[HN Gopher] Grounding AI in reality with a little help from Data...
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Grounding AI in reality with a little help from Data Commons
Author : throwaway888abc
Score : 89 points
Date : 2024-09-13 20:41 UTC (1 days ago)
(HTM) web link (research.google)
(TXT) w3m dump (research.google)
| westurner wrote:
| > _Retrieval Interleaved Generation (RIG)_ : _This approach fine-
| tunes Gemma 2 to identify statistics within its responses and
| annotate them with a call to Data Commons, including a relevant
| query and the model 's initial answer for comparison. Think of it
| as the model double-checking its work against a trusted source._
|
| > [...] _Trade-offs of the RAG approach_ : [...] _In addition,
| the effectiveness of grounding depends on the quality of the
| generated queries to Data Commons._
| Groxx wrote:
| Gotta say, this kinda feels like "giving it correct data didn't
| work, so _what if we did that twice?_ ".
|
| Like, two layers of duct tape are better than one.
|
| Seems reasonable and I can believe it helps, just also seems
| like it doesn't do much to improve confidence in the system as
| a whole. Particularly since they're basically asking it to find
| things worth checking, then have it write the checking query,
| and have it interpret the results. When it's the thing that
| screwed it up in the first place.
| vineyardmike wrote:
| eh, I think this is pretty reasonable and not a "hack". It
| matches what we do as people. I think there probably needs to
| be research into how to tell it when it doesn't know
| something, however.
|
| I think if you remember that LLMs are not databases, but they
| do contain a super lossy-compressed version of (it's
| training) knowledge, this feels less like a hack. If you ask
| someone, "who won the World Cup in 2000?", they may say "I
| think it was X, but let me google it first". That person
| isn't screwed up, using tools isn't a failure.
|
| If the context is a work setting, or somewhere that is data-
| centric, it totally makes sense to check it. Like a Chat Bot
| for a store, or company that is helping someone troubleshoot
| or research. Anything where it really obvious answers that
| are easy to learn from volumes of data ("what company makes
| the corolla?"), probably don't need fact checking as often,
| but why not have the system check its work?
|
| Meanwhile, programming, writing prose, etc are not things you
| generally fact-check mid-way, and are things that can be
| "learned" well from statistical volume. Most programmers can
| get "pretty good" syntax on first try, and any dedicated
| syntax tool will get to basically 100%, and the same makes
| sense for an LLM.
| exe34 wrote:
| I think a better way would be to just use it as a text to
| search interface in the first place?
| westurner wrote:
| This is similar to the difference between data dredging and
| scientific hypothesis testing.
|
| 'But what bias did we infer with [LLM knowledgebase]
| background research prior to formulating a hypothesis, and
| who measured?'
|
| There are various methods of Inference: Inductive,
| Deductive, and Abductive
|
| What are the limits of Null Hypothesis methods of
| scientific inquiry?
| taneq wrote:
| > Like, two layers of duct tape are better than one.
|
| Uh... they _are_? They 're not better than a properly specc'd
| fastener installed at appropriately engineered mounting
| points but still better than one layer of duct tape, let
| alone none.
| zrank wrote:
| So, essentially, you can Google the answer in the first place,
| click on your own trusted sources and compare them. All without
| using a language model.
|
| Or buy an encyclopedia ...
| gaogao wrote:
| There's an encylopedia you can't buy called Wikipedia. It also
| has its own structured auxillary database, WikiData. I wrote up
| roughly what this article is doing about a year and a half ago
| - https://friend.computer/jekyll/update/2023/04/30/wikidata-
| ll...
| fassssst wrote:
| Google uses language models.
| mark_l_watson wrote:
| I was fortunate to be hired as a contractor 11 years ago to work
| on an internal Google Knowledge Graph application. Google is just
| one of many large companies to utilize one huge graph to localize
| information from many sources.
|
| I bought into TBL's Semantic Web ideas (and I cover 'lower case'
| semantic web topics in a few of my books). I think it is a shame
| that publicly accessible world knowledge graphs never really took
| off, but at least Google's Data Commons is available for free for
| non-commercial, educational, and research uses.
| ramraj07 wrote:
| Could you provide any insight into why KGs work well in such
| places? Like a contrived example maybe?
| mark_l_watson wrote:
| Well, from public information: Meta has a huge social graph
| that helps support their social media businesses, and Google
| has a wealth of real world knowledge. These graphs are, I
| think, optimized for super fast 1 millisecond level query
| latencies, and not a rich query language (for example, not
| something like SPARQL).
|
| I have just been looking at the Data Commons data sets, and I
| think I will add a fun example to my live Common Lisp eBook
| (and/or my Racket live eBook).
| zozbot234 wrote:
| > publicly accessible world knowledge graphs never took off
|
| Huh? What's wrong with Wikidata and the Linked Open Data cloud?
| These seem quite real to me.
| openrisk wrote:
| The public / non-government sector (especially in Europe) has
| been quite keen for decades in (linked) open data, knowledge
| graphs and associated technologies. Yet applications, tools and,
| ultimately usability, awareness and adoption have been lagging.
|
| In this sense this project offers a remarkable, albeit implicit,
| endorsement of that broader open data space, as it comes from a
| major private sector entity _and_ links with the hot LLM
| technology of the day.
|
| At high level though, this design seems to violate the "bitter
| lesson" gospel [1].
|
| > 1) AI researchers have often tried to build knowledge into
| their agents
|
| Which is at the same refreshing (as there is something very
| incomplete and self-defeating in the "scaling" hypothesis) and
| hints at the difficulties of meaningfully integrating very
| heterogeneous sources and representations of information.
|
| [1] http://www.incompleteideas.net/IncIdeas/BitterLesson.html
| amelius wrote:
| Information isn't the only problem. Another problem is the
| correct application of logic.
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