Records found: 9
Gemma 3 270M is a compact, 270M-parameter model by Google AI designed for energy-efficient, task-specific fine-tuning and on-device deployment with INT4 QAT support
Anthropic AI proposes a novel method using persona vectors to detect and control personality shifts in large language models, enhancing their reliability and safety.
ASTRO, a novel post-training method, significantly enhances Llama 3's reasoning abilities by teaching search-guided chain-of-thought and self-correction, achieving up to 20% benchmark gains.
Unbabel introduces TOWER+, a unified multilingual large language model that excels in both high-fidelity translation and instruction-following, surpassing existing open-weight models in benchmarks.
New research demonstrates that inference-time prompting can effectively approximate fine-tuned transformer models, offering a resource-efficient approach to NLP tasks without retraining.
New research reveals how integrating in-context learning insights into fine-tuning datasets significantly improves language model generalization on complex reasoning tasks.
The FalseReject dataset helps language models overcome excessive caution by training them to respond appropriately to sensitive yet harmless prompts, enhancing AI usefulness and safety.
Salesforce’s xGen-small offers a compact AI model delivering efficient long-context understanding with reduced costs and strong privacy, transforming enterprise AI workflows.
OpenAI launches Reinforcement Fine-Tuning on the o4-mini model, enabling developers to customize AI reasoning with precision using reinforcement learning techniques.