1. Credit for this example is due to X user Dean Buono (@deanbuono ^(https://www.blogquicker.com/goto/https://x.com/deanbuono?lang=en)); credit for subsequent examples in this section is due to Colin Fraser (@colin_fraser ^(https://www.blogquicker.com/goto/https://x.com/colin_fraser?lang=en)).
2. L. Berglund, M. Tong, M. Kaufmann, et al., “The Reversal Curse: LLMs Trained on ‘A Is B’ Fail to Learn ‘B Is A,’ ^(https://www.blogquicker.com/goto/https://arxiv.org/abs/2309.12288)” arXiv, submitted Sept. 21, 2023, https://arxiv.org.
3. E. Mollick, “Google’s Gemini Advanced: Tasting Notes and Implications ^(https://www.blogquicker.com/goto/https://www.oneusefulthing.org/p/google-gemini-advanced-tasting-notes ),” One Useful Thing, Feb. 8, 2024, www.oneusefulthing.org.
4. “Retrival-Augmented Generation ^(https://www.blogquicker.com/goto/https://en.wikipedia.org/wiki/Retrieval-augmented_generation),” Wikipedia, accessed Oct. 22, 2024, https://en.wikipedia.org.
5. P. Béchard and O.M. Ayala, “Reducing Hallucination in Structured Outputs via Retrieval-Augmented Generation ^(https://www.blogquicker.com/goto/https://arxiv.org/abs/2404.08189v1),” arXiv, submitted April 12, 2024, https://arxiv.org.
6. “Industrial-Strength LLM ^(https://www.blogquicker.com/goto/https://www.deeplearning.ai/the-batch/anthropic-teamed-up-with-south-koreas-largest-mobile-phone-provider/),” The Batch, Aug. 30, 2023, www.deeplearning.ai.
7. X. Xu, M. Li, C. Tao, et al., “A Survey on Knowledge Distillation of Large Language Models ^(https://www.blogquicker.com/goto/https://arxiv.org/abs/2402.13116),” arXiv, submitted Feb. 20, 2024, https://arxiv.org.
8. S. Mukherjee, A. Mitra, G. Jawahar, et al., “Orca: Progressive Learning From Complex Explanation Traces of GPT-4 ^(https://www.blogquicker.com/goto/http://arxiv.org/abs/2306.02707),” arXiv, submitted June 5, 2023, https://arxiv.org.
9. E. Brynjolfsson, T. Mitchell, and D. Rock, “What Can Machines Learn, and What Does It Mean for Occupations and the Economy? ^(https://www.blogquicker.com/goto/https://doi.org/10.1257/pandp.20181019)” AEA Papers and Proceedings 108 (May 2018): 43-47.
10. E. Yan, B. Bischof, C. Frye, et al., “What We Learned From a Year of Building With LLMs (Part 1) ^(https://www.blogquicker.com/goto/https://www.oreilly.com/radar/what-we-learned-from-a-year-of-building-with-llms-part-i/),” O’Reilly, May 28, 2024, www.oreilly.com.
11. J. Wei, X. Wang, D. Schuurmans, et al., “Chain-of-Thought Prompting Elicits Reasoning in Large Language Models ^(https://www.blogquicker.com/goto/https://arxiv.org/abs/2201.11903),” arXiv, submitted Jan. 8, 2022, https://arxiv.org.
12. Wei et al., “Chain-of-Thought Prompting.”
13. H.W. Chung, L. Hou, S. Longpre, et al. “Scaling Instruction-Finetuned Language Models ^(https://www.blogquicker.com/goto/https://doi.org/10.48550/arXiv.2210.11416),” preprint, arXiv, revised Dec. 6, 2022, https://arxiv.org.
14. S. Ranger, “Most Developers Will Soon Use an AI Pair Programmer — but the Benefits Aren’t Black and White ^(https://www.blogquicker.com/goto/https://www.itpro.com/technology/artificial-intelligence/most-developers-will-soon-use-an-ai-pair-programmer-but-the-benefits-arent-black-and-white),” ITPro, April 16, 2024, www.itpro.com.
15. H. Hamel, “Your AI Product Needs Evals ^(https://www.blogquicker.com/goto/https://hamel.dev/blog/posts/evals/),” Husain Hamel (blog), https://hamel.dev; E. Yan, “Task-Specific LLM Evals That Do & Don’t Work ^(https://www.blogquicker.com/goto/https://eugeneyan.com/writing/evals/),” Eugene Yan (blog), https://eugeneyan.com; and L. Zheng, W.-L. Chiang, Ying Sheng, et al., “Judging LLM-as-a-Judge With MT-Bench and Chatbot Arena ^(https://www.blogquicker.com/goto/https://arxiv.org/abs/2306.05685),” arXiv, submitted June 9, 2023, https://arxiv.org.
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