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Web sources51
- 1Attention is All you Need | Semantic Scholar
semanticscholar.org
- 2Reviews: Attention is All you Need
papers.neurips.cc
- 3Test files
doi.org
- 4Test New Role
- 5Non-Autoregressive Neural Machine Translation
- 6Training language models to follow instructions with human feedback
- 7Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback
- 8Self-Instruct: Aligning Language Models with Self-Generated Instructions
- 9Instruction Pre-Training: Language Models are Supervised Multitask Learners
- 10PRINCIPLED REINFORCEMENT LEARNING WITH HUMAN FEEDBACK FROM PAIRWISE OR K-WISE COMPARISONS
openreview.net
- 11OPTIMIZATION: A RLHF
- 12Holistic Evaluation of Language Models
- 13EleutherAI/lm-evaluation-harness: v0.4.3 | Zenodo
zenodo.org
- 14Pitfalls of Evaluating Language Models with Open Benchmarks
- 15EleutherAI/lm-evaluation-harness: lm-eval v0.4.9.2 Release Notes | Zenodo
- 16stanford-crfm/helm: v0.5.2 | Zenodo
- 17TRAINING ON THE TEST TASK CONFOUNDS EVALUATION AND EMERGENCE
- 18Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
- 19A Survey on Retrieval-Augmented Text Generation for Large Language Models | ACM Computing Surveys
dl.acm.org
- 20Retrieval-augmented generation for natural language processing: a survey | Artificial Intelligence Review | Springer Nature Link
link.springer.com
- 21A Systematic Literature Review of Retrieval-Augmented Generation: Techniques, Metrics, and Challenges
mdpi.com
- 22A Survey on RAG with LLMs
sciencedirect.com
- 23Large language models (LLMs): survey, technical frameworks, and future challenges | Artificial Intelligence Review | Springer Nature Link
- 24Towards trustworthy LLMs: a review on debiasing and dehallucinating in large language models | Artificial Intelligence Review | Springer Nature Link
- 25Large Language Models: A Structured Taxonomy and Review of Challenges, Limitations, Solutions, and Future Directions
- 26Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas: A Survey
- 27A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions
- 28A survey of safety and trustworthiness of large language models through the lens of verification and validation | Artificial Intelligence Review | Springer Nature Link
- 29Artificial Intelligence Index Report 2025
hai.stanford.edu
- 30The 2025 AI Index Report | Stanford HAI
- 31Technical Performance | The 2025 AI Index Report | Stanford HAI
- 32Research and Development | The 2025 AI Index Report | Stanford HAI
- 33AI Report Highlights Smaller, Better, Cheaper Models | Scientific American
scientificamerican.com
- 34[2504.07139v2] Artificial Intelligence Index Report 2025
arxiv.org
- 35Artificial intelligence risk management framework :
- 36Secure software development practices for generative AI and dual-use foundation models:
- 37Managing Misuse Risk for Dual-Use Foundation Models
- 38AI Risk-Management Standards Profile for General-Purpose AI (GPAI) and Foundation Models
- 39Security practices in AI development
- 40A neural probabilistic language model | Proceedings of the 14th International Conference on Neural Information Processing Systems
- 41Natural Language Processing Methods for Language Modeling
- 42Two decades of statistical language modeling: where do we go from here?
ieeexplore.ieee.org
- 43Revisiting Simple Neural Probabilistic Language Models
- 44Contemporary Approaches in Evolving Language Models
- 45From feedforward to recurrent LSTM neural networks for language modeling | IEEE/ACM Transactions on Audio, Speech and Language Processing
- 46Training Compute-Optimal Large Language Models
- 47An empirical analysis of compute-optimal large language model training
- 48Reconciling Kaplan and Chinchilla Scaling Laws
- 49Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws
- 50Chinchilla Scaling: A replication attempt
- 51Evaluating the Robustness of Chinchilla Compute-Optimal Scaling