| import Mathlib.RingTheory.RingHom.FinitePresentation | |
| import Mathlib.RingTheory.Flat.Algebra | |
| import Mathlib.AlgebraicGeometry.Morphisms.FinitePresentation | |
| /-! | |
| Authors: Judith Ludwig, Christian Merten | |
| Note: This only works on the mathlib branch chrisflav/finpres.2 | |
| -/ |
| #include <stdio.h> | |
| #include <stdlib.h> | |
| #include <math.h> | |
| #include <assert.h> | |
| #ifndef M_PI | |
| #define M_PI 3.14159265358979323846 | |
| #endif | |
| // Constants for model dimensions, learning rate, etc. |
This is a living document. Everything in this document is made in good faith of being accurate, but like I just said; we don't yet know everything about what's going on.
On March 29th, 2024, a backdoor was discovered in xz-utils, a suite of software that
| import collections | |
| from fractions import Fraction | |
| import os | |
| import re | |
| from typing import Dict, List, Set, Tuple, Union | |
| from datasets import load_dataset | |
| import pandas as pd | |
| import Mathlib.Tactic | |
| inductive E | |
| | lit : Bool → E | |
| | var : Nat → E | |
| | ite : E → E → E → E | |
| deriving DecidableEq | |
| def E.hasNestedIf : E → Bool | |
| | lit _ => false |
| import order.zorn | |
| import category_theory.category.preorder | |
| import category_theory.limits.cones | |
| universes u v w | |
| namespace category_theory | |
| open limits |
Yoav Goldberg, April 2023.
With the release of the ChatGPT model and followup large language models (LLMs), there was a lot of discussion of the importance of "RLHF training", that is, "reinforcement learning from human feedback". I was puzzled for a while as to why RL (Reinforcement Learning) is better than learning from demonstrations (a.k.a supervised learning) for training language models. Shouldn't learning from demonstrations (or, in language model terminology "instruction fine tuning", learning to immitate human written answers) be sufficient? I came up with a theoretical argument that was somewhat convincing. But I came to realize there is an additional argumment which not only supports the case of RL training, but also requires it, in particular for models like ChatGPT. This additional argument is spelled out in (the first half of) a talk by John Schulman from OpenAI. This post pretty much
Audience: I assume you heard of ChatGPT, maybe played with it a little, and was impressed by it (or tried very hard not to be). And that you also heard that it is "a large language model". And maybe that it "solved natural language understanding". Here is a short personal perspective of my thoughts of this (and similar) models, and where we stand with respect to language understanding.
Around 2014-2017, right within the rise of neural-network based methods for NLP, I was giving a semi-academic-semi-popsci lecture, revolving around the story that achieving perfect language modeling is equivalent to being as intelligent as a human. Somewhere around the same time I was also asked in an academic panel "what would you do if you were given infinite compute and no need to worry about labor costs" to which I cockily responded "I would train a really huge language model, just to show that it doesn't solve everything!". We
Audience: I assume you heard of chatGPT, maybe played with it a little, and was imressed by it (or tried very hard not to be). And that you also heard that it is "a large language model". And maybe that it "solved natural language understanding". Here is a short personal perspective of my thoughts of this (and similar) models, and where we stand with respect to language understanding.
Around 2014-2017, right within the rise of neural-network based methods for NLP, I was giving a semi-academic-semi-popsci lecture, revolving around the story that achieving perfect language modeling is equivalent to being as intelligent as a human. Somewhere around the same time I was also asked in an academic panel "what would you do if you were given infinite compute and no need to worry about labour costs" to which I cockily responded "I would train a really huge language model, just to show that it doesn't solve everything!". We
