Rejection Sensitivity in Gifted and Twice-Exceptional Children
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Rejection Sensitivity in Gifted and Twice-Exceptional Children
Unofficial Hacker News client; not affiliated with Y Combinator.
bonsai_spool · · focus · HN ↗
Even the core idea, 'rejection sensitivity' is acknowledged not to be a diagnostic entity: so then how can we make definitive comparisons? Maybe clinical gestalt... but the author is not trained to see and evalaute patients.
adamsb6 · · focus · HN ↗
Vibe writing may be an improvement. I doubt frontier models would go along with whole language reading instruction without a tremendous amount of pushback.
For those not in the know, a generation of children were taught to read sans phonics. They were told to do things like guess at words based on pictures or context clues. Some lessons had teachers covering words up and asking kids to use guessing strategies at what they might be.
CrazyStat · · focus · HN ↗
> Give me two paragraphs on the benefits of whole language reading instruction. Don't refer to other reading instruction methods or compare/contrast, just focus on the positives.
And got, with no pushback,
> Whole language reading instruction emphasizes making reading meaningful, engaging, and connected to students’ real experiences. By surrounding children with rich, authentic texts—such as stories, poems, informational books, and student-created writing—it encourages them to see reading as a purposeful form of communication rather than simply an academic task. Students are invited to use context, prior knowledge, illustrations, language patterns, and their understanding of meaning to engage with texts. This can help foster curiosity, comprehension, confidence, and a positive identity as a reader. Because students often have opportunities to choose books and respond personally to what they read, whole language instruction can also support motivation and a lasting enjoyment of reading.
> Whole language instruction also integrates reading with writing, speaking, and listening, helping children experience literacy as an interconnected set of communication skills. Classroom activities may include shared reading, read-alouds, journal writing, storytelling, discussion, and collaborative projects, allowing students to develop literacy in varied and socially meaningful contexts. Teachers can build instruction around students’ interests and developmental needs, creating opportunities for creativity, self-expression, and deeper thinking about texts. This emphasis on authentic language use can strengthen vocabulary, comprehension, oral language, and awareness of how written language functions while helping students develop independence and confidence in using literacy throughout their daily lives.
Time to update your priors on how sycophantic frontier models are.
sfRattan · · focus · HN ↗
And CrazyStat's implication strikes me as still largely true, so I'll make it explicit: even frontier LLMs will still resort to sycophancy when there's sufficient contradictory information in their weights to reproduce only some of it in response to a user query. And the harnesses that LLM providers silently, invisibly integrate behind-the-scenes to counteract hallucination and syncophancy are definitionally patch-jobs at best. The latest models may be better when explicitly instructed or configured by default to search the web and pull sources into their context, but the root problem remains unsolved and can still rear its flattering head if you're not paying close attention.
[1]: <a href="https://journals.sagepub.com/doi/10.1177/1529100618772271" rel="nofollow">https://journals.sagepub.com/doi/10.1177/1529100618772271
[2]: <a href="https://features.apmreports.org/sold-a-story/" rel="nofollow">https://features.apmreports.org/sold-a-story/
antonvs · · focus · HN ↗
All this talk of contradictory information, sycophancy, and flattery is little more than anthropomorphic projection in cases like this.
One prompt will elicit a response based on one set of weights that are most closely related to the prompt. A different prompt will elicit a response from a different set of weights, for the same reason.
There’s no contradiction from the point of view of the model and its responses, and no need to invoke sycophancy or flattery as a reason for the behavior. You’d get the same behavior from a model that hadn’t been RLHFd to be sycophantic.
sfRattan · · focus · HN ↗
Sycophancy and flattery are words that relate to how humans experience LLMs. It's appropriate to use words that could anthropomorphize as descriptors for a problem when that problem exists in the space of human experience. It doesn't matter that an LLM is merely a predictive model. It matters that humans who use the model perceive it as a conversation partner and walk away feeling 'flattered' by the model's predictive responses to the humans' input, which the model predicts without regard for factual accuracy, which happens in turn because (as you point out) "there’s no contradiction from the point of view of the model."
We could call the problem "LLM predictive output which tends to have the effect of flattering the user's opinionated assumptions without regard for factual accuracy" if you prefer that much of a mouthful, but "sycophancy" describes the problem well and is one word instead of twenty.
> One prompt will elicit a response based on one set of weights that are most closely related to the prompt. A different prompt will elicit a response from a different set of weights, for the same reason.
Yes or, in other words: there's sufficient contradictory data which were used to train the model's weights that it will predict (a series of tokens which when read in order in English convey) accurate information for some prompts and (a series of tokens which when read in order in English convey) inaccurate information for others. That the contradiction is beyond the LLM's scope is part of the problem. The two identical parentheticals above are (or ought to be) as unneeded as pointing out that a statistical model cannot technically be sycophantic.