Unlocking Decoding-time Controllability: Gradient-Free Multi-Objective Alignment with Contrastive Prompts

• URL: http://arxiv.org/abs/2408.05094v1
• Date: Fri, 9 Aug 2024 14:36:42 GMT
• Title: Unlocking Decoding-time Controllability: Gradient-Free Multi-Objective Alignment with Contrastive Prompts
• Authors: Tingchen Fu, Yupeng Hou, Julian McAuley, Rui Yan,
• Abstract summary: Multi-objective alignment aims at balancing and controlling the different alignment objectives of large language models. We propose MCA (Multi-objective Contrastive Alignemnt), which constructs an expert prompt and an adversarial prompt for each objective to contrast.
• Score: 38.95012734839997
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The task of multi-objective alignment aims at balancing and controlling the different alignment objectives (e.g., helpfulness, harmlessness and honesty) of large language models to meet the personalized requirements of different users. However, previous methods tend to train multiple models to deal with various user preferences, with the number of trained models growing linearly with the number of alignment objectives and the number of different preferences. Meanwhile, existing methods are generally poor in extensibility and require significant re-training for each new alignment objective considered. Considering the limitation of previous approaches, we propose MCA (Multi-objective Contrastive Alignemnt), which constructs an expert prompt and an adversarial prompt for each objective to contrast at the decoding time and balances the objectives through combining the contrast. Our approach is verified to be superior to previous methods in obtaining a well-distributed Pareto front among different alignment objectives.

Related papers

• C-MORL: Multi-Objective Reinforcement Learning through Efficient Discovery of Pareto Front [9.04360155372014]
  Constrained MORL is a seamless bridge between constrained policy optimization and MORL. Our algorithm achieves more consistent and superior performances in terms of hypervolume, expected utility, and sparsity on both discrete and continuous control tasks.
  arXiv  Detail & Related papers  (2024-10-03T06:13:56Z)
• UCB-driven Utility Function Search for Multi-objective Reinforcement Learning [75.11267478778295]
  In Multi-objective Reinforcement Learning (MORL) agents are tasked with optimising decision-making behaviours. We focus on the case of linear utility functions parameterised by weight vectors w. We introduce a method based on Upper Confidence Bound to efficiently search for the most promising weight vectors during different stages of the learning process.
  arXiv  Detail & Related papers  (2024-05-01T09:34:42Z)
• Controllable Preference Optimization: Toward Controllable Multi-Objective Alignment [103.12563033438715]
  Alignment in artificial intelligence pursues consistency between model responses and human preferences as well as values. Existing alignment techniques are mostly unidirectional, leading to suboptimal trade-offs and poor flexibility over various objectives. We introduce controllable preference optimization (CPO), which explicitly specifies preference scores for different objectives.
  arXiv  Detail & Related papers  (2024-02-29T12:12:30Z)
• Multi-Target Multiplicity: Flexibility and Fairness in Target Specification under Resource Constraints [76.84999501420938]
  We introduce a conceptual and computational framework for assessing how the choice of target affects individuals' outcomes. We show that the level of multiplicity that stems from target variable choice can be greater than that stemming from nearly-optimal models of a single target.
  arXiv  Detail & Related papers  (2023-06-23T18:57:14Z)
• Learning Transferable Adversarial Robust Representations via Multi-view Consistency [57.73073964318167]
  We propose a novel meta-adversarial multi-view representation learning framework with dual encoders. We demonstrate the effectiveness of our framework on few-shot learning tasks from unseen domains.
  arXiv  Detail & Related papers  (2022-10-19T11:48:01Z)
• PD-MORL: Preference-Driven Multi-Objective Reinforcement Learning Algorithm [0.18416014644193063]
  We propose a novel MORL algorithm that trains a single universal network to cover the entire preference space scalable to continuous robotic tasks. PD-MORL achieves up to 25% larger hypervolume for challenging continuous control tasks and uses an order of magnitude fewer trainable parameters compared to prior approaches.
  arXiv  Detail & Related papers  (2022-08-16T19:23:02Z)
• Generative multitask learning mitigates target-causing confounding [61.21582323566118]
  We propose a simple and scalable approach to causal representation learning for multitask learning. The improvement comes from mitigating unobserved confounders that cause the targets, but not the input. Our results on the Attributes of People and Taskonomy datasets reflect the conceptual improvement in robustness to prior probability shift.
  arXiv  Detail & Related papers  (2022-02-08T20:42:14Z)
• Momentum-based Gradient Methods in Multi-Objective Recommendation [30.894950420437926]
  We create a multi-objective model-agnostic Adamize method for solving single-objective problems. We evaluate the benefits of Multi-objective Adamize on two multi-objective recommender systems and for three different objective combinations, both correlated or conflicting.
  arXiv  Detail & Related papers  (2020-09-10T07:12:21Z)
• A Distributional View on Multi-Objective Policy Optimization [24.690800846837273]
  We propose an algorithm for multi-objective reinforcement learning that enables setting desired preferences for objectives in a scale-invariant way. We show that setting different preferences in our framework allows us to trace out the space of nondominated solutions.
  arXiv  Detail & Related papers  (2020-05-15T13:02:17Z)

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.