A Comprehensive Evaluation framework of Alignment Techniques for LLMs

• URL: http://arxiv.org/abs/2508.09937v1
• Date: Wed, 13 Aug 2025 16:42:01 GMT
• Title: A Comprehensive Evaluation framework of Alignment Techniques for LLMs
• Authors: Muneeza Azmat, Momin Abbas, Maysa Malfiza Garcia de Macedo, Marcelo Carpinette Grave, Luan Soares de Souza, Tiago Machado, Rogerio A de Paula, Raya Horesh, Yixin Chen, Heloisa Caroline de Souza Pereira Candello, Rebecka Nordenlow, Aminat Adebiyi,
• Abstract summary: This paper introduces a multi-dimensional evaluation of alignment techniques for Large Language Models (LLMs)<n>Our framework assesses methods along four key dimensions: alignment detection, alignment quality, computational efficiency, and robustness.
• Score: 5.9090038202345
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As Large Language Models (LLMs) become increasingly integrated into real-world applications, ensuring their outputs align with human values and safety standards has become critical. The field has developed diverse alignment approaches including traditional fine-tuning methods (RLHF, instruction tuning), post-hoc correction systems, and inference-time interventions, each with distinct advantages and limitations. However, the lack of unified evaluation frameworks makes it difficult to systematically compare these paradigms and guide deployment decisions. This paper introduces a multi-dimensional evaluation of alignment techniques for LLMs, a comprehensive evaluation framework that provides a systematic comparison across all major alignment paradigms. Our framework assesses methods along four key dimensions: alignment detection, alignment quality, computational efficiency, and robustness. Through experiments across diverse base models and alignment strategies, we demonstrate the utility of our framework in identifying strengths and limitations of current state-of-the-art models, providing valuable insights for future research directions.

Related papers

• Towards Alignment-Centric Paradigm: A Survey of Instruction Tuning in Large Language Models [20.544181414963877]
  This survey provides a comprehensive overview of the full pipeline of instruction tuning strategies.<n>We categorized data construction into three major paradigms: expert annotation, distillation from larger models, and self-improvement mechanisms.<n>We discuss promising directions for automated data generation, adaptive optimization, and robust evaluation frameworks.
  arXiv  Detail & Related papers  (2025-08-24T01:51:55Z)
• Part I: Tricks or Traps? A Deep Dive into RL for LLM Reasoning [53.85659415230589]
  This paper systematically reviews widely adoptedReinforcement learning techniques.<n>We present clear guidelines for selecting RL techniques tailored to specific setups.<n>We also reveal that a minimalist combination of two techniques can unlock the learning capability of critic-free policies.
  arXiv  Detail & Related papers  (2025-08-11T17:39:45Z)
• Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges [47.14342587731284]
  This survey provides a comprehensive overview of alignment techniques, training protocols, and empirical findings in large language models (LLMs) alignment.<n>We analyze the development of alignment methods across diverse paradigms, characterizing the fundamental trade-offs between core alignment objectives.<n>We discuss state-of-the-art techniques, including Direct Preference Optimization (DPO), Constitutional AI, brain-inspired methods, and alignment uncertainty quantification (AUQ)
  arXiv  Detail & Related papers  (2025-07-25T20:52:58Z)
• A New Approach for Multicriteria Assessment in the Ranking of Alternatives Using Cardinal and Ordinal Data [0.0]
  We propose a novel MCA approach that combines two Virtual Gap Analysis (VGA) models.<n>The VGA framework, rooted in linear programming, is pivotal in the MCA methodology.
  arXiv  Detail & Related papers  (2025-07-10T04:00:48Z)
• EVA-MILP: Towards Standardized Evaluation of MILP Instance Generation [13.49043811341421]
  Mixed-Integer Linear Programming (MILP) is fundamental to solving complex decision-making problems.<n>The proliferation of MILP instance generation methods, driven by machine learning's demand for diverse datasets, has significantly outpaced standardized evaluation techniques.<n>This paper introduces a comprehensive benchmark framework designed for the systematic and objective evaluation of MILP instance generation methods.
  arXiv  Detail & Related papers  (2025-05-30T16:42:15Z)
• Learning Reward and Policy Jointly from Demonstration and Preference Improves Alignment [58.049113055986375]
  We develop a single stage approach named Alignment with Integrated Human Feedback (AIHF) to train reward models and the policy.<n>The proposed approach admits a suite of efficient algorithms, which can easily reduce to, and leverage, popular alignment algorithms.<n>We demonstrate the efficiency of the proposed solutions with extensive experiments involving alignment problems in LLMs and robotic control problems in MuJoCo.
  arXiv  Detail & Related papers  (2024-06-11T01:20:53Z)
• Optimal Baseline Corrections for Off-Policy Contextual Bandits [61.740094604552475]
  We aim to learn decision policies that optimize an unbiased offline estimate of an online reward metric. We propose a single framework built on their equivalence in learning scenarios. Our framework enables us to characterize the variance-optimal unbiased estimator and provide a closed-form solution for it.
  arXiv  Detail & Related papers  (2024-05-09T12:52:22Z)
• Better Understanding Differences in Attribution Methods via Systematic Evaluations [57.35035463793008]
  Post-hoc attribution methods have been proposed to identify image regions most influential to the models' decisions. We propose three novel evaluation schemes to more reliably measure the faithfulness of those methods. We use these evaluation schemes to study strengths and shortcomings of some widely used attribution methods over a wide range of models.
  arXiv  Detail & Related papers  (2023-03-21T14:24:58Z)
• Towards Better Understanding Attribution Methods [77.1487219861185]
  Post-hoc attribution methods have been proposed to identify image regions most influential to the models' decisions. We propose three novel evaluation schemes to more reliably measure the faithfulness of those methods. We also propose a post-processing smoothing step that significantly improves the performance of some attribution methods.
  arXiv  Detail & Related papers  (2022-05-20T20:50:17Z)

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.