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 develops a method to scale up language models by increasing their memory capacity, allowing for better performance and more efficient use of parameters. Practitioners may care about this research as it could lead to more powerful and efficient language models for applications like language translation and text generation.
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 teaches language models to synthesize complete software programs from scratch, which is a challenging task. Practitioners might care because this can improve the models' performance on software engineering tasks.
This paper develops a new method for improving reasoning language models, called β-OPSD, which combines policy optimization and self-distillation to improve stability and performance. Practitioners might care about this method because it provides a more efficient and effective way to improve language model reasoning abilities.
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.