Filtered to language models · clear filter
This paper creates a platform to help large language models generate code for data pipelines, which can then be edited and used to automate data processing workflows. Practitioners might care about this because it can help reduce the time and cost of developing and maintaining these pipelines.
This paper investigates how Diffusion Language Models (DLMs) internally represent time and how this representation can be used to modulate the model's behavior. Practitioners might care because understanding how DLMs process time could lead to more controllable and interpretable models.
This paper investigates whether large language models, like Google's Gemma-4-E4B-it, represent scientific concepts and governing physics, and whether this representation affects their answers. Practitioners caring about the accuracy and reliability of language models in scientific domains might find this research valuable.
This paper develops a mathematical framework to analyze the Transformer architecture, using differential geometry to model its core components. Practitioners may care about this work because it provides new insights into the stability and optimization dynamics of Large Language Models.
This paper evaluates language models and deep-research agents at predicting football match outcomes before kickoff, using a dynamic benchmark that can be reused for future leagues. Practitioners can learn from the results to improve their own models' performance in similar tasks.
This paper addresses a common issue in agentic language models where they may over-call tools, leading to suboptimal performance. A practitioner might care about this problem because it can negatively impact the model's ability to provide accurate and efficient responses.