5 citations · 12 across the 9 of their papers we have counts for
10 papers
Document-as-Image Representations Fall Short for Scientific Retrieval
Ghazal Khalighinejad, Raghuveer Thirukovalluru, Alexander H. Oh +1
Many recent document embedding models are trained on document-as-image representations, embedding rendered pages as images rather than the underlying source. Meanwhile, existing be…
It's LIT! Reliability-Optimized LLMs with Inspectable Tools
Ruixin Zhang, Jon Donnelly, Zhicheng Guo +4
Large language models (LLMs) have exhibited remarkable capabilities across various domains. The ability to call external tools further expands their capability to handle real-world…
34 Examples of LLM Applications in Materials Science and Chemistry: Towards Automation, Assistants, Agents, and Accelerated Scientific Discovery
Yoel Zimmermann, Adib Bazgir, Alexander Al-Feghali +32
Large Language Models (LLMs) are reshaping many aspects of materials science and chemistry research, enabling advances in molecular property prediction, materials design, scientifi…
Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
Yoel Zimmermann, Adib Bazgir, Zartashia Afzal +141
Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hyb…
Training Neural Networks as Recognizers of Formal Languages
Alexandra Butoi, Ghazal Khalighinejad, Anej Svete +3
Characterizing the computational power of neural network architectures in terms of formal language theory remains a crucial line of research, as it describes lower and upper bounds…
MatViX: Multimodal Information Extraction from Visually Rich Articles
Ghazal Khalighinejad, Sharon Scott, Ollie Liu +4
Multimodal information extraction (MIE) is crucial for scientific literature, where valuable data is often spread across text, figures, and tables. In materials science, extracting…
IsoBench: Benchmarking Multimodal Foundation Models on Isomorphic Representations
Deqing Fu, Ruohao Guo, Ghazal Khalighinejad +5
Current foundation models exhibit impressive capabilities when prompted either with text only or with both image and text inputs. But do their capabilities change depending on the…
Extracting Polymer Nanocomposite Samples from Full-Length Documents
Ghazal Khalighinejad, Defne Circi, L. C. Brinson +1
This paper investigates the use of large language models (LLMs) for extracting sample lists of polymer nanocomposites (PNCs) from full-length materials science research papers. The…
Approximating CKY with Transformers
Ghazal Khalighinejad, Ollie Liu, Sam Wiseman
We investigate the ability of transformer models to approximate the CKY algorithm, using them to directly predict a sentence's parse and thus avoid the CKY algorithm's cubic depend…
Galloping in fast-growth natural merge sorts
Elahe Ghasemi, Vincent Jugé, Ghazal Khalighinejad +1
We study the impact of merging routines in merge-based sorting algorithms. More precisely, we focus on the galloping routine that TimSort uses to merge monotonic sub-arrays, hereaf…