Jeremi Joslin

The Salesforce AI team just solved LLM text summarization They released a new prompting technique called Chain of Density (CoD). Researchers realized that there's a delicate balance between the amount of details and core ideas in a summary. They then created a prompt that allows you to increase the density of any summary, you decide when to stop. The team even did a human preference study and showed that this new version was preferred to vanilla GPT. Paper: arxiv.org/pdf/2309.04269.pdf You can copy the prompt below: --- Article: {{ ARTICLE }} You will generate increasingly concise, entity-dense summaries of the above article. Repeat the following 2 steps 5 times. Step 1. Identify 1-3 informative entities (";" delimited) from the article which are missing from the previously generated summary. Step 2. Write a new, denser summary of identical length which covers every entity and detail from the previous summary plus the missing entities. A missing entity is: - relevant to the main story, - specific yet concise (5 words or fewer), - novel (not in the previous summary), - faithful (present in the article), - anywhere (can be located anywhere in the article). Guidelines: - The first summary should be long (4-5 sentences, ~80 words) yet highly non-specific, containing little information beyond the entities marked as missing. Use overly verbose language and fillers (e.g., "this article discusses") to reach ~80 words. - Make every word count: rewrite the previous summary to improve flow and make space for additional entities. - Make space with fusion, compression, and removal of uninformative phrases like "the article discusses". - The summaries should become highly dense and concise yet self-contained, i.e., easily understood without the article. - Missing entities can appear anywhere in the new summary. - Never drop entities from the previous summary. If space cannot be made, add fewer new entities. Remember, use the exact same number of words for each summary. Answer in JSON. The JSON should be a list (length 5) of dictionaries whose keys are "Missing_Entities" and "Denser_Summary". ```

Meta just announced that Code Llama was now free for both research and commercial. This might the strongest competitor to ChatGPT: ▸ Can generate, explain, and debug your code ▸ Handles input 100,000 tokens ▸ Free for research + commercial use ▸ Outperforms most open models ▸ Comes in 7B, 13B, and 34B ▸ Supports Python, C++, Java, PHP, Typescript (Javascript), C#, and Bash Available in: ▸ Foundation base models (Code Llama) ▸ Python specializations (Code Llama - Python), ▸ Instruction-following models (Code Llama - Instruct) github.com/facebookresearch/…