by jsendak | May 20, 2025 | Computer Science
Expert Commentary: Enhancing Reinforcement Learning in Large Language Models
Reinforcement Learning (RL) has become a key technique in improving the reasoning abilities of large language models (LLMs) such as DeepSeek-R1. One popular RL method, Group Relative Policy Optimization (GRPO), has been successful in training these models, but faces challenges when all sampled responses in a group are incorrect, leading to what is known as an “all-negative-sample” group. This can hinder learning progress as GRPO fails to update the policy in such cases.
The recent paper introduces a novel framework to address this issue by introducing response diversity within these all-negative-sample groups using AI feedback. The addition of this diversification not only improves learning dynamics, as shown through theoretical analysis, but also leads to enhanced performance across different model sizes and learning settings in offline and online scenarios.
This research contributes significantly to the understanding of learning dynamics in RL for LLMs, building upon recent insights from related work. By showing the feasibility and benefits of learning from all-negative-sample groups, this work opens up new avenues for enhancing the performance and capabilities of language models through reinforcement learning techniques.
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by jsendak | May 19, 2025 | Computer Science
arXiv:2505.11237v1 Announce Type: new
Abstract: Metaphorical imagination, the ability to connect seemingly unrelated concepts, is fundamental to human cognition and communication. While understanding linguistic metaphors has advanced significantly, grasping multimodal metaphors, such as those found in internet memes, presents unique challenges due to their unconventional expressions and implied meanings. Existing methods for multimodal metaphor identification often struggle to bridge the gap between literal and figurative interpretations. Additionally, generative approaches that utilize large language models or text-to-image models, while promising, suffer from high computational costs. This paper introduces textbf{C}oncept textbf{D}rift textbf{G}uided textbf{L}ayerNorm textbf{T}uning (textbf{CDGLT}), a novel and training-efficient framework for multimodal metaphor identification. CDGLT incorporates two key innovations: (1) Concept Drift, a mechanism that leverages Spherical Linear Interpolation (SLERP) of cross-modal embeddings from a CLIP encoder to generate a new, divergent concept embedding. This drifted concept helps to alleviate the gap between literal features and the figurative task. (2) A prompt construction strategy, that adapts the method of feature extraction and fusion using pre-trained language models for the multimodal metaphor identification task. CDGLT achieves state-of-the-art performance on the MET-Meme benchmark while significantly reducing training costs compared to existing generative methods. Ablation studies demonstrate the effectiveness of both Concept Drift and our adapted LN Tuning approach. Our method represents a significant step towards efficient and accurate multimodal metaphor understanding. The code is available: href{https://github.com/Qianvenh/CDGLT}{https://github.com/Qianvenh/CDGLT}.
Expert Commentary
The ability to understand and convey metaphors is a crucial aspect of human communication and cognition. When it comes to multimodal metaphors, such as those seen in internet memes, the challenges are unique due to their unconventional expressions and implied meanings. This paper introduces the CDGLT framework, which aims to address these challenges in a training-efficient manner.
The CDGLT framework incorporates innovative concepts like Concept Drift, which leverages cross-modal embeddings to generate new, divergent concept embeddings. This helps bridge the gap between literal features and the figurative task of identifying multimodal metaphors. Additionally, the prompt construction strategy utilized in CDGLT adapts feature extraction and fusion methods using pre-trained language models, further enhancing the framework’s effectiveness.
From a multidisciplinary perspective, this research combines concepts from natural language processing, computer vision, and cognitive psychology to develop a solution for multimodal metaphor identification. By tapping into the fields of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities, the CDGLT framework showcases the potential for interdisciplinary collaboration in advancing understanding of complex cognitive processes.
Furthermore, the state-of-the-art performance of CDGLT on the MET-Meme benchmark highlights its efficacy in tackling the challenges posed by multimodal metaphors. The reduced training costs compared to existing generative methods make CDGLT a promising tool for researchers and practitioners interested in multimodal metaphor understanding.
In conclusion, the CDGLT framework represents a significant contribution to the field of multimodal metaphor identification, paving the way for more efficient and accurate methods of analyzing complex and layered forms of communication.
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by jsendak | May 16, 2025 | Computer Science
arXiv:2505.09936v1 Announce Type: cross
Abstract: The rapid development of generative artificial intelligence (GenAI) presents new opportunities to advance the cartographic process. Previous studies have either overlooked the artistic aspects of maps or faced challenges in creating both accurate and informative maps. In this study, we propose CartoAgent, a novel multi-agent cartographic framework powered by multimodal large language models (MLLMs). This framework simulates three key stages in cartographic practice: preparation, map design, and evaluation. At each stage, different MLLMs act as agents with distinct roles to collaborate, discuss, and utilize tools for specific purposes. In particular, CartoAgent leverages MLLMs’ visual aesthetic capability and world knowledge to generate maps that are both visually appealing and informative. By separating style from geographic data, it can focus on designing stylesheets without modifying the vector-based data, thereby ensuring geographic accuracy. We applied CartoAgent to a specific task centered on map restyling-namely, map style transfer and evaluation. The effectiveness of this framework was validated through extensive experiments and a human evaluation study. CartoAgent can be extended to support a variety of cartographic design decisions and inform future integrations of GenAI in cartography.
Expert Commentary: The Future of Cartography with Generative AI
In the age of rapid technological advancements, the integration of generative artificial intelligence (GenAI) in cartographic processes presents exciting new opportunities. Traditional approaches to map design often struggle to balance accuracy with aesthetic appeal, but the emergence of multimodal large language models (MLLMs) opens up a new realm of possibilities.
CartoAgent, the novel framework proposed in this study, leverages the power of MLLMs to simulate key stages in cartographic practice, such as preparation, map design, and evaluation. By assigning different MLLMs as agents with specific roles, CartoAgent enables collaboration and discussion between these virtual entities to produce visually appealing and informative maps.
One of the most intriguing aspects of CartoAgent is its ability to separate style from geographic data, allowing for the creation of unique map styles without compromising geographic accuracy. This innovative approach to map restyling, demonstrated through map style transfer and evaluation tasks, showcases the potential of GenAI in revolutionizing cartography.
As an expert commentator in the field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities, I see the multi-disciplinary nature of this research as a bridge between the realms of AI and cartography. The integration of GenAI in cartographic design decisions is a promising path towards more efficient and creative map-making processes.
Future advancements in CartoAgent could lead to even more sophisticated map design techniques and ultimately transform the way we interact with and interpret geographic information. This study sets the stage for further exploration and integration of GenAI in the field of cartography, offering a glimpse into the exciting possibilities that lie ahead.
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by jsendak | May 13, 2025 | DS Articles
[This article was first published on
R-posts.com, and kindly contributed to
R-bloggers]. (You can report issue about the content on this page
here)
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Join our workshop on Using LLMs with ellmer, which is a part of our workshops for Ukraine series!
Here’s some more info:
Title: Using LLMs with ellmer
Date: Friday, June 13th, 18:00 – 20:00 CEST (Rome, Berlin, Paris timezone)
Speaker: Hadley Wickham is Chief Scientist at Posit PBC, winner of the 2019 COPSS award, and a member of the R Foundation. He builds tools (both computational and cognitive) to make data science easier, faster, and more fun. His work includes packages for data science (like the tidyverse, which includes ggplot2, dplyr, and tidyr)and principled software development (e.g. roxygen2, testthat, and pkgdown). He is also a writer, educator, and speaker promoting the use of R for data science. Learn more on his website, <http://hadley.nz>.
Description: Join us for an engaging, hands-on hackathon workshop where you’ll learn to use large language models (LLMs) from R with the ellmer (https://ellmer.tidyverse.org) package. In this 2-hour session, we’ll combine theory with practical exercises to help you create AI-driven solutions—no extensive preparation needed!
## What you’ll learn:
– A quick intro to LLMs: what they’re good at and where they struggle
– How to use ellmer with different model providers (OpenAI, Anthropic, Google Gemini, and others)
– Effective prompt design strategies and practical applications for your work
– Function calling: how to let LLMs use R functions for tasks they can’t handle well
– Extracting structured data from text, images, and video using LLMs
## What you’ll need:
– A laptop with R installed
– The development version of ellmer (`pak::pak(“tidyverse/ellmer”))`
– An account with either Claude (cheap) or Google Gemini (free).
Follow the instructions at <github.com/hadley/workshop-llm-hackathon> to get setup.
Minimal registration fee: 20 euro (or 20 USD or 800 UAH)
Please note that the registration confirmation is sent 1 day before the workshop to all registered participants rather than immediately after registration
How can I register?
- Save your donation receipt (after the donation is processed, there is an option to enter your email address on the website to which the donation receipt is sent)
- Fill in the registration form, attaching a screenshot of a donation receipt (please attach the screenshot of the donation receipt that was emailed to you rather than the page you see after donation).
If you are not personally interested in attending, you can also contribute by sponsoring a participation of a student, who will then be able to participate for free. If you choose to sponsor a student, all proceeds will also go directly to organisations working in Ukraine. You can either sponsor a particular student or you can leave it up to us so that we can allocate the sponsored place to students who have signed up for the waiting list.
How can I sponsor a student?
- Save your donation receipt (after the donation is processed, there is an option to enter your email address on the website to which the donation receipt is sent)
- Fill in the sponsorship form, attaching the screenshot of the donation receipt (please attach the screenshot of the donation receipt that was emailed to you rather than the page you see after the donation). You can indicate whether you want to sponsor a particular student or we can allocate this spot ourselves to the students from the waiting list. You can also indicate whether you prefer us to prioritize students from developing countries when assigning place(s) that you sponsored.
If you are a university student and cannot afford the registration fee, you can also sign up for the waiting list here. (Note that you are not guaranteed to participate by signing up for the waiting list).
You can also find more information about this workshop series, a schedule of our future workshops as well as a list of our past workshops which you can get the recordings & materials here.
Looking forward to seeing you during the workshop!
Using LLMs with ellmer workshop by Hadley Wickham was first posted on May 13, 2025 at 3:06 pm.
Continue reading: Using LLMs with ellmer workshop by Hadley Wickham
Analysis: The Future of LLMs with ellmer Workshops
In the ever-evolving field of data science, continuous learning and keeping up-to-date with the latest technologies and methodologies are of utmost importance. A recent announcement on R-bloggers.com discussed a fast-approaching online workshop on ‘Using LLMs with ellmer’ which undoubtedly caught the attention of many data science enthusiasts.
Implications and Future Developments
Large Language Models (LLMs), as introduced in this workshop, are a critical component in the realm of AI, capable of understanding and generating human-like text. Notably, the ellmer package enables these advanced AI capabilities to be integrated into the R environment. Ensuring that data scientists are adept in such tools has long-term implications for the speed, efficiency, and novel applications in data science.
Hadley Wickham, the speaker for this session, is a distinguished data scientist and prolific contributor to R packages, making the promise of future workshops held by him or speakers of a similar calibre, highly beneficial for learners. It’s quite plausible that the increased demand for these workshops could lead them to become a regular occurrence, facilitating upskilling in the R community.
In the future, we might see an expansion of topics, covering more R packages and advanced AI techniques. Furthermore, the flexible approach today’s workshop adopted towards payment (acceptable in different currencies and also by sponsoring a student) combined with its charitable cause, paints an encouraging picture of an inclusive learning community that values diversity and social responsibility. This could lead to increased accessibility in the future, as more and more professionals and students benefit from these affordable (or sponsored) learning opportunities.
Actionable Advice
- Stay Informed: Regularly check R-bloggers and similar resources for updates about forthcoming workshops and apply promptly. Remember that registration confirmations are sent out a day before the workshop.
- Prepare Adequately: Ensuring that the necessary prerequisites are met before the workshop (such as having R installed and setting up the ellmer package) allows for a more effective learning experience.
- Be Charitable: If able, consider sponsoring a student. This not only supports the learning of individuals unable to afford the fee, but additionally contributes towards addressing social implications in areas such as Ukraine.
- Take Part: Even if one is not an R user, such workshops, often held by industry experts, offer valuable insights which could be applied to data science work in general.
By utilizing such actionable advice, not only can individuals further their personal knowledge and skills, but the broader R, data science, and AI communities can continue to grow and evolve positively.
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by jsendak | May 9, 2025 | AI
arXiv:2505.04638v1 Announce Type: new
Abstract: Large Language Models (LLMs) and Large Multi-Modal Models (LMMs) have emerged as transformative tools in scientific research, yet their reliability and specific contributions to biomedical applications remain insufficiently characterized. In this study, we present textbf{AR}tificial textbf{I}ntelligence research assistant for textbf{E}xpert-involved textbf{L}earning (ARIEL), a multimodal dataset designed to benchmark and enhance two critical capabilities of LLMs and LMMs in biomedical research: summarizing extensive scientific texts and interpreting complex biomedical figures. To facilitate rigorous assessment, we create two open-source sets comprising biomedical articles and figures with designed questions. We systematically benchmark both open- and closed-source foundation models, incorporating expert-driven human evaluations conducted by doctoral-level experts. Furthermore, we improve model performance through targeted prompt engineering and fine-tuning strategies for summarizing research papers, and apply test-time computational scaling to enhance the reasoning capabilities of LMMs, achieving superior accuracy compared to human-expert corrections. We also explore the potential of using LMM Agents to generate scientific hypotheses from diverse multimodal inputs. Overall, our results delineate clear strengths and highlight significant limitations of current foundation models, providing actionable insights and guiding future advancements in deploying large-scale language and multi-modal models within biomedical research.
Expert Commentary on Large Language Models and Multi-Modal Models in Biomedical Research
Large Language Models (LLMs) and Large Multi-Modal Models (LMMs) have been at the forefront of scientific research, revolutionizing the way we approach data analysis and interpretation. In this study, the researchers introduce ARIEL, a multimodal dataset specifically tailored for benchmarking and enhancing the capabilities of LLMs and LMMs in the field of biomedical research. This marks a significant step towards harnessing the power of artificial intelligence in a domain that is crucial for advancing healthcare and medical knowledge.
Interdisciplinary Approach
One of the key aspects of this study is the multi-disciplinary nature of the concepts explored. By combining expertise in artificial intelligence, natural language processing, and biomedical research, the researchers have been able to create a dataset that challenges current models to perform tasks specific to the biomedical domain. This highlights the importance of collaboration across different fields to push the boundaries of what is possible with AI technologies.
Enhancing Model Performance
The researchers go beyond simply benchmarking existing models and delve into strategies for improving performance. By incorporating expert evaluations and fine-tuning strategies, they are able to enhance the summarization and interpretation capabilities of these models. This approach not only highlights the potential of AI in biomedical research but also underscores the importance of continuous refinement and optimization to achieve superior results.
Future Directions
The findings of this study offer valuable insights into the strengths and limitations of current foundation models in the context of biomedical applications. By identifying areas for improvement and providing actionable recommendations, the researchers pave the way for future advancements in the deployment of LLMs and LMMs in biomedical research. The exploration of using LMM Agents to generate scientific hypotheses further opens up new possibilities for leveraging multimodal inputs in research settings.
This study serves as a compelling example of how artificial intelligence can be harnessed to drive innovation in complex domains such as biomedical research. By continuing to push the boundaries of what is possible with large-scale language and multi-modal models, we are likely to see even greater advancements in scientific discovery and knowledge generation.
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