(1/12) Don't fall behind! Stay ahead of the game with March 2023's top NLP papers Curated by @forai_ml
, this list covers the latest advancements in NLP.
Get up to speed with the latest language AI advancements now! Post generated with Cohere. https://
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LLMS
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March 2023’s Top NLP Papers: Latest Language AI Advancements
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Expert Settings and Specialized AI Models in Domain-Specific Applications
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1. Curious if you have some examples of more-focused expert settings. I agree with settings with private data being important for specialized models. But even for things like the medical domain or low-resource NLP, i think general AI models like PaLM and GPT-4 hold the current
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Language Models Training: Autoregressive vs Diffusion Approaches
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Common Q: Can you train language model w diffusion?
Favorite A: read this post (the whole blog is excellent) (Roughly speaking state of the art generative AI is either trained autoregressively or with diffusion. The underlying neural net usually a Transformer.) -
GPT-4 Jailbreaks Reveal Alignment Challenges and Future Risks
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lol i agree the outputs are ridiculous rn, however, that's not really the point jailbreaks show how hard it is to "align" a model even with the amount of work OpenAI has done if they can't get gpt-4 to operate how they want it to rn, then we will have bigger problems later on
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Beyond LLMs: Pursuing Diverse AI Research Areas Over Hype
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Maybe I'm taking the bait, but I find this advice misguided and kind of rude. There are many interesting & worthwhile research areas beyond LLMs. I hope people continue to lead and push forward in other areas, rather than being a follower in the area with the most money and hype
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DataAISummit 2026: 180+ Sessions on LLMs, MLOps, Data Streaming
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The #DataAISummit session catalog is live Choose from over 180 sessions across a variety of tracks, technologies, and industries! Data professionals will share their expertise on topics such as LLMs, data streaming, MLOps and Lakehouse. Join us https://
bit.ly/3KAkywr -
GPT-4 Improved Safety Against Jailbreak Prompts
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GPT-4 is capable and aligned enough to not fall for that directly, that worked on earlier GPT models but does not work anymore Try using that exact verbiage without using the rest of the prompt and you will see it will fail to produce the same responses
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Cost efficiency improvements in large language models
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Cost is definitely important, though i have two thoughts here:
1. Cost is decreasing quickly. E.g., Flan-PaLM-8B (2022) is about as good as GPT-3 175B (2020). So there is a ~10x improvement in just 2 years.
2. For cases where a model with 90% performance costs 10x more than a -
Vector Matching Engine: Efficient Search for GPT-4 Embeddings
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To use GPT-4 with your own data, you need to create embeddings for all your information. The problem: Searching for these embeddings is not easy. We built a Vector Matching Engine. You can use it to search large amounts of vector embeddings efficiently. ↓
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General Models Superior to Task-Specific Models for Language Tasks
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Really surprised to see that Eric Schmidt believes in task-specific models. While I agree that specialized models are good when you have proprietary data, many broad language tasks have been and will be continue to be done best by a general model. A few prominent examples: 1.