Every argument clarity score on this site is built from rows on this page. Each
question and answer was assessed with names hidden, the host's own answers included, on
four things from 1 to 5:
directness (does it answer the question asked), coherence (do the ideas follow),
precision (concrete details and clear references), compression (says a lot per word). The weighted
mix (30/30/25/15) is the exchange score. A person's published score averages their exchange
scores on raw tape only, at least 8 of them, shrunk toward the cohort mean.
Full method →
Vishal Misra no published score: only 2 usable exchanges on raw tape, and a fair score needs 8+ record → ← everyone
Every exchange below was scored with names hidden, four dimensions each from 1 to 5.
An exchange's score is 0.30·directness + 0.30·coherence + 0.25·precision + 0.15·compression.
The published score averages the raw tape exchange scores and shrinks small samples toward the
cohort mean, so five great answers can't beat twenty good ones. Produced feed rows count only
toward coarse estimates, never toward a full score.
Answered raw tape
D 5 · C 5 · P 4 · Cm 4 4.60
Q And, and so maybe reflecting back since, since the release of GPT-III, what has most surprised you about how LLMs have, have developed?
A So what has most surprised me? The pace of development. So GPT-III was, you know, It was a nice pilot trick and you had to jump through hoops to get it to do something useful. But starting with the, you know, chat GPT was an advance over GPT three. And then you had all these things like chain of thought instruction, following GPT four really made it polished. And, uh, you know, the pace of development has really surprised me. Now, you know, when I started working with GPT three, I could sort of see what its limitations were, what I could make it do, what I couldn't make it do, but I never thought of it as, you know, what it, what these LLFs have become for me now and what, what have become from millions of people around the world. We treat, uh, these, uh, models as our coworkers, almost like an intern that, you know, you're constantly, uh, chatting with them, brainstorming, making them do all sorts of work, which we couldn't imagine, you know, Just when ChatGPT was released, it was nice, it was, it could write poems, it could write limericks, it could answer some hallucinative questions, but the capabilities that have emerged now, that pace has been very sort of surprising to me.
AI assessment note: “So what has most surprised me? The pace of development.”
Answered raw tape
D 5 · C 5 · P 4 · Cm 3 4.45
Q And, and so maybe reflecting back since, since the release of GPT-III, what has most surprised you about how LLMs have, have developed?
A So what has most surprised me? The pace of development. So GPT-III was, you know, It was a nice pilot trick and you had to jump through hoops to get it to do something useful. But starting with the, you know, chat GPT was an advance over GPT three. And then you had all these things like chain of thought instruction, following GPT four really made it polished. And, uh, you know, the pace of development has really surprised me. Now, you know, when I started working with GPT three, I could sort of see what its limitations were, what I could make it do, what I couldn't make it do, but I never thought of it as, you know, what it, what these LLFs have become for me now and what, what have become from millions of people around the world. We treat, uh, these, uh, models as our coworkers, almost like an intern that, you know, you're constantly, uh, chatting with them, brainstorming, making them do all sorts of work, which we couldn't imagine, you know, Just when ChatGPT was released, it was nice, it was, it could write poems, it could write limericks, it could answer some hallucinative questions, but the capabilities that have emerged now, that pace has been very sort of surprising to me.
AI assessment note: “So what has most surprised me? The pace of development.”
