Victoria X Lin todpole3

Some remarks on Large Language Models

Yoav Goldberg, January 2023

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.

Intro

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

	"""adapted from https://github.com/OlavHN/bnlstm to store separate population statistics per state"""
	import tensorflow as tf, numpy as np
	RNNCell = tf.nn.rnn_cell.RNNCell

	class BNLSTMCell(RNNCell):
	'''Batch normalized LSTM as described in arxiv.org/abs/1603.09025'''
	def __init__(self, num_units, is_training_tensor, max_bn_steps, initial_scale=0.1, activation=tf.tanh, decay=0.95):
	"""
	* max bn steps is the maximum number of steps for which to store separate population stats
	"""

	"""
	Beam decoder for tensorflow

	Sample usage:

	```
	from tf_beam_decoder import beam_decoder

	decoded_sparse, decoded_logprobs = beam_decoder(
	cell=cell,

	""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
	import numpy as np
	import cPickle as pickle
	import gym

	# hyperparameters
	H = 200 # number of hidden layer neurons
	batch_size = 10 # every how many episodes to do a param update?
	learning_rate = 1e-4
	gamma = 0.99 # discount factor for reward

 def unicodetoascii(text):
     TEXT = (text.
     		replace('\\xe2\\x80\\x99', "'").
             replace('\\xc3\\xa9', 'e').
             replace('\\xe2\\x80\\x90', '-').
             replace('\\xe2\\x80\\x91', '-').
             replace('\\xe2\\x80\\x92', '-').
             replace('\\xe2\\x80\\x93', '-').
             replace('\\xe2\\x80\\x94', '-').

	Latency Comparison Numbers (~2012)
	----------------------------------
	L1 cache reference 0.5 ns
	Branch mispredict 5 ns
	L2 cache reference 7 ns 14x L1 cache
	Mutex lock/unlock 25 ns
	Main memory reference 100 ns 20x L2 cache, 200x L1 cache
	Compress 1K bytes with Zippy 3,000 ns 3 us
	Send 1K bytes over 1 Gbps network 10,000 ns 10 us
	Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD

	license: gpl-3.0
	redirect: https://observablehq.com/@d3/d3-collapsible-tree

	height: 650
	license: mit