4). A Survey on Data Synthesis and Augmentation for LLMs – provides a comprehensive summary of data generation techniques in the lifecycle of LLMs; includes discussions on data preparation, pre-training, fine-tuning, instruction-tuning, preference alignment, and applications.
PROMPT ENGINEERING
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Top ML Papers of the Week: LongRAG, Granite 3.0, and Beyond
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The Top ML Papers of the Week (Oct 21 – 27): – LongRAG
– Granite 3.0
– Data Synthesis Overview
– Agentic Information Retrieval
– Scalable Watermarking for LLMs
– A Theoretical Understanding of CoT Read on for more: -
Using ChatGPT-4o for stock trading and investment prompts
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I'm shocked why people don’t use ChatGPT-4o for stock trading. Here are 10 prompts to use for stock trading and investing:
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The Rarity of Sane LLM Whisperers for Useful Commentary
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The problem is that to get any useful commentary we'd need to find an LLM Whisperer who is not insane. I'm given to understand that they exist, but they're rare.
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Strange Bimodal Results: The Role of Exact Prompts
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Okay, then what oneshot prompt? We've really got a strange bimodal thing going on where some people report that they can't get any good results, and it probably has something to do with exact prompts!
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Communication Foundation: Why Agents Need Better Frameworks
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Communication is all you need Wrote a blog on how communication is the hardest part of building agents. From this axiom, a bunch of fun hot takes can be derived: Why prompt engineering isn’t going away
Why you need an agent framework
Why we made LangSmith the most user -
Add Custom Quick Actions Beyond Built-in AI Features
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This a very cool add – don't limit yourself to the built in quick actions (change length, change reading level, add emojis) but add your own quick actions! https://t.co/VFHYDzYcdt
— Harrison Chase (@hwchase17) 25 octobre 2024This a very cool add – don't limit yourself to the built in quick actions (change length, change reading level, add emojis) but add your own quick actions!
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Feature Steering vs Prompt Engineering: Unexpected Off-Target Effects
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We also compared feature steering with prompt engineering and found some surprising similarities: for example, prompt engineering also showed unexpected off-target effects.
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Google Adds Custom Prompts to NotebookLM
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WIP : Google is working on the possibility of setting system prompts to Notebooks on NotebookLM Users will be able to specify a "custom prompt" and a style guide. Current placeholders suggest that it may be a way to specify a "persona" of the "guest"?
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Retrieving the Right Data from Multiple Sources in LLM Applications
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A major challenge in LLM applications is retrieving the right data from multiple sources based on the prompt. Here’s an informative guide on this.