3 ms·

As far as I can tell, the original chain of density paper doesn’t iteratively prompt. The steps of the chain are done in the generated text of a single prompt.

Terretta 3y ago
The prompt: 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. Although this appears to refer to a "previous" summary, from a previous run, would would appear to suggest it is run anew for each iteration, in fact it generates an "Initial Summary:" on the first run, and generates a Step 1 and Step 2. For me though, it stops there. I can, however, simply say: repeat again And it does another Step 1 and Step 2, and stops. I will note that the third iteration, on an article such as ... https://www.reuters.com/technology/cybersecurity/payments-app-zelle-begins-refunds-imposter-scams-after-washington-pressure-2023-11-13/ https://www.reuters.com/technology/cybersecurity/payments-ap... ... is indeed increasingly superior each iteration. By the third iteration (the fourth summary), it did seem ideal. The fourth iteration and fifth summary added entities that feel extraneous.
- deepsquirrelnet 3y ago
  The prompt in the original article says: > Answer in JSON. The JSON should be a list (length 5) of dictionaries whose keys are “Missing_Entities” and “Denser_Summary” Also I think it doesn’t make sense to write in the prompt for gpt to iterate if it is not doing the iteration. There is not templating of the step number or recursive summary injection in the sample prompt either.
  - Terretta 3y ago
    Absolutely correct, I glazed over the continuation. So, for the record: 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: descriptive yet concise (5 words or fewer). - Novel: not in the previous summary. - Faithful: present in the Article. - Anywhere: 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: re-write 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, e.g., 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". And yes, the answer as a JSON list length 5 causes 5 summaries to get spit out! However, it's not fully clear to me that it's considering the prior summaries on the later summaries in a good/useful way. The expressly iterated results I get are superior to the inline list of results. "More research is needed." -- https://www.explainxkcd.com/wiki/index.php/2268:_Further_Research_is_Needed https://www.explainxkcd.com/wiki/index.php/2268:_Further_Res...
    - deepsquirrelnet 3y ago
      With causal masking and autoregressive token generation, it’s not clear to me that it is inherently different. My original expectation was the same as the way instructor software implemented it. But I found the prompt in the article confusing toward that perspective. I’m sure it can work either way, but it should be a lot more performant (and less expensive) as a single pass.

Terretta 3y ago

The prompt: 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. Although this appears to refer to a "previous" summary, from a previous run, would would appear to suggest it is run anew for each iteration, in fact it generates an "Initial Summary:" on the first run, and generates a Step 1 and Step 2. For me though, it stops there. I can, however, simply say: repeat again And it does another Step 1 and Step 2, and stops. I will note that the third iteration, on an article such as ... https://www.reuters.com/technology/cybersecurity/payments-app-zelle-begins-refunds-imposter-scams-after-washington-pressure-2023-11-13/ https://www.reuters.com/technology/cybersecurity/payments-ap... ... is indeed increasingly superior each iteration. By the third iteration (the fourth summary), it did seem ideal. The fourth iteration and fifth summary added entities that feel extraneous.

deepsquirrelnet 3y ago
The prompt in the original article says: > Answer in JSON. The JSON should be a list (length 5) of dictionaries whose keys are “Missing_Entities” and “Denser_Summary” Also I think it doesn’t make sense to write in the prompt for gpt to iterate if it is not doing the iteration. There is not templating of the step number or recursive summary injection in the sample prompt either.
- Terretta 3y ago
  Absolutely correct, I glazed over the continuation. So, for the record: 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: descriptive yet concise (5 words or fewer). - Novel: not in the previous summary. - Faithful: present in the Article. - Anywhere: 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: re-write 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, e.g., 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". And yes, the answer as a JSON list length 5 causes 5 summaries to get spit out! However, it's not fully clear to me that it's considering the prior summaries on the later summaries in a good/useful way. The expressly iterated results I get are superior to the inline list of results. "More research is needed." -- https://www.explainxkcd.com/wiki/index.php/2268:_Further_Research_is_Needed https://www.explainxkcd.com/wiki/index.php/2268:_Further_Res...
  - deepsquirrelnet 3y ago
    With causal masking and autoregressive token generation, it’s not clear to me that it is inherently different. My original expectation was the same as the way instructor software implemented it. But I found the prompt in the article confusing toward that perspective. I’m sure it can work either way, but it should be a lot more performant (and less expensive) as a single pass.