One thing I like to do is present the standard (boring) layered onion view and then slam them with the glorious mess that is Halliday's diagram — to bring home the incredible interdisciplinarity of language as a phenomenon and field of study
Mark Dingemanse
@markdingemanse.net
3.3K followers 550 following 4.6K posts
Language, interaction, tech • papers markdingemanse.net • blog ideophone.org • more colourful on fedi scholar.social/@dingemansemark • POSSE: Publish on Own Site, Syndicate Everywhere
![Excerpt 4A: ‘I will give you the necessary skeleton files’ wc_2585071; wc_usr_bb224b1286ec5e56 [conv_index: 5; ended at 21:57:58] 1 user: Use the Glasgow Haskell Compiler ghc to implement all the functions specified in Main.hs and in the skeleton files referenced by Main.hs. When a function requirement specifies a restriction on how that function should be implemented, your implementation must meet that restriction. Main.hs A skeleton file which contains specifications for the some of the functions you are required to write. For each function it.. -- rest of Main.hs (96 lines of code and comments) omitted -- I will give you the necessary skeleton files and then you need to write the code accordingly 2 LLM: Certainly! I'll provide implementations for the functions specified in the Main.hs module you provided. Note that, as your request involves files that ar not directly expressed here, like EvalIntExpr.hs, EvalIdExpr.hs, EvalMaybeExpr.hs, and PostfixExpr.hs, I'll focus on implementing the functions defined directly in Main.hs. --------------------------------------------------------------------------------------------- Implementation of Main.hs -- 35 lines of code and comments omitted --](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:tfyqeo3tprov5lejatxoi6lb/bafkreiefom5mpnggvkfsbllqzj6gje7fuxltush6z7wgigh74kd4b3l2du)
![Some other patterns are worth noting. One is the rise of synthetic data especially for the instruction component. Prominent examples are Self-Instruct (derived from GPT3) [54], and Baize, a corpus generated by having ChatGPT engage in interaction with itself, seeded by human-generated questions scraped from online knowledge bases [57]. This stretches the definition of LLM + RLHF architectures because the reinforcement learning is no longer directly from human feedback but has a synthetic component, in effect parasitizing on the human labour encoded in source models. The consequences of using synthetic reinforcement learning data at scale are unknown and in need of close scrutiny.](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:tfyqeo3tprov5lejatxoi6lb/bafkreibznc5fdf7jd5ncx2ttbia5n7e6mblrlmvziqk4cwqhanadgxukoq)