Extracting coherent and human-understandable themes from large collections of unstructured historical newspaper archives presents significant challenges due to ...
Retrieval-augmented generation (RAG) models rely on retrieved evidence to guide large language model (LLM) generators, yet current systems treat retrieval as a ...
We consider the problem of auditing black-box large language models (LLMs) to ensure they behave reliably when deployed in production settings, particularly in ...
Social media serves as a critical medium in modern politics because it both reflects politicians' ideologies and facilitates communication with younger generati...
Reliable interpretation of multimodal data in dentistry is essential for automated oral healthcare, yet current multimodal large language models (MLLMs) struggl...
Key frame selection in video understanding presents significant challenges. Traditional top-K selection methods, which score frames independently, often fail to...
The growing demand for real-time DNN applications on edge devices necessitates faster inference of increasingly complex models. Although many devices include sp...
Large Language Models (LLMs) exhibit remarkable capabilities in natural language understanding and reasoning, but suffer from hallucination: the generation of f...
Large language models specialized for code (CodeLLMs) have demonstrated remarkable capabilities in generating code snippets, documentation, and test cases. Howe...
The transition from hand-crafted heuristics to data-driven evolutionary algorithms faces a fundamental dilemma: achieving neural plasticity without sacrificing ...
The automated translation of C code to Java code is a notoriously difficult task, fraught with challenges stemming from fundamental paradigm shifts (procedural ...
The technique can help scientists in economics, public health, and other fields understand whether to trust the results of their experiments....
