Data mixing -- determining the ratios of data from different domains -- is a first-order concern for training language models (LMs). While existing mixing metho...

Efficient long-context processing remains a crucial challenge for contemporary large language models (LLMs), especially in resource-constrained environments. So...

AI models have achieved state-of-the-art results in textual reasoning; however, their ability to reason over spatial and relational structures remains a critica...

The prevailing paradigm in large language model (LLM) development is to pretrain a base model, then perform further training to improve performance and model be...

Diffusion language models generate text through iterative refinement, a process that is often computationally inefficient because many tokens reach stability lo...

Misinformation detection is a critical task that can benefit significantly from the integration of external knowledge, much like manual fact-checking. In this w...

Reinforcement learning (RL) based post-training for explicit chain-of-thought (e.g., GRPO) improves the reasoning ability of multimodal large-scale reasoning mo...

Misalignment in Large Language Models (LLMs) refers to the failure to simultaneously satisfy safety, value, and cultural dimensions, leading to behaviors that d...

Large language models (LLMs) demonstrate strong general reasoning and language understanding, yet their performance degrades in domains governed by strict forma...

Large Language Model (LLM) agents have shown promising potential in automating Instructional Systems Design (ISD), a systematic approach to developing education...

Language models have become practical tools for quantum computing education and research, from summarizing technical papers to explaining theoretical concepts a...

The principal goal of the RAG TREC Instrument for Multilingual Evaluation (RAGTIME) track at TREC is to study report generation from multilingual source documen...