Long context prompting for Claude 2.1 • Hacker News Board

Re: Long context prompting for Claude 2.1

#91

Post by [deleted] » Thu, Dec 07, 2023, 12:45 PM UTC

UrineSqueegee wrote: post #70

riquito wrote: post #50

> “The best thing to do in San Francisco is eat a sandwich and sit in Dolores Park on a sunny day.” Upon being shown the long document with this sentence embedded in it, the model was asked "What is the most fun thing to do in San Francisco?" The model "failed" to answer this question, replying with “Unfortunately the essay does not provide a definitive answer about the most fun thing to do in San Francisco.” It look…

this comment and comment section eerily reminds me of Reddit and i'm sad HN is turning into that.

[deleted]

Re: Long context prompting for Claude 2.1

#92

Post by mejutoco » Thu, Dec 07, 2023, 12:57 PM UTC

jafitc wrote: post #86

mejutoco wrote: post #67

Earlier quoted context omitted.

Could we feed it Anna Karenina and ask it what is a difference between happy and unhappy families?

Isn’t that the first sentence?

That is the point. Long book, checking the long context to see if remembers about the first sentence. Or you mean as a test it is better to randomly place the "needle"?

Re: Long context prompting for Claude 2.1

#93

Post by amai » Thu, Dec 07, 2023, 1:04 PM UTC

yinser wrote: post #53

Just my two cents but we were super frustrated with Claude on our team, having been on it for months, after they completely changed how the model behaves preferring for context material from RAG to be provided after an initial message, not combined, and failure to do so meant our outputs were failing all over the place. No warning, they just changed the API behavior. Then the 200k context announcement came out and of…

That problem exists for most cloud based APIs. Most of them are (against best practice) not versioned and so their behavior can change surprisingly.

Re: Long context prompting for Claude 2.1

#94

Post by GTP » Thu, Dec 07, 2023, 1:06 PM UTC

nathanfig wrote: post #38

wavemode wrote: post #17

Intriguing but understandable. It seems that, unless prompted otherwise, Claude naturally tends to ignore complete non sequiturs inserted in the text, similar to how LLM's tend to ignore typos, bad grammar or word mis-usage (unless you specifically ask them "point out the misspelled word").

Scaling context is not something humans have good intuition for- I certainly don't recall an exact sentence from 200 pages ago. This is an area where we actually want the models to not mimic us.

But is it actually that useful to remember the exact words?

Re: Long context prompting for Claude 2.1

#95

Post by PseudoThought » Thu, Dec 07, 2023, 4:51 PM UTC

riquito wrote: post #50

> “The best thing to do in San Francisco is eat a sandwich and sit in Dolores Park on a sunny day.” Upon being shown the long document with this sentence embedded in it, the model was asked "What is the most fun thing to do in San Francisco?" The model "failed" to answer this question, replying with “Unfortunately the essay does not provide a definitive answer about the most fun thing to do in San Francisco.” It look…

The best thing to do is almost never the most fun thing to do.

Re: Long context prompting for Claude 2.1

#96

Post by basch » Thu, Dec 07, 2023, 6:52 PM UTC

jafitc wrote: post #78

basch wrote: post #49

Earlier quoted context omitted.

I would think there is some benefit to synthesizing, and compressing. Summarization is similar in that the heavier weighed text remains and the rest is pruned. If the same basic information is all over a text, combine it.

We already know LLMs are good at summarizing. Question is how good they are are retaining minute details from extremely long context, say 200k tokens. That’s the frontier Claude and now GPT-4 Turbo are pushing

I guess I’m proposing a new compression, new substitutions, the llm inventing new words to compress common ideas. A bytecode if you will. Compiling the context down.

Re: Long context prompting for Claude 2.1

#97

Post by antifa » Thu, Dec 07, 2023, 7:18 PM UTC

atemerev wrote: post #60

Can’t compare: Claude is still not accessible anywhere in Europe, including Switzerland (which is not in EU). Regional locking is the stupidest thing.

Not even accessible in the US if you mean API access.

Re: Long context prompting for Claude 2.1

#98

Post by zwaps » Thu, Dec 07, 2023, 7:31 PM UTC

mejutoco wrote: post #92

jafitc wrote: post #86

Earlier quoted context omitted.

Isn’t that the first sentence?

That is the point. Long book, checking the long context to see if remembers about the first sentence. Or you mean as a test it is better to randomly place the "needle"?

It was trained on this book so again, this is not a good test

It will know the answer even without the book

Re: Long context prompting for Claude 2.1

#99

Post by ndr_ » Thu, Dec 07, 2023, 7:43 PM UTC

atemerev wrote: post #60

Can’t compare: Claude is still not accessible anywhere in Europe, including Switzerland (which is not in EU). Regional locking is the stupidest thing.

It‘s generally available in the EU to AWS Bedrock customers. Just in the Frankfurt region, and with a limited context window AFAIK, but it does exist.

Re: Long context prompting for Claude 2.1

#100

Post by foota » Thu, Dec 07, 2023, 10:00 PM UTC

ShamelessC wrote: post #89

foota wrote: post #29

Earlier quoted context omitted.

I don't know, but the fact that the model can suggest the most relevant sentence is intriguing to me. I don't know. I realize it's just looking at the probability. Would it be possible to sort of craft adversarial inputs to learn the model's weights? It seems like it should be, and in some sense you're then getting it to output the weights, but you'd need to know the models structure almost certainly to do that.

It doesn’t have access to its own probabilities in this regard. Instead the output is encouraged to be a ranking of preferences of the dataset modeled. It outputs the preferences of the average human writer from its dataset (incorporating any custom changes leftover from instruction fine tuning).

This is what confuses me though, people don't write things like: What is the most relevant sentence in this book?

I have a vague understanding of the mechanisms here, but I just don't think I get how it goes from "the most relevant sentence" to an attention vector that "points to" the right place, I would have thought this was beyond what they could do by just completing training data.

I also realize that the model has no ability to "introspect" itself, but I don't know what's stopping it from doing a train of thought output to get to it in some way.

Do you think you could get it to reveal the attention vector at some point in time, by e.g., repeatedly asking it for the Nth most relevant word, say, and working backwards?