Large Language Models (LLMs) have revolutionized interactions between human and artificial intelligence (AI) systems, demonstrating state-of-the-art performance across various domains, including scientific discovery and hypothesis generation. However, the absence of a comprehensive and systematic evaluation framework for LLM-driven research idea generation hinders a rigorous understanding of their strengths and limitations. To address this gap, we propose IdeaBench, a benchmark system that provides a structured dataset and evaluation framework for standardizing the assessment of research idea generation by LLMs. Our dataset comprises titles and abstracts from 2,374 influential papers across eight research domains, along with their 29,408 referenced works, creating a context-rich environment that mirrors human researchers' ideation processes. By profiling LLMs as domain-specific researchers and grounding them in similar contextual constraints, we directly leverage the models' knowledge learned from the pre-training stage to generate new research ideas. To systematically evaluate LLMs' research ideation capability and approximate human assessment, we propose a reference-based metric that aligns with human judgment to quantify idea quality with the assistance of LLMs. Through this evaluation, we find that while LLMs excel at generating novel ideas, they may struggle with generating feasible ideas. IdeaBench serves as a critical resource for benchmarking and comparing LLMs, ultimately advancing research on AI's role in automating scientific discovery.

In the emerging landscape of AI-driven scientific discovery, foundation models hold significant promise for enhancing research ideation and overall scientific advancement. This paper explores a future where foundation models should be able to effectively utilize both external and internal knowledge sources to maximize their role in scientific discovery. The core challenge lies in optimizing two knowledge types: external knowledge, drawn from diverse data sources, and internal knowledge, the parametric understanding acquired during training. We propose a dual-framework solution for this optimization, including X-augmented generation and in-context X learning. X-augmented generation approaches, such as retrieval-augmented generation, knowledge graph-augmented generation, and third-party tool integration, enhance external knowledge processing. In-context X learning methods, including in-context adversarial learning and in-context reinforcement learning, improve models’ internal knowledge adaptation and utility for scientific tasks. We aim to inspire the research community by proposing a bold pathway toward leveraging foundation models as active participants in scientific discovery, tackling the inherent complexity of optimizing vast, multimodal knowledge sources. By addressing this challenge, we envision a future where foundation models catalyze breakthroughs across disciplines, ultimately leading to a more dynamic, collaborative, and insight-driven scientific process.

Generalized Additive Models (GAMs) have long been a powerful white-box tool for the intelligible analysis of tabular data, revealing the influence of each feature on the model predictions. Despite the success of Neural Networks (NNs) in various domains, their application as NN-based GAMs in tabular data analysis remains suboptimal compared to tree-based ones, and the opacity of encoders in NN-GAMs also prevents users from understanding how networks learn the functions. In this work, we propose a new deep tabular learning method, termed Prototypical Neural Additive Model (ProtoNAM), which introduces prototypes into NNs in the framework of GAMs. With the introduced prototype-based feature activation, ProtoNAM can flexibly model the irregular mapping from tabular features to the outputs while maintaining the explainability of the final prediction. We also propose a gradient-boosting inspired hierarchical shape function modeling method, facilitating the discovery of complex feature patterns and bringing transparency into the learning process of each network layer. Our empirical evaluations demonstrate that ProtoNAM outperforms all existing NN-based GAMs, while providing additional insights into the shape function learned for each feature. The source code of ProtoNAM is available at https://github.com/Teddy-XiongGZ/ProtoNAM .

Machine learning foundation models, particularly large language models (LLMs) such as GPT-4o, have revolutionized traditional applications in computer vision and natural language processing, marking a significant shift in recent years. Building on these advancements, recent efforts have explored the potential of foundation models in hypothesis generation, highlighting their possibility in aiding human researchers in scientific discovery. In this paper, we envision a future where academia increasingly integrates foundation models to accelerate and enhance the process of scientific discovery. Motivated by potential application scenarios of foundation models in scientific research, our vision is anchored in a central question: How can we accelerate scientific discovery with the aid of foundation models? To address this overarching question, we raise two key challenges that need to be addressed: (1) how to effectively harness the parametric knowledge embedded in foundation models to propel scientific discovery? and (2) how to develop rigorous yet scalable methods to evaluate the effectiveness of foundation models in supporting scientific research? To tackle these two challenges, we propose our approaches, termed knowledge-grounded Chain-of-Idea (KG-CoI) hypothesis generation and IdeaBench - Benchmarking LLM hypothesis generators in a customizable manner. Through addressing these challenges, we outline our vision in hope to inspire new ideas and innovations in harnessing foundation models for advancing scientific discovery, paving the way for a new era of research collaboration between humans and artificial intelligence.

Interpretability of Deep Neural Networks using concept-based models offers a promising way to explain model behavior through human understandable concepts. A parallel line of research focuses on disentangling the data distribution into its underlying generative factors, in turn explaining the data generation process. While both directions have received extensive attention, little work has been done on explaining concepts in terms of generative factors to unify mathematically disentangled representations and human-understandable concepts as an explanation for downstream tasks. In this paper, we propose a novel method CoLiDR - which utilizes a disentangled representation learning setup for learning mutually independent generative factors and subsequently learns to aggregate the said representations into human-understandable concepts using a novel aggregation/decomposition module. Experiments are conducted on datasets with both known and unknown latent generative factors. Our method successfully aggregates disentangled generative factors into concepts while maintaining parity with state-of-the-art concept-based approaches. Quantitative and visual analysis of the learned aggregation procedure demonstrates the advantages of our work compared to commonly used concept-based models over four challenging datasets. Lastly, our work is generalizable to an arbitrary number of concepts and generative factors - making it flexible enough to be suitable for various types of data.