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Frustratingly Easy Data Augmentation for Low-Resource ASR
arXiv:2509.15373v2 Announce Type: replace Abstract: This paper introduces three self-contained data augmentation methods for low-resource...
FrugalRAG: Learning to retrieve and reason for multi-hop QA
arXiv:2507.07634v2 Announce Type: replace Abstract: We consider the problem of answering complex questions, given access...
From XAI to Stories: A Factorial Study of LLM-Generated Explanation Quality
arXiv:2601.02224v2 Announce Type: replace Abstract: Explainable AI (XAI) methods like SHAP and LIME produce numerical...
From Text to Tables: Feature Engineering with LLMs for Tabular Data
While large language models (LLMs) are typically used for conversational purposes in use cases that...
From terabytes to insights: Real-world AI obervability architecture
GUEST: Consider maintaining and developing an e-commerce platform that processes millions of transactions every minute...
From Surveys to Narratives: Rethinking Cultural Value Adaptation in LLMs
arXiv:2505.16408v2 Announce Type: replace Abstract: Adapting cultural values in Large Language Models (LLMs) presents significant...
From Roots to Rewards: Dynamic Tree Reasoning with Reinforcement Learning
arXiv:2507.13142v3 Announce Type: replace-cross Abstract: Modern language models address complex questions through chain-of-thought (CoT) reasoning...
From RLHF to Direct Alignment: A Theoretical Unification of Preference Learning for Large Language Models
arXiv:2601.06108v1 Announce Type: cross Abstract: Aligning large language models (LLMs) with human preferences has become...
From prompt chaos to clarity: How to build a robust AI orchestration layer
Choosing orchestration frameworks can be overwhelming, but some experts believe there are best practices to...
From Pretraining to Post-Training: Why Language Models Hallucinate and How Evaluation Methods Reinforce the Problem
Large language models (LLMs) very often generate “hallucinations”—confident yet incorrect outputs that appear plausible. Despite...
From Perception to Action: The Role of World Models in Embodied AI Systems
Introduction to Embodied AI Agents Embodied AI agents are systems that exist in physical or...
From Interpretability to Performance: Optimizing Retrieval Heads for Long-Context Language Models
arXiv:2601.11020v1 Announce Type: new Abstract: Advances in mechanistic interpretability have identified special attention heads, known...
