Quality follows upgrading
All Articles
arXiv:2602.20169v1 Announce Type: cross Abstract: This Article examines the circumstances in which AI-generated outputs remain linked to their creators and the points at which they …
arXiv:2602.20164v1 Announce Type: new Abstract: Knowledge distillation offers a transformative pathway to developing powerful, yet efficient, small language models (SLMs) suitable for resource-constrained environments. In …
arXiv:2602.20294v1 Announce Type: new Abstract: Simulating real personalities with large language models requires grounding generation in authentic personal data. Existing evaluation approaches rely on demographic …
arXiv:2602.20300v1 Announce Type: new Abstract: Large Language Model (LLM) hallucinations are usually treated as defects of the model or its decoding strategy. Drawing on classical …
arXiv:2602.20332v1 Announce Type: new Abstract: Advanced reasoning capabilities in Large Language Models (LLMs) have led to more frequent hallucinations; yet most mitigation work focuses on …
arXiv:2602.20336v1 Announce Type: new Abstract: This article presents an evaluation of several machine learning methods applied to automated text classification, alongside the design of a …
arXiv:2602.20372v1 Announce Type: new Abstract: Recent research argues that exact recursive numeral systems optimize communicative efficiency by balancing a tradeoff between the size of the …
arXiv:2602.20379v1 Announce Type: new Abstract: Enterprise Retrieval-Augmented Generation (RAG) assistants operate in multi-turn, case-based workflows such as technical support and IT operations, where evaluation must …
arXiv:2602.20433v1 Announce Type: new Abstract: Geometric properties of Transformer weights, particularly the unembedding matrix, have been widely useful in language model interpretability research. Yet, their …
arXiv:2602.20513v1 Announce Type: new Abstract: As large language models (LLMs) continue to improve at completing discrete tasks, they are being integrated into increasingly complex and …
arXiv:2602.20528v1 Announce Type: new Abstract: The Stop-Think-AutoRegress Language Diffusion Model (STAR-LDM) integrates latent diffusion planning with autoregressive generation. Unlike conventional autoregressive language models limited to …
arXiv:2602.20580v1 Announce Type: new Abstract: Modern language models (LM) are trained on large scrapes of the Web, containing millions of personal information (PI) instances, many …