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Found 8 papers, 2 papers with code
Context-Enhanced Language Models for Generating Multi-Paper Citations
This research underscores the potential of harnessing LLMs for citation generation, opening a compelling avenue for exploring the intricate connections between scientific documents.
MM-PhyRLHF: Reinforcement Learning Framework for Multimodal Physics Question-Answering
We employ the LLaVA open-source model to answer multimodal physics MCQs and compare the performance with and without using RLHF.
Mathify: Evaluating Large Language Models on Mathematical Problem Solving Tasks
Our experiments reveal that among the three models, MAmmoTH-13B emerges as the most proficient, achieving the highest level of competence in solving the presented mathematical problems.
TC-OCR: TableCraft OCR for Efficient Detection & Recognition of Table Structure & Content
Our proposed approach achieves an IOU of 0. 96 and an OCR Accuracy of 78%, showcasing a remarkable improvement of approximately 25% in the OCR Accuracy compared to the previous Table Transformer approach.
KG-CTG: Citation Generation through Knowledge Graph-guided Large Language Models
Citation Text Generation (CTG) is a task in natural language processing (NLP) that aims to produce text that accurately cites or references a cited document within a source document.
RanLayNet: A Dataset for Document Layout Detection used for Domain Adaptation and Generalization
To solve this problem, domain adaptation approaches have been developed that use a small quantity of labeled data to adjust the model to the target domain.
MM-PhyQA: Multimodal Physics Question-Answering With Multi-Image CoT Prompting
While Large Language Models (LLMs) can achieve human-level performance in various tasks, they continue to face challenges when it comes to effectively tackling multi-step physics reasoning tasks.
Advancements in Scientific Controllable Text Generation Methods
The previous work on controllable text generation is organized using a new schema we provide in this study.
