enhancing the coherence of entity predictions. We achieve new state-of-the-art results on popular ED benchmarks, with an average improvement of 1.3 F1 points.
  arXiv  Detail & Related papers  (2023-11-06T16:40:13Z)
• STAR Loss: Reducing Semantic Ambiguity in Facial Landmark Detection [80.04000067312428]
  We propose a Self-adapTive Ambiguity Reduction (STAR) loss by exploiting the properties of semantic ambiguity. We find that semantic ambiguity results in the anisotropic predicted distribution, which inspires us to use predicted distribution to represent semantic ambiguity. We also propose two kinds of eigenvalue restriction methods that could avoid both distribution's abnormal change and the model's premature convergence.
  arXiv  Detail & Related papers  (2023-06-05T10:33:25Z)
• A novel approach for Fair Principal Component Analysis based on eigendecomposition [10.203602318836444]
  We propose a novel PCA algorithm which tackles fairness issues by means of a simple strategy comprising a one-dimensional search. Our findings are consistent in several real situations as well as in scenarios with both unbalanced and balanced datasets.
  arXiv  Detail & Related papers  (2022-08-24T08:20:16Z)
• Regularizing Variational Autoencoder with Diversity and Uncertainty Awareness [61.827054365139645]
  Variational Autoencoder (VAE) approximates the posterior of latent variables based on amortized variational inference. We propose an alternative model, DU-VAE, for learning a more Diverse and less Uncertain latent space.
  arXiv  Detail & Related papers  (2021-10-24T07:58:13Z)
• Learning explanations that are hard to vary [75.30552491694066]
  We show that averaging across examples can favor memorization and patchwork' solutions that sew together different strategies. We then propose and experimentally validate a simple alternative algorithm based on a logical AND.
  arXiv  Detail & Related papers  (2020-09-01T10:17:48Z)
• Rule-based Bayesian regression [0.90238471756546]
  We introduce a novel rule-based approach for handling regression problems. The new methodology carries elements from two frameworks: (i) it provides information about the uncertainty of the parameters of interest using Bayesian inference, and (ii) it allows the incorporation of expert knowledge through rule-based systems.
  arXiv  Detail & Related papers  (2020-08-02T07:20:45Z)
• Towards a Theoretical Understanding of the Robustness of Variational Autoencoders [82.68133908421792]
  We make inroads into understanding the robustness of Variational Autoencoders (VAEs) to adversarial attacks and other input perturbations. We develop a novel criterion for robustness in probabilistic models: $r$-robustness. We show that VAEs trained using disentangling methods score well under our robustness metrics.
  arXiv  Detail & Related papers  (2020-07-14T21:22:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.