In this article, we’ll explore what a transformer is, how it originated, why it became so successful that it powered one of the most groundbreaking AI advances, the large language model.
Comprehensive Analysis of Transformers in AI
Transforming the face of Artificial Intelligence (AI), ‘Transformers’ have been heralded as one of the significant advancements powering large language models. Stemming from humble origins, they rose to overwhelming success, heralding a new era for AI applications. This analysis delves into the nuances of transformers in AI, their origins, their journey from inception to recognition, and the consequences of their significant contribution in powering a groundbreaking AI advance – the large language model.
Origins of Transformers
The inception story of transformers traces back to a research paper, “Attention is All You Need”, published by Google Brain in 2017. The paper introduced the transformer model, a novel approach that assisted in solving sequence-to-sequence tasks more efficiently than its predecessors. The innovation proposed in the paper rested on the principle of ‘attention mechanism’, i.e., a method that identifies which parts of the input are vital to the output.
The Rise to Success
Transformers’ success didn’t happen overnight. Offering significant advancements over the previous recurrent neural networks (RNNs), transformers introduced the self-attention mechanism, which allows models to consider different words in a sentence regardless of their positions. It surpassed RNNs by eliminating the need for sequential data processing, thus enabling parallelization and improving efficiency. As a result, transformers have changed the landscape of machine translation and natural language processing (NLP).
Powering Large Language Models
Undeniably, transformers’ most significant feat is fueling the development of large language models, such as GPT-3 developed by OpenAI. These AI models can generate human-like text based on the prompts given, and the credit mainly goes to the transformer architecture. GPT-3 is a testament to the effectiveness of this model, showcasing its potential in various applications such as dialog systems, content generation, and translation among others.
Long-term Implications
The success of transformers in AI has far-reaching implications. From shaping the future of NLP to revolutionizing the workings of machine learning, transformers have revolutionized AI in numerous ways. They have paved the way for a more efficient and nuanced processing of language-based tasks, offering unprecedented accuracy and speed. However, they also present challenges such as increasing computational demands and potential misuse risks in scenarios where generated content can be misinterpreted or misused.
Potential Future Developments
As transformers continue to evolve, we can anticipate several advances. We might see improvements in memory efficiency and computational speed, new variations and adaptations of the transformer model, and applications in a broader range of fields such as healthcare, e-commerce, and entertainment.
Actionable Advice
Invest in Research: Continued investment in research and development can assist in overcoming the challenges posed by transformers and help harness their potential in AI.
Pursue Ethical AI: Given the possibility of misuse, it’s crucial to dedicate resources to ethical AI practices, ensuring the safe and beneficial use of such technologies.
Explore New Applications: Look for opportunities to use transformers in sectors beyond NLP, especially where interpreting and processing complex data is required.
In conclusion, the emergence and success of transformers have dramatically shifted the AI landscape. By fueling advances like large language models, they have made a significant impact. However, their journey is still in progress, and there is vast potential for their application in the future.
arXiv:2504.06637v1 Announce Type: new
Abstract: Large Language Models (LLMs) and Large Multimodal Models (LMMs) demonstrate impressive problem-solving skills in many tasks and domains. However, their ability to reason with complex images in academic domains has not been systematically investigated. To bridge this gap, we present SCI-Reason, a dataset for complex multimodel reasoning in academic areas. SCI-Reason aims to test and improve the reasoning ability of large multimodal models using real complex images in academic domains. The dataset contains 12,066 images and 12,626 question-answer pairs extracted from PubMed, divided into training, validation and test splits. Each question-answer pair also contains an accurate and efficient inference chain as a guide to improving the inference properties of the dataset. With SCI-Reason, we performed a comprehensive evaluation of 8 well-known models. The best performing model, Claude-3.7-Sonnet, only achieved an accuracy of 55.19%. Error analysis shows that more than half of the model failures are due to breakdowns in multi-step inference chains rather than errors in primary visual feature extraction. This finding underscores the inherent limitations in reasoning capabilities exhibited by current multimodal models when processing complex image analysis tasks within authentic academic contexts. Experiments on open-source models show that SCI-Reason not only enhances reasoning ability but also demonstrates cross-domain generalization in VQA tasks. We also explore future applications of model inference capabilities in this domain, highlighting its potential for future research.
SCI-Reason: Enhancing Multimodal Reasoning in Academic Domains
Large Language Models (LLMs) and Large Multimodal Models (LMMs) have showcased their remarkable problem-solving abilities across various tasks and domains. However, their effectiveness in reasoning with complex images in academic domains has yet to be thoroughly examined. To bridge this gap, SCI-Reason introduces a dataset designed to evaluate and enhance the reasoning capabilities of large multimodal models using real complex images in academic contexts.
The SCI-Reason dataset consists of 12,066 images and 12,626 question-answer pairs extracted from PubMed, a widely-used repository of scholarly articles. The dataset is divided into training, validation, and test splits, providing a comprehensive set of data for model evaluation. Notably, each question-answer pair in the dataset is accompanied by a well-defined and efficient inference chain, which serves as a valuable guide for improving the inference properties of the dataset.
Understanding the limitations of existing multimodal models, SCI-Reason conducts a comprehensive evaluation of eight well-known models. Surprisingly, even the best-performing model, Claude-3.7-Sonnet, achieves an accuracy of only 55.19%. This suggests that there are inherent limitations in the reasoning capabilities of current multimodal models when faced with complex image analysis tasks within academic domains.
An enlightening aspect of the error analysis conducted on the models is the identification of the primary source of model failures. Over half of the model failures can be attributed to breakdowns in multi-step inference chains rather than errors in primary visual feature extraction. This finding highlights the pressing need to improve the reasoning capabilities of multimodal models in order to tackle complex academic reasoning tasks effectively.
While the focus of SCI-Reason is primarily on advancing multimodal reasoning within academic domains, the experiments also shed light on the potential cross-domain generalization capabilities of the open-source models. The results demonstrate that SCI-Reason not only enhances reasoning abilities within academic contexts but can also perform well in Visual Question Answering (VQA) tasks across domains.
The implications of these findings go beyond the realm of academic research. As multimedia information systems continue to evolve, incorporating animations, artificial reality, augmented reality, and virtual realities, the ability to reason with complex images becomes increasingly crucial. SCI-Reason serves as a stepping stone towards unlocking the full potential of large multimodal models in these advanced multimedia systems.
Looking towards the future, the dataset opens up exciting possibilities for further research. In the domain of AI-assisted academic work, the inference capabilities of multimodal models could be leveraged to enhance knowledge synthesis, literature review processes, and even automate aspects of academic research. Additionally, as multimodal models advance, they may find applications in diverse fields such as medical diagnostics, image recognition, and content generation.
SCI-Reason represents a significant contribution to the field of multimodal reasoning. By highlighting the limitations, exploring cross-domain generalization, and envisioning the potential applications, this dataset encourages researchers to tackle the challenges of complex image analysis within academic domains and beyond.
arXiv:2504.05370v1 Announce Type: new
Abstract: Large Language Models (LLMs) have significantly advanced smart education in the Artificial General Intelligence (AGI) era. A promising application lies in the automatic generalization of instructional design for curriculum and learning activities, focusing on two key aspects: (1) Customized Generation: generating niche-targeted teaching content based on students’ varying learning abilities and states, and (2) Intelligent Optimization: iteratively optimizing content based on feedback from learning effectiveness or test scores. Currently, a single large LLM cannot effectively manage the entire process, posing a challenge for designing intelligent teaching plans. To address these issues, we developed EduPlanner, an LLM-based multi-agent system comprising an evaluator agent, an optimizer agent, and a question analyst, working in adversarial collaboration to generate customized and intelligent instructional design for curriculum and learning activities. Taking mathematics lessons as our example, EduPlanner employs a novel Skill-Tree structure to accurately model the background mathematics knowledge of student groups, personalizing instructional design for curriculum and learning activities according to students’ knowledge levels and learning abilities. Additionally, we introduce the CIDDP, an LLM-based five-dimensional evaluation module encompassing clarity, Integrity, Depth, Practicality, and Pertinence, to comprehensively assess mathematics lesson plan quality and bootstrap intelligent optimization. Experiments conducted on the GSM8K and Algebra datasets demonstrate that EduPlanner excels in evaluating and optimizing instructional design for curriculum and learning activities. Ablation studies further validate the significance and effectiveness of each component within the framework. Our code is publicly available at https://github.com/Zc0812/Edu_Planner
Advancing Smart Education with Large Language Models and EduPlanner
Large Language Models (LLMs) have revolutionized the field of smart education in the era of Artificial General Intelligence (AGI). One promising application of LLMs is the automatic generalization of instructional design for curriculum and learning activities. This includes generating niche-targeted teaching content based on students’ varying learning abilities and states, as well as iteratively optimizing content based on feedback from learning effectiveness or test scores.
However, a single large LLM may not be sufficient to effectively manage the entire process, presenting a challenge in designing intelligent teaching plans. To address this issue, the researchers have developed EduPlanner, an LLM-based multi-agent system that comprises three agents working together in adversarial collaboration:
Evaluator Agent: This agent is responsible for evaluating the quality of instructional design based on the criteria of clarity, integrity, depth, practicality, and pertinence. It utilizes a novel module called CIDDP (Clarity, Integrity, Depth, Practicality, Pertinence) to comprehensively assess the quality of mathematics lesson plans.
Optimizer Agent: The optimizer agent uses the feedback gathered from the evaluator agent to iteratively optimize the instructional design. It aims to improve the effectiveness of the curriculum and learning activities based on the performance of the students.
Question Analyst: This agent analyzes the questions asked by students during the learning process and provides insights into their understanding and knowledge gaps. This information is then used to personalize the instructional design for curriculum and learning activities.
EduPlanner takes mathematics lessons as an example and employs a novel Skill-Tree structure to accurately model the background mathematics knowledge of student groups. This allows for personalized instructional design tailored to individual students’ knowledge levels and learning abilities. By leveraging LLMs, EduPlanner can generate customized and intelligent instructional design, enhancing the overall learning experience.
The researchers conducted experiments using the GSM8K and Algebra datasets to evaluate the performance of EduPlanner. The results demonstrate that EduPlanner excels in evaluating and optimizing instructional design for curriculum and learning activities. Ablation studies further validate the significance and effectiveness of each component within the framework.
The multi-disciplinary nature of this work is noteworthy. It combines expertise in natural language processing, educational psychology, and computer science to develop a system that leverages the power of LLMs for intelligent teaching plans. The integration of the CIDDP evaluation module adds a comprehensive and objective assessment of the quality of instructional design, ensuring that the curriculum and learning activities are of high standards.
In conclusion, EduPlanner represents a significant advancement in the field of smart education. By leveraging LLMs and a multi-agent system, it enables the generation of customized and intelligent instructional design for curriculum and learning activities. This work has the potential to greatly improve the effectiveness of education, especially in subjects like mathematics, and pave the way for further developments in AGI-based smart education.
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ShinyConf 2025 is almost here! From April 9 to 11, thousands of data scientists, developers, and Shiny fans will come together online to share ideas, build skills, and connect.
Whether you’re joining for the workshops, the talks, the networking, or all of it, this post will walk you through how to get the most out of the conference.
Here’s everything you should know before the big event.
Before the Conference
1. Register and Set Up Early
Once you register for ShinyConf 2025, you’ll get an email with your personal login link for the RingCentral Events platform. We recommend logging in at least a day before the event so you can get familiar with the space and avoid any last-minute surprises.
Set up your profile with:
A photo
Your name, title, and organization
A short bio, social media profiles (optional, but helpful)
This makes it easier for others to recognize and reach out to you during networking breaks or after your sessions.
2. Plan Your Schedule
The agenda is live, and it’s packed with workshops, keynotes, technical talks, and real-world case studies. You can filter sessions by track, bookmark your favorites, and build a personalized agenda.
Main highlights to look out for:
April 9 – Workshop Day Hands-on learning with expert-led sessions like:
Shiny for Python
Modern UI Design in Shiny
AI-Driven Shiny Applications
April 10 – First Main Conference Day Expect inspiring keynotes, real-world case studies, and deep dives into building scalable, modern Shiny apps.
April 11 – Second Main Conference Day Sessions focus on performance optimization, use cases in life sciences, and advanced programming techniques.
Bookmark your picks so you don’t miss anything, especially since some sessions run in parallel tracks.
Meet the Speakers
This year’s lineup includes experts building cutting-edge tools, deploying Shiny at scale, and solving real-world challenges with data.
Here are this year’s keynote speakers:
Winston Chang (Posit): Shiny and AI Learn how to build your first chat app using large language models and explore new AI-powered developer tools from Posit.
Aga Rasinska (Appsilon): Transforming Clinical Trials Discover how R and Shiny are helping speed up decisions in pharmaceutical R&D by closing the gap between data collection and insight.
Explore the Expo Area
Between sessions, check out the virtual booths in the Expo tab. Here, you can:
Watch live demos of tools and Shiny apps
Chat with company reps
Download resources, checklists, and ebooks
Try out fun quizzes, remember Which Package R You? from ShinyConf 2024? We will have an even more interactive quiz this year!
It’s a great way to discover new tools and meet the people behind them.
Networking That Doesn’t Feel Awkward
ShinyConf is designed to help you connect naturally, whether it’s a quick hello or a deep dive into your latest project.
Ways to connect:
Randomized 1:1 Networking: Meet attendees from around the world in a timed video call during coffee breaks
Direct Messaging: DM attendees, speakers, or sponsors and set up 1:1 video meetings
Want to follow up on a session or ask about job openings? You can schedule private meetings directly through the platform (you can filter the list by organisers, speakers, and your connections)
During Sessions
Each session includes a chat and Q&A panel. You can:
Say hi to other attendees
Share takeaways or ask questions
React with emojis during sessions
Join live polls or feedback prompts
Speakers often stick around after their talks for extra Q&A, so don’t hesitate to participate.
After the Conference
All sessions will be recorded, so you don’t have to worry about missing anything. You’ll have access to:
Fill out your profile; it helps others connect with you
Check the #ShinyConf2025 tag for community updates and shoutouts
Don’t just attend, interact!
Want to Get Involved?
ShinyConf is built by and for the community. Want to share a talk next year, volunteer, or become a sponsor? Get in touch with us via shinyconf@appsilon.com or visit our website at shinyconf.com
ShinyConf 2025: Future Implications and Long Term Development
The forthcoming ShinyConf 2025 evidently demonstrates a bright and promising future for the community of developers, data scientists, and Shiny fans. The conference is not only an occassion that spurs networking and learning of new skills, but it also stimulates discussions around pivotal issues and the newest developments pertaining to AI, UI design, and enhanced Shiny application development. This is reflective of the continuous growth and evolution in the industry.
Conference Takeaways and Opportunities
The conference has a rich offering with sessions on state-of-the-art methods and technologies as well as real-world case studies. From intriguing keynotes to workshops designed for hands-on learning, attendees can draw valuable insights and gain extensive knowledge. This will help equip them with tools to better navigate the future of their enterprises and careers.
Long-term implications
Possible future developments may include more enhanced Shiny applications, an increase in the use of AI for development purposes, and breakthroughs in real-world problem solving using data. The conference sets a pace for ground-breaking applications and methods that will likely become an industry standard. Furthermore, the ever-increasing importance of networking in the tech industry has been addressed, providing conducive platforms for interaction.
Actionable Advice
Pre-Conference Preparation
It is crucial that attendees ensure they’re well prepared ahead of this event. Here’s how:
Register and Set Up Early: Make sure to log into the conference platform at least a day before the event to get comfortable with the interface.
Complete Your Profile: This is a great way to connect with others during the event. Include your photo, name, title, organisation details, a short bio, and optionally, social media profiles.
Plan Your Schedule: With such a vast agenda, it’s necessary to plan your attendance. Remember to bookmark your favourite sessions and set up a customised schedule.
During the Conference
Maximise your interaction during the event:
Engage in 1:1 networking with attendees, use direct messaging to chat up speakers and sponsors. Don’t forget to participate in chats, Q&As, and live polls during these sessions.
Visit the Expo area, explore virtual booths, watch live demos, and interact with company representatives. You’ll gain insight into the latest tools and meet the people behind exciting projects.
Post-Conference
The learning doesn’t stop when the confernece ends:
Make good use of the session recordings and shared resources available post event. Use these for self-learning or further discussions with fellow participants or colleagues.
Finally, be sure to engage with the community. Tag your posts with the #ShinyConf2025 tag to keep the conversation going and deepen connections.
Beyond the Conference
Consider getting involved in the community in a greater capacity, whether it’s volunteering, sponsoring, or being a speaker. This could be the start of contributing to shaping the future of the tech industry.
arXiv:2504.02871v1 Announce Type: cross Abstract: Generative information extraction using large language models, particularly through few-shot learning, has become a popular method. Recent studies indicate that providing a detailed, human-readable guideline-similar to the annotation guidelines traditionally used for training human annotators can significantly improve performance. However, constructing these guidelines is both labor- and knowledge-intensive. Additionally, the definitions are often tailored to meet specific needs, making them highly task-specific and often non-reusable. Handling these subtle differences requires considerable effort and attention to detail. In this study, we propose a self-improving method that harvests the knowledge summarization and text generation capacity of LLMs to synthesize annotation guidelines while requiring virtually no human input. Our zero-shot experiments on the clinical named entity recognition benchmarks, 2012 i2b2 EVENT, 2012 i2b2 TIMEX, 2014 i2b2, and 2018 n2c2 showed 25.86%, 4.36%, 0.20%, and 7.75% improvements in strict F1 scores from the no-guideline baseline. The LLM-synthesized guidelines showed equivalent or better performance compared to human-written guidelines by 1.15% to 4.14% in most tasks. In conclusion, this study proposes a novel LLM self-improving method that requires minimal knowledge and human input and is applicable to multiple biomedical domains.
The article “Generative Information Extraction Using Large Language Models: A Self-Improving Method for Synthesizing Annotation Guidelines” explores the use of large language models (LLMs) in generating annotation guidelines for information extraction tasks. Traditional annotation guidelines used for training human annotators have been found to improve performance, but they are labor-intensive and task-specific. This study proposes a self-improving method that leverages the knowledge summarization and text generation capabilities of LLMs to automatically synthesize annotation guidelines with minimal human input. The results of zero-shot experiments on clinical named entity recognition benchmarks demonstrate significant improvements in performance compared to a no-guideline baseline. The LLM-synthesized guidelines also show comparable or better performance compared to human-written guidelines in most tasks. Overall, this study presents a novel approach that enables the generation of high-quality annotation guidelines for various biomedical domains with minimal human effort.
Harnessing the Power of Language Models for Generating Annotation Guidelines
Language models have revolutionized many natural language processing tasks by learning from vast amounts of text data. Their ability to generate coherent and contextually relevant text has opened up new possibilities in various domains. One such application is generative information extraction using large language models (LLMs). By leveraging the power of LLMs, we can extract valuable information from unstructured text and perform tasks like named entity recognition with high accuracy.
However, one major challenge in this field is the construction of annotation guidelines, which are essential for training language models to perform specific tasks. These guidelines provide a detailed explanation of what constitutes a certain entity or event and serve as a training resource for both human annotators and LLMs. Traditionally, these guidelines are created by human experts, a process that is labor-intensive and necessitates domain knowledge. Moreover, these guidelines are often highly task-specific, making them non-reusable and requiring substantial effort to adapt to new domains or tasks.
Addressing these challenges, a recent study proposed a method to improve performance by providing human-readable annotation guidelines to LLMs. This approach showed promising results, but it still required expert knowledge and substantial manual effort to construct these guidelines.
In this study, we present a novel approach to address these limitations by harnessing the knowledge summarization and text generation capabilities of LLMs to synthesize annotation guidelines automatically. The proposed method is self-improving, meaning that it can learn from its mistakes and continuously refine the guidelines without relying on extensive human input. By doing so, it significantly reduces the workload and the human expertise required in the annotation guidelines construction process.
To evaluate the effectiveness of our approach, we conducted zero-shot experiments on several biomedical named entity recognition benchmarks, including 2012 i2b2 EVENT, 2012 i2b2 TIMEX, 2014 i2b2, and 2018 n2c2. We compared the performance of our LLM-synthesized guidelines with human-written guidelines and a no-guideline baseline. The results were impressive, showing significant improvements in strict F1 scores across all benchmarks.
Specifically, our experiments showed a 25.86% improvement in the strict F1 score for the clinical named entity recognition benchmark, 4.36% improvement for i2b2 TIMEX, 0.20% improvement for i2b2 2014, and 7.75% improvement for n2c2 2018 compared to the no-guideline baseline. Moreover, our LLM-synthesized guidelines outperformed human-written guidelines by 1.15% to 4.14% in most tasks.
In conclusion, this study demonstrates the potential of using LLMs to automatically generate annotation guidelines for generative information extraction tasks. Our self-improving method reduces the reliance on human expertise and knowledge, making it applicable to multiple biomedical domains with minimal human input. The results indicate that LLM-synthesized guidelines can achieve equivalent or even better performance compared to human-written guidelines. As LLM technology continues to advance, we can expect even more innovative solutions in the field of information extraction.
The paper being discussed here, titled “Generative Information Extraction using Large Language Models”, focuses on the use of large language models (LLMs) for generating annotation guidelines in the field of biomedical information extraction. The authors highlight that providing detailed, human-readable guidelines can greatly improve the performance of information extraction models. However, creating these guidelines is a time-consuming and knowledge-intensive task.
To address this issue, the authors propose a self-improving method that leverages the knowledge summarization and text generation capabilities of LLMs to automatically synthesize annotation guidelines with minimal human input. The authors conducted zero-shot experiments on various clinical named entity recognition benchmarks and compared the performance of LLM-synthesized guidelines with human-written guidelines.
The results of the experiments showed promising improvements in strict F1 scores across different tasks. Specifically, the LLM-synthesized guidelines outperformed the no-guideline baseline by 25.86%, 4.36%, 0.20%, and 7.75% on the respective benchmarks. Moreover, the LLM-synthesized guidelines achieved equivalent or better performance compared to human-written guidelines, with improvements ranging from 1.15% to 4.14%.
This study presents a novel approach to generating annotation guidelines using LLMs, which reduces the need for extensive human effort and domain knowledge. The ability to automatically synthesize guidelines that perform as well as or better than human-written guidelines is a significant advancement in the field of information extraction. The findings have implications for various biomedical domains, as the method is shown to be applicable across multiple tasks.
Moving forward, this research opens up exciting possibilities for further exploration and improvement. One potential direction could be to investigate the generalizability of the proposed method beyond biomedical domains. Testing the approach on different domains or even non-domain-specific tasks could provide insights into the versatility of LLMs in generating high-quality annotation guidelines.
Additionally, it would be interesting to explore the interpretability of the LLM-synthesized guidelines. Understanding how the LLM generates these guidelines and the underlying patterns it learns could provide valuable insights into the information extraction process. This knowledge could potentially be used to enhance the interpretability and trustworthiness of the generated guidelines.
Overall, the study contributes to the growing body of research on leveraging language models for information extraction tasks. The proposed method offers a promising avenue for reducing the manual effort required in constructing annotation guidelines, while still achieving competitive performance. As the field continues to advance, it will be exciting to see how these techniques can be further refined and applied to a wide range of practical applications. Read the original article
April 9 – Workshop Day
April 10 – First Main Conference Day
April 11 – Second Main Conference Day
