Diverse Explanations from Data-driven and Domain-driven Perspectives for Machine Learning Models

• URL: http://arxiv.org/abs/2402.00347v1
• Date: Thu, 1 Feb 2024 05:28:28 GMT
• Title: Diverse Explanations from Data-driven and Domain-driven Perspectives for Machine Learning Models
• Authors: Sichao Li and Amanda Barnard
• Abstract summary: Explanations of machine learning models are important, especially in scientific areas such as chemistry, biology, and physics. This paper calls attention to inconsistencies due to accurate yet misleading machine learning models and various stakeholders with specific needs, wants, or aims.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Explanations of machine learning models are important, especially in scientific areas such as chemistry, biology, and physics, where they guide future laboratory experiments and resource requirements. These explanations can be derived from well-trained machine learning models (data-driven perspective) or specific domain knowledge (domain-driven perspective). However, there exist inconsistencies between these perspectives due to accurate yet misleading machine learning models and various stakeholders with specific needs, wants, or aims. This paper calls attention to these inconsistencies and suggests a way to find an accurate model with expected explanations that reinforce physical laws and meet stakeholders' requirements from a set of equally-good models, also known as Rashomon sets. Our goal is to foster a comprehensive understanding of these inconsistencies and ultimately contribute to the integration of eXplainable Artificial Intelligence (XAI) into scientific domains.

Related papers

• Scientists' First Exam: Probing Cognitive Abilities of MLLM via Perception, Understanding, and Reasoning [59.518397361341556]
  We present the Scientists' First Exam (SFE) benchmark, designed to evaluate the scientific cognitive capacities of Multimodal Large Language Models (MLLMs)<n>SFE comprises 830 expert-verified VQA pairs across three question types, spanning 66 multimodal tasks across five high-value disciplines.<n>Experiments reveal that current state-of-the-art GPT-o3 and InternVL-3 achieve only 34.08% and 26.52% on SFE, highlighting significant room for MLLMs to improve in scientific realms.
  arXiv  Detail & Related papers  (2025-06-12T09:29:16Z)
• Interpretable Machine Learning in Physics: A Review [10.77934040629518]
  We aim to establish interpretable machine learning as a core research focus in science. We categorize different aspects of interpretability, discuss machine learning models in terms of both interpretability and performance. We highlight recent advances in interpretable machine learning across many subfields of physics.
  arXiv  Detail & Related papers  (2025-03-30T22:44:40Z)
• Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning [51.11965014462375]
  Multimodal Large Language Models (MLLMs) integrate text, images, and other modalities. This paper argues that MLLMs can significantly advance scientific reasoning across disciplines such as mathematics, physics, chemistry, and biology.
  arXiv  Detail & Related papers  (2025-02-05T04:05:27Z)
• Probing the limitations of multimodal language models for chemistry and materials research [3.422786943576035]
  We introduce MaCBench, a benchmark for evaluating how vision-language models handle real-world chemistry and materials science tasks. We find that while these systems show promising capabilities in basic perception tasks, they exhibit fundamental limitations in spatial reasoning, cross-modal information synthesis, and logical inference. Our insights have important implications beyond chemistry and materials science, suggesting that developing reliable multimodal AI scientific assistants may require advances in curating suitable training data and approaches to training those models.
  arXiv  Detail & Related papers  (2024-11-25T21:51:45Z)
• Improving Molecular Modeling with Geometric GNNs: an Empirical Study [56.52346265722167]
  This paper focuses on the impact of different canonicalization methods, (2) graph creation strategies, and (3) auxiliary tasks, on performance, scalability and symmetry enforcement. Our findings and insights aim to guide researchers in selecting optimal modeling components for molecular modeling tasks.
  arXiv  Detail & Related papers  (2024-07-11T09:04:12Z)
• A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery [68.48094108571432]
  Large language models (LLMs) have revolutionized the way text and other modalities of data are handled. We aim to provide a more holistic view of the research landscape by unveiling cross-field and cross-modal connections between scientific LLMs.
  arXiv  Detail & Related papers  (2024-06-16T08:03:24Z)
• LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery [141.39722070734737]
  We propose to enhance the knowledge-driven, abstract reasoning abilities of Large Language Models with the computational strength of simulations. We introduce Scientific Generative Agent (SGA), a bilevel optimization framework. We conduct experiments to demonstrate our framework's efficacy in law discovery and molecular design.
  arXiv  Detail & Related papers  (2024-05-16T03:04:10Z)
• Opportunities for machine learning in scientific discovery [16.526872562935463]
  We review how the scientific community can increasingly leverage machine-learning techniques to achieve scientific discoveries. Although challenges remain, principled use of ML is opening up new avenues for fundamental scientific discoveries.
  arXiv  Detail & Related papers  (2024-05-07T09:58:02Z)
• Understanding Biology in the Age of Artificial Intelligence [4.299566787216408]
  Modern life sciences research is increasingly relying on artificial intelligence approaches to model biological systems. Although machine learning (ML) models are useful for identifying patterns in large, complex data sets, its widespread application in biological sciences represents a significant deviation from traditional methods of scientific inquiry. Here, we identify general principles that can guide the design and application of ML systems to model biological phenomena and advance scientific knowledge.
  arXiv  Detail & Related papers  (2024-03-06T23:20:34Z)
• Scientific Large Language Models: A Survey on Biological & Chemical Domains [47.97810890521825]
  Large Language Models (LLMs) have emerged as a transformative power in enhancing natural language comprehension. The application of LLMs extends beyond conventional linguistic boundaries, encompassing specialized linguistic systems developed within various scientific disciplines. As a burgeoning area in the community of AI for Science, scientific LLMs warrant comprehensive exploration.
  arXiv  Detail & Related papers  (2024-01-26T05:33:34Z)
• SciInstruct: a Self-Reflective Instruction Annotated Dataset for Training Scientific Language Models [57.96527452844273]
  We introduce SciInstruct, a suite of scientific instructions for training scientific language models capable of college-level scientific reasoning. We curated a diverse and high-quality dataset encompassing physics, chemistry, math, and formal proofs. To verify the effectiveness of SciInstruct, we fine-tuned different language models with SciInstruct, i.e., ChatGLM3 (6B and 32B), Llama3-8B-Instruct, and Mistral-7B: MetaMath.
  arXiv  Detail & Related papers  (2024-01-15T20:22:21Z)
• Interpretable and Explainable Machine Learning for Materials Science and Chemistry [2.2175470459999636]
  We summarize applications of interpretability and explainability techniques for materials science and chemistry. We discuss various challenges for interpretable machine learning in materials science and, more broadly, in scientific settings. We showcase a number of exciting developments in other fields that could benefit interpretability in material science and chemistry problems.
  arXiv  Detail & Related papers  (2021-11-01T15:40:36Z)
• Machine Learning in Nano-Scale Biomedical Engineering [77.75587007080894]
  We review the existing research regarding the use of machine learning in nano-scale biomedical engineering. The main challenges that can be formulated as ML problems are classified into the three main categories. For each of the presented methodologies, special emphasis is given to its principles, applications, and limitations.
  arXiv  Detail & Related papers  (2020-08-05T15:45:54Z)

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.