Mirror, Mirror on the Wall -- Which is the Best Model of Them All?

• URL: http://arxiv.org/abs/2512.02043v1
• Date: Tue, 25 Nov 2025 20:52:45 GMT
• Title: Mirror, Mirror on the Wall -- Which is the Best Model of Them All?
• Authors: Dina Sayed, Heiko Schuldt,
• Abstract summary: Large Language Models (LLMs) have become one of the most transformative tools across many applications.<n>We argue that there are two main dimensions that need to be taken into consideration when selecting a model for further training.
• Score: 2.2022484178680877
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have become one of the most transformative tools across many applications, as they have significantly boosted productivity and achieved impressive results in various domains such as finance, healthcare, education, telecommunications, and law, among others. Typically, state-of-the-art (SOTA) foundation models are developed by large corporations based on large data collections and substantial computational and financial resources required to pretrain such models from scratch. These foundation models then serve as the basis for further development and domain adaptation for specific use cases or tasks. However, given the dynamic and fast-paced nature of launching new foundation models, the process of selecting the most suitable model for a particular use case, application, or domain becomes increasingly complex. We argue that there are two main dimensions that need to be taken into consideration when selecting a model for further training: a qualitative dimension (which model is best suited for a task based on information, for instance, taken from model cards) and a quantitative dimension (which is the best performing model). The quantitative performance of models is assessed through leaderboards, which rank models based on standardized benchmarks and provide a consistent framework for comparing different LLMs. In this work, we address the analysis of the quantitative dimension by exploring the current leaderboards and benchmarks. To illustrate this analysis, we focus on the medical domain as a case study, demonstrating the evolution, current landscape, and practical significance of this quantitative evaluation dimension. Finally, we propose a Model Selection Methodology (MSM), a systematic approach designed to guide the navigation, prioritization, and selection of the model that best aligns with a given use case.

Related papers

• Generalized Top-k Mallows Model for Ranked Choices [7.389630498367403]
  We present a novel sampling scheme tailored to top-k Mallows models and an efficient algorithm for computing choice probabilities.<n>We also present an active learning algorithm for estimating the model parameters from observed choice data.<n>These contributions provide new tools for analysis and prediction in critical decision-making scenarios.
  arXiv  Detail & Related papers  (2025-10-24T21:49:21Z)
• Understanding the Implicit Biases of Design Choices for Time Series Foundation Models [90.894232610821]
  Time series foundation models (TSFMs) are a class of potentially powerful, general-purpose tools for time series forecasting and related temporal tasks.<n>Their behavior is strongly shaped by subtle inductive biases in their design.<n>We show how these biases can be intuitive or very counterintuitive, depending on properties of the model and data.
  arXiv  Detail & Related papers  (2025-10-22T04:42:35Z)
• OptMerge: Unifying Multimodal LLM Capabilities and Modalities via Model Merging [124.91183814854126]
  Model merging seeks to combine multiple expert models into a single model.<n>We introduce a benchmark for model merging research that clearly divides the tasks for MLLM training and evaluation.<n>We find that model merging offers a promising way for building improved MLLMs without requiring training data.
  arXiv  Detail & Related papers  (2025-05-26T12:23:14Z)
• MOSLIM:Align with diverse preferences in prompts through reward classification [6.6431471703308915]
  We introduce a novel multi-objective alignment method, MOSLIM, which utilizes a single reward model and policy model to address diverse objectives.<n> MOSLIM provides a flexible way to control these objectives through prompting and does not require preference training during SFT phase.<n>We demonstrate the efficacy of our proposed method across several multi-objective benchmarks and conduct ablation studies on various reward model sizes and policy optimization methods.
  arXiv  Detail & Related papers  (2025-05-24T12:22:21Z)
• Not-Just-Scaling Laws: Towards a Better Understanding of the Downstream Impact of Language Model Design Decisions [65.89403417819764]
  We quantify the impact of design choices on language model capabilities.<n>By incorporating features besides model size and number of training tokens, we can achieve a relative 3-28% increase in ability to predict downstream performance.
  arXiv  Detail & Related papers  (2025-03-05T19:46:04Z)
• A Collaborative Ensemble Framework for CTR Prediction [73.59868761656317]
  We propose a novel framework, Collaborative Ensemble Training Network (CETNet), to leverage multiple distinct models. Unlike naive model scaling, our approach emphasizes diversity and collaboration through collaborative learning. We validate our framework on three public datasets and a large-scale industrial dataset from Meta.
  arXiv  Detail & Related papers  (2024-11-20T20:38:56Z)
• OLMES: A Standard for Language Model Evaluations [64.85905119836818]
  OLMES is a documented, practical, open standard for reproducible language model evaluations.<n>It supports meaningful comparisons between smaller base models that require the unnatural "cloze" formulation of multiple-choice questions.<n> OLMES includes well-considered, documented recommendations guided by results from existing literature as well as new experiments resolving open questions.
  arXiv  Detail & Related papers  (2024-06-12T17:37:09Z)
• What matters when building vision-language models? [52.8539131958858]
  We develop Idefics2, an efficient foundational vision-language model with 8 billion parameters. Idefics2 achieves state-of-the-art performance within its size category across various multimodal benchmarks. We release the model (base, instructed, and chat) along with the datasets created for its training.
  arXiv  Detail & Related papers  (2024-05-03T17:00:00Z)
• The Role of Model Architecture and Scale in Predicting Molecular Properties: Insights from Fine-Tuning RoBERTa, BART, and LLaMA [0.0]
  This study introduces a systematic framework to compare the efficacy of Large Language Models (LLMs) for fine-tuning across various cheminformatics tasks. We assessed three well-known models-RoBERTa, BART, and LLaMA-on their ability to predict molecular properties. We found that LLaMA-based models generally offered the lowest validation loss, suggesting their superior adaptability across tasks and scales.
  arXiv  Detail & Related papers  (2024-05-02T02:20:12Z)
• Has Your Pretrained Model Improved? A Multi-head Posterior Based Approach [25.927323251675386]
  We leverage the meta-features associated with each entity as a source of worldly knowledge and employ entity representations from the models. We propose using the consistency between these representations and the meta-features as a metric for evaluating pre-trained models. Our method's effectiveness is demonstrated across various domains, including models with relational datasets, large language models and image models.
  arXiv  Detail & Related papers  (2024-01-02T17:08:26Z)
• Modeling Choice via Self-Attention [8.394221523847325]
  We show that our attention-based choice model is a low-optimal generalization of the Halo Multinomial Logit (Halo-MNL) model. We also establish the first realistic-scale benchmark for choice estimation on real data, conducting an evaluation of existing models.
  arXiv  Detail & Related papers  (2023-11-11T11:13:07Z)
• Large-scale Multi-Modal Pre-trained Models: A Comprehensive Survey [66.18478838828231]
  Multi-modal pre-trained big models have drawn more and more attention in recent years. This paper introduces the background of multi-modal pre-training by reviewing the conventional deep, pre-training works in natural language process, computer vision, and speech. Then, we introduce the task definition, key challenges, and advantages of multi-modal pre-training models (MM-PTMs), and discuss the MM-PTMs with a focus on data, objectives, network, and knowledge enhanced pre-training.
  arXiv  Detail & Related papers  (2023-02-20T15:34:03Z)

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.