231–240 of 491 posts
Is there something specifically with Kimi that's better here? As far as I know Kimi pricing is about the same as Sonnet 5 -- what happens if you use that model and Fable instead? Or Grok 4.5 which is even cheaper?
One benefit of an open source one is that you can, as a large corporation, run it "locally" within your own data center. Even fine tune it.
That's incredible. Hope the chinese keep it up!
I will accept a 5% drop in benchmarks for a model that talks to me like a human.
Maybe it is something that is easy for it to read and write, but definitely not for humans.
For example,
Skim once now; refer back while reading Part II. \*Every bold technical term in Part II is defined here\* — treat these as a dictionary, not a reading assignment. The first table covers the vocabulary of the *deck*; the three that follow cover the *methodology* vocabulary introduced in Part II, grouped so you can find a term fast: \*(A)\* the logic of rules, \*(B)\* the neural-network & training machinery, \*(C)\* the method-design ideas.
JMRL is the paper the thesis instantiates, so this is the one to know cold. Its pitch is \*end-to-end\*: earlier rule methods (LogicRE, MILR) bolt a rule learner *onto a frozen* extractor in a pipeline and suffer \*error propagation\*; JMRL trains the rule module *jointly* with the extractor.
**Identity.** Conformal prediction for NER producing **finite-sample-valid prediction sets** at two granularities: **full-sequence** sets over entire label sequences (capturing contextual dependence — "if Sarah=PER then NYC likely LOC") and **subsequence-level** (per-span, **class-conditional**) sets; an **integrated** method filters full-sequence predictions with entity-level sets. Adds **covariate-stratified calibration** by **sentence length and language** for valid multilingual coverage, and studies **combined nonconformity scores** (Naive / Conditional / RAPS). **Read in this order.** Abstract → §1 contributions (full-sequence / subsequence / integrated / **covariate (length + language) calibration** / combined scores) → §2 CP recap (inductive split-CP) → §3 NER formulation (IOB2, CRF) → the subsequence / entity-level set construction + class-conditional coverage → the language-stratified calibration results. **Why it matters here.** The **span-level construction** for **Topic 11**'s per-triple score, and — crucially — its **language-stratified calibration is exactly the EN↔zh case**: it shows how to keep conformal coverage valid across languages of differing length/script. Complements PASC (pipeline-level joint coverage) with the *NER-internal* set construction. **Caveat.** A heavy statistics paper (44 pp., *Annals of Applied Statistics* submission) with CRF-based NER; the project needs only the **inductive split-CP + subsequence/entity-level sets + language-stratified calibration**, not the full-sequence machinery (likely overkill for triple-confidence). Assumes exchangeability — borderline under the EN→zh shift, which is precisely why the PASC/ConformalNER *shift* analyses matter.
Earlier quoted context omitted.
They distilled Fable, in the couple weeks it was available?
The idea that the Chinese labs cannot make progress except by copying superior American products is just prejudice against the former and exceptionalism of the latter at play. Even the OpenAI top brass have admitted otherwise [1]. China is an equal match in every respect, and we'd better admit this to ourselves sooner rather than later so as to see the game clearly. [1] https://xcancel.com/deanwball/status/2078133895…
I will accept a 5% drop in benchmarks for a model that talks to me like a human.
Yeah. I've found that Opus by default outputs something I call "Claude-lang." It consists of oversimplified, grammatically incomplete sentences that I find painful to read. Maybe it is something that is easy for it to read and write, but definitely not for humans. For example, Skim once now; refer back while reading Part II. \*Every bold technical term in Part II is defined here\* — treat these as a dictionary, not a…
Is there something specifically with Kimi that's better here? As far as I know Kimi pricing is about the same as Sonnet 5 -- what happens if you use that model and Fable instead? Or Grok 4.5 which is even cheaper?
One benefit of an open source one is that you can, as a large corporation, run it "locally" within your own data center. Even fine tune it.
You can more-or-less approximate what your build out spend for a data center hosting database software is going to be. Your book of business will require a given amount of revenue to pay it off, but once that's known, it's off to the races.
With AI, things are moving so fast and new business models are being tried all the time. You would be competing with some of the wealthiest companies in the world for data center hardware capable of hosting these models in a usable state.
We've gone with Claude somehow hosted through GCP Vertex AI where I'm at.
Earlier quoted context omitted.
For me, the only utility OpenRouter gives me is billing consolidation - I don't really need the routing capabilities because I use Bifrost for that. As a router, it's not very feature rich. For example I restricted the available models to the ones I want to use however the `/models` endpoint still lists all the models, making my LLM client list the 200+ models available on the service (even though they will throw an…
Did you look at LiteLLM at all? It seems fine but Bifrost looks interesting too.