MisSynth: Improving MISSCI Logical Fallacies Classification with Synthetic Datacs.AI updates on arXiv.org arXiv:2510.26345v1 Announce Type: cross
Abstract: Health-related misinformation is very prevalent and potentially harmful. It is difficult to identify, especially when claims distort or misinterpret scientific findings. We investigate the impact of synthetic data generation and lightweight fine-tuning techniques on the ability of large language models (LLMs) to recognize fallacious arguments using the MISSCI dataset and framework. In this work, we propose MisSynth, a pipeline that applies retrieval-augmented generation (RAG) to produce synthetic fallacy samples, which are then used to fine-tune an LLM model. Our results show substantial accuracy gains with fine-tuned models compared to vanilla baselines. For instance, the LLaMA 3.1 8B fine-tuned model achieved an over 35% F1-score absolute improvement on the MISSCI test split over its vanilla baseline. We demonstrate that introducing synthetic fallacy data to augment limited annotated resources can significantly enhance zero-shot LLM classification performance on real-world scientific misinformation tasks, even with limited computational resources. The code and synthetic dataset are available on https://github.com/mxpoliakov/MisSynth.
arXiv:2510.26345v1 Announce Type: cross
Abstract: Health-related misinformation is very prevalent and potentially harmful. It is difficult to identify, especially when claims distort or misinterpret scientific findings. We investigate the impact of synthetic data generation and lightweight fine-tuning techniques on the ability of large language models (LLMs) to recognize fallacious arguments using the MISSCI dataset and framework. In this work, we propose MisSynth, a pipeline that applies retrieval-augmented generation (RAG) to produce synthetic fallacy samples, which are then used to fine-tune an LLM model. Our results show substantial accuracy gains with fine-tuned models compared to vanilla baselines. For instance, the LLaMA 3.1 8B fine-tuned model achieved an over 35% F1-score absolute improvement on the MISSCI test split over its vanilla baseline. We demonstrate that introducing synthetic fallacy data to augment limited annotated resources can significantly enhance zero-shot LLM classification performance on real-world scientific misinformation tasks, even with limited computational resources. The code and synthetic dataset are available on https://github.com/mxpoliakov/MisSynth. Read More
Unravelling the Mechanisms of Manipulating Numbers in Language Modelscs.AI updates on arXiv.org arXiv:2510.26285v1 Announce Type: cross
Abstract: Recent work has shown that different large language models (LLMs) converge to similar and accurate input embedding representations for numbers. These findings conflict with the documented propensity of LLMs to produce erroneous outputs when dealing with numeric information. In this work, we aim to explain this conflict by exploring how language models manipulate numbers and quantify the lower bounds of accuracy of these mechanisms. We find that despite surfacing errors, different language models learn interchangeable representations of numbers that are systematic, highly accurate and universal across their hidden states and the types of input contexts. This allows us to create universal probes for each LLM and to trace information — including the causes of output errors — to specific layers. Our results lay a fundamental understanding of how pre-trained LLMs manipulate numbers and outline the potential of more accurate probing techniques in addressed refinements of LLMs’ architectures.
arXiv:2510.26285v1 Announce Type: cross
Abstract: Recent work has shown that different large language models (LLMs) converge to similar and accurate input embedding representations for numbers. These findings conflict with the documented propensity of LLMs to produce erroneous outputs when dealing with numeric information. In this work, we aim to explain this conflict by exploring how language models manipulate numbers and quantify the lower bounds of accuracy of these mechanisms. We find that despite surfacing errors, different language models learn interchangeable representations of numbers that are systematic, highly accurate and universal across their hidden states and the types of input contexts. This allows us to create universal probes for each LLM and to trace information — including the causes of output errors — to specific layers. Our results lay a fundamental understanding of how pre-trained LLMs manipulate numbers and outline the potential of more accurate probing techniques in addressed refinements of LLMs’ architectures. Read More
Building a Rules Engine from First PrinciplesTowards Data Science How recasting propositional logic as sparse algebra leads to an elegant and efficient design
The post Building a Rules Engine from First Principles appeared first on Towards Data Science.
How recasting propositional logic as sparse algebra leads to an elegant and efficient design
The post Building a Rules Engine from First Principles appeared first on Towards Data Science. Read More
Build LLM Agents Faster with Datapizza AITowards Data Science Intro Organizations are increasingly investing in AI as these new tools are adopted in everyday operations more and more. This continuous wave of innovation is fueling the demand for more efficient and reliable frameworks. Following this trend, Datapizza (the startup behind Italy’s tech community) just released an open-source framework for GenAI with Python, called Datapizza
The post Build LLM Agents Faster with Datapizza AI appeared first on Towards Data Science.
Intro Organizations are increasingly investing in AI as these new tools are adopted in everyday operations more and more. This continuous wave of innovation is fueling the demand for more efficient and reliable frameworks. Following this trend, Datapizza (the startup behind Italy’s tech community) just released an open-source framework for GenAI with Python, called Datapizza
The post Build LLM Agents Faster with Datapizza AI appeared first on Towards Data Science. Read More
Bending Spoons’ acquisition of AOL shows the value of legacy platformsAI News The acquisition of a legacy platform like AOL by Bending Spoons shows the latent value of long-standing digital ecosystems. AOL’s 30 million monthly active users represent an enduring brand and a data-rich resource that can be used in AI-driven services. That statement is true only if the data is properly governed and integrated. Such deals
The post Bending Spoons’ acquisition of AOL shows the value of legacy platforms appeared first on AI News.
The acquisition of a legacy platform like AOL by Bending Spoons shows the latent value of long-standing digital ecosystems. AOL’s 30 million monthly active users represent an enduring brand and a data-rich resource that can be used in AI-driven services. That statement is true only if the data is properly governed and integrated. Such deals
The post Bending Spoons’ acquisition of AOL shows the value of legacy platforms appeared first on AI News. Read More
“Systems thinking helps me put the big picture front and center”Towards Data Science Shuai Guo on deep research agents, analytical AI vs LLM-based agents, and systems thinking
The post “Systems thinking helps me put the big picture front and center” appeared first on Towards Data Science.
Shuai Guo on deep research agents, analytical AI vs LLM-based agents, and systems thinking
The post “Systems thinking helps me put the big picture front and center” appeared first on Towards Data Science. Read More
Expanding Stargate to MichiganOpenAI News OpenAI is expanding Stargate to Michigan with a new one-gigawatt campus that strengthens America’s AI infrastructure. The project will create jobs, drive investment, and support economic growth across the Midwest.
OpenAI is expanding Stargate to Michigan with a new one-gigawatt campus that strengthens America’s AI infrastructure. The project will create jobs, drive investment, and support economic growth across the Midwest. Read More
Introducing Aardvark: OpenAI’s agentic security researcherOpenAI News OpenAI introduces Aardvark, an AI-powered security researcher that autonomously finds, validates, and helps fix software vulnerabilities at scale. The system is in private beta—sign up to join early testing.
OpenAI introduces Aardvark, an AI-powered security researcher that autonomously finds, validates, and helps fix software vulnerabilities at scale. The system is in private beta—sign up to join early testing. Read More
How AGI became the most consequential conspiracy theory of our timeMIT Technology Review Are you feeling it? I hear it’s close: two years, five years—maybe next year! And I hear it’s going to change everything: it will cure disease, save the planet, and usher in an age of abundance. It will solve our biggest problems in ways we cannot yet imagine. It will redefine what it means to…
Are you feeling it? I hear it’s close: two years, five years—maybe next year! And I hear it’s going to change everything: it will cure disease, save the planet, and usher in an age of abundance. It will solve our biggest problems in ways we cannot yet imagine. It will redefine what it means to… Read More
Chatbots are surprisingly effective at debunking conspiracy theoriesMIT Technology Review It’s become a truism that facts alone don’t change people’s minds. Perhaps nowhere is this more clear than when it comes to conspiracy theories: Many people believe that you can’t talk conspiracists out of their beliefs. But that’s not necessarily true. It turns out that many conspiracy believers do respond to evidence and arguments—information that…
It’s become a truism that facts alone don’t change people’s minds. Perhaps nowhere is this more clear than when it comes to conspiracy theories: Many people believe that you can’t talk conspiracists out of their beliefs. But that’s not necessarily true. It turns out that many conspiracy believers do respond to evidence and arguments—information that… Read More