Likelihood-Based Diffusion Language Models
Authors: Ishaan Gulrajani
Tatsunori Hashimoto
Venue: NeurIPS 2023 poster (2023)
Keywords: diffusion
language
model
TL;DR: We train and study diffusion language models from a likelihood perspective.

Abstract: Despite a growing interest in diffusion-based language models, existing work has not shown that these models can attain nontrivial likelihoods on standard language modeling benchmarks. In this work, we take the first steps towards closing the likelihood gap between autoregressive and diffusion-based language models, with the goal of building and releasing a diffusion model which outperforms a small but widely-known autoregressive model. We pursue this goal through algorithmic improvements, scaling laws, and increased compute. On the algorithmic front, we introduce several methodological improvements for the maximum-likelihood training of diffusion language models. We then study scaling laws for our diffusion models and find compute-optimal training regimes which differ substantially from autoregressive models. Using our methods and scaling analysis, we train and release Plaid 1B, a large diffusion language model which outperforms GPT-2 124M in likelihood on benchmark datasets and generates fluent samples in unconditional and zero-shot control settings.

Sixelihood-Baseed Diffusion Language Models
Ishaan Guirajani Stanford University igul222@gmail.com com
Tatsunori B. Hashimoto Stanford University thashin@stanford edu
Abstract
Despite growing interest in diffusion-basse language models, existing work has not shown that these models can attain nontrivial likelihoods on standard language modeling benchmarks. In this work, take the first steps towards closing the likelihood gap between autoregressive and diffusion- based language models, with the goal of building and releasing a diffusion model which outperforms small but widely-known autoregressive model. We pursue this goal through algorithmic improvemenss, scaling laws, and increased compute. On the algorithmic front, we introduce several methodological improvements for the maximum-likeliman training of diffusion language models. We then study scaling laws for our diffusion models and find compute-optimal training regimes which differ substantially from autoregressive models. Using our methods and scaling analysis, we train and release Plaid IB, large diffusion language model which outperforms GPT-2 124M in likelihood benchmark datasets and generates fluent samples in unconditional and zero-shot control settings.
1 Introduction
Large language models at the center of recent advances in artificial intelligence. Shared across nearly all such language models is common recipe: learn model that maximizes data likelihoods pretraining has been a
using an autoregressive, left-to-right factorization. Maximum-likelihon remarkably successful paradigm, leading models that perform well range of downstream tasks and display complex behaviors like in-context learning 28
Thus far, autoregressive modeling has been core part of this process due to its computational efficiency and empirical performance. However, this choice carries drawbacks. Autoregressive models generate tokens one at time, making it difficult to perform long-range planning controllable generation [17 19 21. In addition, certain sequence distributions may be fundamentally more difficult to model autoregressively (22)
Given the importance of language modeling, these potential drawbacks motivate us to explore alternatives to the autoregressive approach. As promising candidate, we turn continuous diffusion
models 32 12,, which have achieved sfate-of-the-an results in image modeling [ 30 31. In
language, prior works on diffusion models exist [e.g. [1] [0] 6), but these optimize non- con-likelihoon based objectives. Without the ability to use standard likelihood-baase is difficult to say precisely how these models compare to autoregressive models (see Section 7 for a discussion). Somewhat concerningly, there is no work showing that it is possible for diffusion language models to achieve any nontrivial likelihoods standard benchmarks.
benchmarks (2] 14| 26
In this work, we explore the limits of likelihood-basee diffusion language models. Our goal is to train and release a diffuusin model which achieves better likelihoods than GPT-2 124M [29], which we consider the smallest widely-adopter autoregressive model today. To achieve this goal, we first
ctm We release our code and pretrained models at http:/chhbbbccmmiggggg////////
37th Conference on Neural Information Processing Systems (NcurIPS 2023).

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5.0
4.5
4.0
NLL (val)
3.5
3.0
Plaid IB
2.5
1015
10
1017
1018
10t9
1020
1021
1022
Non-Embeddding FLOPs
Figure I: Plaid models scale predictably across five orders of magnitude. Our largest model, Plaid IB, outperforms GPT-2 124M in zero-shot likelihood (see Table
develop an algorithmic framework, then study its scaling laws to enable -pppimll training, and finally train large model called Plaid IB.
Our contributions are as follows:
1. We explore the design space of likelihood-basse diffusion language models and propose an algorithmic framework called Plaid. We validate the design choices of Plaid through compute-matcher matched ablations.
2. We study the scaling laws of Plaid training. Our analysis shows that the log-likelihood of Plaid models improves predictably with more compute. We derive a recipe for compute- optimal training which differs substantially from the usual autoregressive rule.
3. We train and release Plaid IB, large diffusion language model pretrained OeenWeb- Text2 77 Plaid 1B outperforms GPT-2 124M in zero-shot likelihood across six standard benchmarks. We demonstrate Plaid 1B's ability perform fluent and controllable text generation.
2 Variational Diffusion Models for language
In this background section, we formally define continuous diffusion models over text sequences, adopting the Variational Diffusion Models (VDM) framework [18) which is natural fit for likelihood- based training (see Karras et al. [16] for a survey on other formalisms). For brevity, we simplify some details in our exposition and refer the reader to Kingma et al. [18] for details.
Consistent with prior work (e.g. Li al. 21 our basic approach will be to map discrete text sequences into a continuous space with token-wise embedding function and then construct diffusion model on the embedded data.
2.1 Forward diff'usion process
drawn from the data distribution q(x). We transform æ into sequence ā of embedding vectors using an invertible token-wise embedding function Embed(-), such that (()) (z.)d
Consider a sequence of tokens = (,(")......l
[(41
The forward process is a Markov chain over latent variables 2, from / which progres- sively adds Gaussian noise to ë. Let (t) be some monotonic function that specifies the total noise

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added by time t. We then define the forward process distribution & with T discrete timesteps as
== q x) II ((
(1)
i=1
where ((2022)) o( and ((,,(=)) == W2,,, (t) dz(s)). It follows from this that (<2s/ Fr, 2) is also Gaussian, which will be useful later.
2.2 Reverse generative process
We approximate the forward process distribution by learned Markov reverse process where time runs backward from = 1 to t = 0. The reverse process with timesteps is defined via the decomposition
po(x,
: == p(1)
II po po(
h p(x)2o).
Let < denote the portion 2, at sequence position Then we choose p= NV N(0,)=)(((, and p(z|2z) = 11, ((x(1))))))) (()) 1=00)) where 1(x("1==0))
(6) (z;). Recalling that ((<((-)/r/=;...22 is Gaussian, for the remaining factors we choose pe (e(2(1- 13/T ki/T): = (( #2(2/T)) where (x{))) is a denoiser neural network that approximates By []|2z] Finally, our generative model given by the marginal distribution pe (=(x) J2 (1 2). &g is optimal, then the forward and reverse processes express the same joint distribution as o (0) + (1) + co, and - oc.
2.3 Likelihood bound
To optimize and evaluate the likelihood, we can write a variational lower bound (VLB) for the log-likelihood as
log (x) S VLB(() == Dk((o(z))))ll(ee)) + (otooia)--oopppllann + Cr (3)
where
LT
E [DKI Pue
(4)
In the T limit, Cr simplifies
L.c = [
(SNReeeeerooeeessSRRS/S//RR (SNR' (=)
(5)
2
where SNR'(t) := di (-)))) We use Monte- Carlo estimates of the resulting continuous-tin likeli- hood bound to train and evaluate our model.
2.4 Learned noise schedule
crucial hyperparameter in diffusion models is the noise schedule d²(t), which specifies how much noise add at each time in the diffusion process. In setting, the VLB is differentiable with respect to (t) via the reparameterization trick. Moreover, the VLB is invariant to the value of (²) except at 0 and
in the continuous-time limit.
We can therefore parameterize (t) as a ccalar-to-scala neural network and learn by gradient descent. We train the endpoints (0) and o² maximize the VLB, and the schedule in between the endpoints to minimize the variance of the Monte-Cari estimate of the VLB. Minimizing the loss variance is proxy for minimizing the gradient covariance trace, which generally speeds up learning. See Kingma et al. 18 for further implementation details about this training procedure.
The Plaid framework
In this section, we present series of algorithmic improvements to the basic setup described in Section [ The result framework for diffusion language models which we refer as Plaid (Perplexity- LAnguage Inverse Diffusion).

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3.1 Learned embeddings
In an autoregressive language model, the embedding operation is simply the first layer of the neural network and thus can be treated as just another part of the network. This is not true of embeddings in diffusion language models, which play a more fundamental role: they determine the order which different tokens get generated. Tokens whose embeddings are far apart become distinguishable early the reverse process, whereas nearby embeddings are distinguishable only later, at low noise levels.
Despite the importance of embeddings in diffusion language models, the loss functions used in prior work [21 6 lead to ill-posed problems when optimized over Embeed: for example. if our objective is Ly reconstruction, then collapsing the embeddings by setting WEmbed = 0 and (2(2)) = 0 yields a degenerate solution with zero loss. Prior work addresses this with workarounds like choosing WEmbed by hand 33 or using heuristic regularizers [21] or constraints [6].
In contrast, the Plaid loss function is bound on the log- logeliiel of the discrete data, which is a meaningful objective over both the model weights and embeddings. We therefore optimize the embedding matrix Wmmbed jointly with the rest of the model without additional constraints.
3.2 Categorical reparameteriizttoon
When optimally trained, (=(=;) learns to approximate a conditional expectation E[ū[z]] over se- quences of word embeddings At low noise levels, some all of the embeddings in & deterministic given 2,, so an optimal iig (2,, should output these exactly. However, doing so requires memorizing embedding vectors high precision somewhere inside the model parameters, which is poor use of capacity.
Instead of foreing the model memorize the embedding vectors, reparameterize (2())) as an average of embeddings weighted by a softmax over tokens. More formally, let fo(2)) be a neural network which outputs logits and define â average over embeddings =! (z+)
W WmmbedSSftmaa (f
(=z)). We can interpret f as learning a posterior over each discrete token (+() given 21. This relates to methods proposed in prior work, but these either require proxy objectives 21 6) or consider image models [3].
3.3 Output prior
When we interpret fe as a posterior over tokens, the optimal value of (2() is log ((x(0)11)) Z.
which decomposes as log q(z) (,())))))))) + log ((x())=((()) + Z where (+1) := ( (1) : i We
view the first term as a prior constraining the model's predictions to those which are plausible given 24, while the second term models relationships between different tokens.
To make it easier to model fa. compute the first term in closed form the log-density of Gaussian oz (t)1) and add it to the output. This leaves the neural network with only the easier task of estimating log p(x(((()))) (((+t)). Empirically, we found it helpful to linearly anneal in this
prior over the first 5000 steps of training.
3.4 Learned conditional likelihood
Recall that our loss function (3) includes conditional likelihood term log p(x)=g). We are free to choose p however wish, and in Section 2 we chose position-wis wise factorial model p(2|220) := P( (0)) ), with a simple fixed distribution for each factor. This choice is optimal for sufficiently small (0), but using a more powerful model allows (0) to take a larger value, effectively truncating the reverse process and therefore making it simpler to learn.
Here we leverage the fact that, after applying the categorical reparameterization (Section 3.2 our neural network fo (2)) can be interpreted as learning the logits for (22) [2x) at all positions i. We therefore choose to keep p(zo22) as a factorial model, but define each factor p(x;) (26 using the more powerful learned model softmax (z+)).
Implementing this change naively requires two evaluations of So for each minibatch example during training, corresponding to the two terms of 3 Loe and log pe (z|20). We instead split each minibatch,

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Table 1: Compute-matched ablations of algorithmic components on Open Wextx2...
NLL bound (val.)
Our full method
3.89
Our full method (0.5x compute)
4.01
No learned noise schedule
4.17
No learned embeddings
4.54
No categorical reparameterization
4.25
No output prior
3.95
No learned conditional likelihood
4.03
No self-conditioning
3.98
CDCD (our reimplementation)
4.23
using some examples compute L. and the compute log We allocate examples between the terms according the ratio V Var(log where we compute the variances using running estimates of each term's first and second moments. This minimizes the variance of the full loss
Var(
3.5 Seff-conditioning
3 is technique which improve the performance of diffusion language models. The core idea is reparameterize the denoiser (2())) the fixed point Y/c. of recurrence y/ 0, W1+1 == Ky zt, Wi) where i is a neural network which now takes two inputs instead of one. During training. we approximate the fixed point yoo by randomly unrolling the recurrence either // (with probability 0.75) or y2 (otherwise). When we unroll to 32 during training, we zero the gradients with respect y1, the noise schedule, and the embeddings. During held-out likelihood evaluation, we always unroll to During sampling, instead of solving the recurrence from scratch at each step of the diffusion chain, we compute b% (21, 0) for the first step and [z,, subsequent steps.
Self-conditioning
(2, (2(24((/)))---- for
3.6 Other details
We perform all forward and backward computations in double precision except for the Transformer layers themselves, which happen in bfloat16 16 mixed precision. This comes at negligible extra cost since the Transformer layers dominate the overall cost.
Architecture choices We condition io(=)) on the timestep / by adding sinusoidal encoding of I. the Transformer's 's residual stream before the first layer. Before feeding 2₁ into the Transformer, we rescale it by a factor of / + o²( which makes each input dimension approximately unit-variance. Whereas autoregressive Transformers are relatively insensitive to aspect ratio [15) we find that Plaid performance increases significantly with Transformer depth up to about 16 layers. We also find that performance is sensitive to the choice of embedding dimension, with small values performing best. In all experiments, we use embedding dimension 16.
Stochastic sequence length Unlike autoregressive models, diffusion language models only operate on sequences of exactly the same length as those seen during training. To enable our model to generalize to shorter sequence lengths, truncate small random subset of examples seen during training to random lengths. We observe that truncating even 3% of examples allows the model generalize well across lengths without impacting full-length performance. Short-sequence performance does not improve substantially as we increase the number of truncated examples.
4 Ablation experiments
In this section, we validate different aspects of the Plaid framework through compute-matchen ablation experiments.
5

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4.1 Validating likelihood-bard based training
We take a likelihood- based approach this work for multiple reasons: it has a principled interpreta- tion, it simplifies training and evaluation, and it has yielded strong results in autoregressive models. Here, we validate that the log-likelihood objective can attain competitive sample quality through a human evaluation.
In diffusion models, the logeliiel bound is an expectation over noise levels of reconstruction loss weighted by specific function of the noise level. In contrast, most prior work on diffusion models for language [21 33 as well as images [12 use heuristic weight schedules. Motivated by the intuition that human perception is more sensitive to coarse structure than fine details, these typically assign more weight to higher noise levels than the likelihood weight schedule.
We train three Plaid models: one with the likelihood weight schedule ("VLB") and two with heuristic weight schedules ("Schedule A" and "Schedule B²") which plot in Appendix [ The models trained on a large dataset of short children's ' stories which we constructed by finetuning GPT-J [34] on ROCStories [27]. Because learning embeddings only straightforward when training against the likelihood bound, all models use fixed embeddings obtained from a previously- trained known-good model.
We repeatedly asked crowdworkers to choose from a pair of model samples, where one sample came from the likelihood-traine model and the other came from heuristically- trained model. On average, crowdworkers preferred the likelihood-trained model over both alternatives: Weighting A's win rate was 0.449 (p- 0.001, 95% CI [0.417, 0.482)) and Weighting B's win rate 0.457 (p 0.005, 95% CI [0.425, 0.490)). Because we only consider two alternative weight schedules, we do not claim that the likelihood objective yields optimal sample quality, but our results suggest that it performs at least comparably to other choices.
4.2 Validating algorithmic components
Having validated our likelihood-bacce approach, we show in this section that each of the algorithmic components described in Section lead to improved likelihoods in a compute- matphed ablation study.
We train Plaid models on OpenWeeTex12 7 and report their log-likelihood bounds on held-out data in Table D Our reference model ("full method") is a 16 x 384 Transformer with 28M non-embedding parameters, trained for 92K steps at batch size 256 and sequence length 256, corresponding to 1.12 x 10 non-embedding FLOPs. For each ablation model, we stay as close to this configuration as possible while preserving the number of non-embedding FLOPs (we exclude FLOPs from the embedding and output projections because these become negligible at large scale). We observe that ablating each of the components described in Section results in a worse log- ligliiielhoodd As a comparison point, also train model at half the compute budget (5.6 x 10"7 FL.PS) by halving the model size. See Appendix [oror more training details.
Finally, as a comparison to prior work, we reimplement CDCD 6 train it following the same configuration, and report its log-likelihood. We follow the authors' description as faithfully as possible except for the noise schedule endpoints, embedding dimension, and embedding weight initialization, which we tune to maximize oogllikelhhood. We observe Table | that even the half-compute- budget version of Plaid surpasses CDCD implementation in likelihood. Note that CDCD was not developed as a likelihood- based model, and the lack of a public implementation means that there most likely differences between our implementation and the original.
Scaling laws for Plaid
Having developed an algorithmic framework for diffusion language models, we now study its scaling properties in order to guide large-scale training of Plaid models. In the case of autoregressive models, the work of Kaplan et al. [15] demonstrates that model log- likeliiissoo follow a log-linear sealing law: across many orders of magnitude, training with more compute predictably improves likelihood. Using these scaling laws, Kaplan et al. [15] and Hoffmann et al. [13] accurately predict the optimal model size as a function of the given compute budget across many orders of magnitude. Both results together enable effective large-scale training. In this section, we experimentally determine them for Plaid models.

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5.0
Diffissin Autoregressive
Non- Con-Eimeeddingg Parsmeters
10"
NLL (val)
107
Diffusion
Autoregressive
10a
101s
1020
10th
1017
10
Non- Non- Embedking FLOPs
Non-Embedding FLOPs
Figure 2: Plaid models improve with compute similar rate to autoregressive models, but Plaid is less efficient by a constant factor of x4x.
Figure Compute-optimal Plaid models should be 4x smaller (and trained for 4x longer) than compute-optimal autoregressive models.
5.1 Methodology
Our main experimental method will be an IsoFLOP analysis 13 We first fix set of FLOP budgets {Ci,..., CR). For each budget C, train models with different sizes {Nc. Nc.M) } and perform a quadratic fit of the loss L to log N. We plot all the data along with quadratic fits in Appendix D and find that the fits approximate the data well.
The minimum of the quadratic gives us the compute-optimal loss Lg and corresponding model size Nč for that budget.
Given the compule-optimal loss Tic for each FL.OP budget C, we fit the parameters loss scaling law
min E(log(LZ.) - log(C;) aa)
which can then be used to predict the compute-optimal loss as L'(C) aC We also fit a parameter scaling law N (C) in the same fashion from the model sizes Nč.-
perform IsoFLOP analyses for both Plaid and autoregressive models order to compare the results. We choose compute budgets log-uniformly between 10 and 10 FLOPs and corresponding model sizes heuristically. We choose learning rates using μTransfer [35] and batch sizes, weight decays, and aspect ratios by well-tuned heuristics. When computing FLOPs, from the embedding layers and output projections. This enables us to use much smaller compute budgets than Hoffmann et al. [13], but it causes autoregressive scaling law to differ slightly from theirs. We consider this acceptable since we are mainly interested in the differences between our autoregressive and Plaid scaling laws.
exclude FLOPs
5.2 Loss improves predictably with compute
We plot both of our scaling laws in Figure Our first finding is that over many orders of magnitude, the compute-optimal log-likelihood compute. Surprisingly, we find that the slopes of both the autoregressive and diffusion scaling laws are almost exactly the same. These results validate Plaid's scalability and suggest that we can obtain strong improvements by training larger scale.
Plaid models closely matches power law function of the
Regardless of scale, Plaid models require constant factor of about 64x more compute to match their autoregressive equivalents. While this factor is large, our work represents the very first attempt efficient diffusion model training and focused engineering effort on constant-factor improvements to diffusion models may enable them to perform similarly to autoregressive models in the future.

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Table 2: Plaid IB outperforms GPT-2 124M in zero-shot likelihood across six benchmark datasets from Radford al. 29 Our GPT-2 numbers differ from the originals due striding and detokeniza- tion (see Section 6.1
PTB
enwik8
text8 (BPC)
WikiText2 (PPL)
WikiText103 (PPL)
IBW
(PPL)
(BPC)
(PPL)
Plaid IB (ours)
74.33
1.18
1.12
29.42
28.28
77.64
GPT-2 124M
87.97
1.24
1.22
35.01
35.92
87.85
GPT-2 345M
64.92
1.09
1.11
26.80
26.13
67.34
GPT-2 762M
53.42
1.04
1.06
23.30
22.24
59.48
GPT-2 1.5B
47.59
1.00
1.02
21.33
20.13
54.09
Table 3: Chosen unconditional samples from Plaid 1B demonstrate fluent syntax and long-range coherence. See Appendix for un un-picked random samples.
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5.3 Compute-optimal training recipe
Our next goal is to understand how to optimally use given compute budget C to maximize the held-out likelihood of model. Specifically, must choose between training large model for fewer iterations or training a small model for longer. For this, we leverage our parameter scaling law N (*(C) which predicts the optimal model size given a compute budget.
We plot both of our parameter scaling laws in Figure and again find that the trends have nearly the same slope but differ by constant factor. Specifically, compute-optimal Plaid models should be about 4x smaller (and therefore trained for 4x longer) than compute-optimal autoregressive models. The large gap in compute-optinet settings suggests that selecting model sizes based existing scaling laws 11 13,, which were developed for autoregressive models, could incur a substantial loss in the effective compute budget.
Plaid 1B
To demonstrate the scalability of Plaid models and achieve our goal of outperforming autoregressive model in likelihoods, we train, evaluate, and release a large Plaid model called Plaid IB. Plaid IB is a Transformer- based Plaid model with 1.3B parameters, trained for 314B tokens OpenWebText22 [7]. In total, Plaid IB was trained for 2.5 x 102 FLOPs, which to our knowledge equals the largest purely diffusion-based language model trained in prior work (6). We give further training details in Appendix C
6.1 Likelihood evaluation
We evaluate Plaid IB's likelihood in zero-shot setting a suite of six benchmark datasets orig- inally used in Radford et al. [29]: Penn Treebank 225 enwik8 and text8 [4), WikiText2 and WikiText103 (26). and the One Billion Word corpus 2
8

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Table 4: Chosen conditional samples from Plaid IB in different zero-shot control settings. Highlighted spans are prompts. See Appendix for un-picked random samples.
Prefix completion:
Generative models of text very versatile: they used data classification model and also incorparated into miltiple data procensing angines, inin this article, wa present too nee neural and conputational
nemory nodels capable of processing terabytea of and the neural techniques that are used in those models.
Infilling:
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day! So relaxed and too happy. do not think Token-level weights (5x weight on "law"):
Let's talk about las and nedicine. SaAnlaNoIn her dissent, Justice Ron Sen, veteran adninistrative laz judgo, points out that the decision "ignores the fact that the original separation agreenent was reaponded to "the legitinate ethical administration," which is what liee "at the heart of lav and zedicine." Token-level weights (5x weight "medicine"):
reached by binding arbitration"
of the univeraity
Let's talk about las and uedicine. In'aIn part because of advancenenta in technology, personal information about
drug use spreading. Healthcare professionals across rely this personal data to take decisions about drug prescriptions and clinical trials and monitor people at inmediate rick of serious or chronic disesees.
Lexical constraints ("Donald" anywhere):
Also facing legal challenges Donald Trump's executive order banning innigration from seven hualin-najority countries that facing temporary halt, nothing acheduled to effect. Two Iederal judges have ruled that such an order violates the establishment clause.
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A nonth later, with little time to spare, the government hired Donald V. Davis, a forner senior aide to Tom Nondale lead tactical centera.
and Chief Security Operations Officer
House, to
Radford et al. 29 use sliding windows of size 32 in their likelihood computation. As a non- autoregressive model, Plaid doesn" support sliding-window likelihood evaluations, so non- overlapping 1024-token sequence when computing likelihoods. Following Radford et al. [29], we use heuristic invertible detokenizers for PTB. IBW, and WikiText to minimize the effect of tokenization artifacts on the perplexity results. For fair comparison, we also recompute GPT-2 likelihoods using the same protocol, resulting in different numbers than Radford et al. 29
In Table 2 we observe that Plaid IB consistently outperforms the 124M parameter GPT-2 model, demonstrating that diffusion models capable of scaling to perplexities par with small modern autoregressive model.
6.2 Unconditional samples
We generate from Plaid IB by starting from 21 ~ NN NN (1))),, performing ancestral sampling of p( ((=t_(1/T) |xx)) for T 4096 steps, and finally arg max (2) Following Dieleman et al. (6)
we sample using a score temperature of = 0.9, which in our formulation corresponds to adding |- 22) &o (2x) at each step.
We generate unconditional samples with sequence length 1024 and present chosen samples in Table 3 We observe that the model is capable of generating fluent text and remaining on-topic over several hundred words. We provide random un-picked samples in Appendix
6.3 Zero-shot control
Although Plaid models are trained a purely unconditional fashion, we present a zero-shot control technique called token guidance which allows to implement number of conditioning structures generation time. We begin with classifier guidance, a technique which allows diffusion models to generate samples conditioned on arbitrary attribute y. Classifier guidance first trains probabilistic classifier p(y)22 24) of y given noisy latents 2,, and then biases the diffusion model's sampling steps by
We choose the splitting boundaries using the Plaid tokenizer, yielding sequences slightly shorter than 1024 tokens under the GPT-2 tokenizer.

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guidance term derived from the gradient of the classifier probability V 2, log p(y)2z). Now, recall from Section 3.2 that our denoiser (2(2) is parameterized in terms of model fo(2) which learns the distribution over the token x²(i) at each position i given 2t. We can therefore implement many different conditioning structures via classifier guidance on probabilities derived from fo itself. We give few examples:
Conditioning on span: We perform guidance on the joint probability of the span under the factorial model [2x) x p(x()) p(z(()/aa) where fo estimates each factor in the product. This lets us implement prefix completion and infilling as special cases. Lexical constraints: order to condition the presence of token without specifying its location, we perform guidance on the token's s probability under the unigram distribution ((z((ny) (2x) x Ex p(x(())2)). where fo estimates each term the sum. Token-level weights: We can emphasize a specific conditioning token by multiplying the corresponding guidance term by scalar weight. Negation: We condition on the negation of an attribute y by performing guidance on the complement probability ppy =t).
Using Plaid IB and token guidance, we generate samples under various zero-shot control settings. We present chosen samples in Table 4 and random samples in Appendix E Despite being trained unconditionally, Plaid IB is able to follow diverse conditioning structures.
7
Related work
We contribute to a growing body of work on diffusion-basee language models 21
9 33
2,24 37 23
36 [1. Our biggest departure from those works is that we aim for strong likelihood performance, which to our knowledge has not been attempted in any prior work except for an appendix result from Li et al. 211. We therefore benchmark against well-known autoregressive models instead of prior diffusion language models.
The work most comparable to ours is CDCD [6]. which is also a strong general- purpose diffusion language model. However, without the ability to use standard likelihood-baset benchmarks (25 14 26). it is difficult to say precisely where CDCD stands in comparison to autoregressive models: in every result, either CDCD underperforms the autoregressive baseline, or the evaluation metric saturates and lacks the statistical power to distinguish the models. Many of the other works above share similar difficulties. In contrast, our likelihood-baaae approach enables unambiguous comparisons to widely-known models.
general-purpose
Other diffusion language model works consider more constrained settings like controllable generation 21 or sequence-to-sequenee tasks [3 9 8 37 23 36). or propose hybrid approaches involving pretrained autoregressive models 24 I.I. Particularly, concurrent work, Han et al. [TT] finetune an OPT 13B [38] model into a hybrid model which is autoregressive over 25-token blocks and uses diffusion within blocks. Compared their work, we focus the more general setting of training a fully diffusion based language model from scratch.
Finally, our work builds recent advances in diffusion models for images 32 notably, we adopt the framework of Variational Diffusion Models [8] and extend it to language modeling.
12
30
18
5). Most
8 Conclusion
In this work, we have taken the first steps toward a competitive Akelihood-based diffusion language model. We built Plaid IB. which matches GPT-2 124M in likelihood by combining several algorithmic improvements and a scaling law analysis. Our ablations show that maximizing likelihood does not substantially harm sample quality, and we show samples are fluent in both unconditional and zero-shot conditional settings. Despite this progress, substantial work remains: Plaid narrows the compute- efficiency gap between diffusion and autoregressive language models to 64x, and we view this gap as tractable and exciting open problem that may be addressed with further research.
10

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Acknowledgements
We thank Sander Dieleman, Lisa Li, and John Thickstun for valuable conversations, This work was supported by Open Philanthropy grant.
References
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[33] Robin Strudel, Corentin Tallec, Florent Altché, Yilun Du, Yaroslav Ganin, Arthur Mensch, Will Grathwohl, Nikolay Savinov, Sander Dieleman, Laurent Sifre, al. Self-conditioned embedding diffusion for text generation. arXiv preprint arXiv:2211.042330 2022.
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A Additional experiments
Plaid IB Sample Quality We compare unconditional samples from Plaid IB and GPT-2 through human study. We generate samples of length 128 from both GPT-2 and Plaid IB (n = 1023 samples each), and repeatedly ask Mechanical Turk crowdworkers choose the most coherent sample from pair (one GPT-2, one Plaid. blinded and randomly ordered). The crowdworkers found the models comparable, with the 95% CI for the win rate of Plaid ranging from 0.47 0.55, with a mean win rate of 0.51.
Sampling Timesteps When generating samples, we attempt approximate the infinite-timestepee timestepsse naive sampler (vanilla ancestral sampling) paired with very large number of timesteps than necessary (4096) to approximate the continuous-time limit. To validate that our choice of 4096 steps sufficient approximate the continuous-time limit (with respect to human judgement), we run human binary choice preference study of unconditional Plaid 1B samples generated with 2048 and 4096 steps (n 1024 samples each). We find that crowdworkers do not strongly prefer either choice, with the 4096-step samples having win rate of 0.51 (CI: [0.47, 0.53)).
B VLB and heuristic weight schedules
VLB Schedule A Schedule B
140
120
Weight
80
60
40
20
2
logeA()
Figure VLB weight schedule and and heuristic weight schedules used in ablation experiments.
C Experiment details
C.1 Dataset
Unless otherwise noted, all models in this work are trained on subset of @eennWeTexx12 [ which we filter to remove documents labeled as non-English. The data is tokenized using 32K-token BPE tokenizer which we train on the Open Web2Txx22 training split.
C.2 Architecture
We use standard pre-activation Transformers models with RMSNorn normalization layers and GeLU nonlinearities throughout. Unless otherwise noted, all Plaid models have 16 Transformer layers, which we found to approximately optimal for our scale. We scale autoregressive model depth approximately following (20). For efficiency, our implementation uses FlashAttention 4 and other fused kernels wherever applicable. We train at sequence length 256 for all experiments except Plaid IB.
14

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C.3
Optimization
We optimize all models using A with sareciier-speciffc learning rates derived by pTransfer 35] based learning rate of 1.4 10 at width 256. Each parameter's ' weight decay is set to where is that parameter's learning rate. We use linear warmup on the learning rate and weight decay over the first 2500 steps, followed by a linear decay to zero over training. We train at batch size 256 for algorithm ablations and 128 for scaling law experiments. All of our small runs take less than 24 hours on a single A100, We perform all forward and backward computations double precision except for the Transformer layers themselves, which happen in bfloat16 mixed precision. This comes at negligible extra cost since the Transformer layers dominate the overall cost. Our learning rate and precision choices optional: when implemented carefully, our method trains stably and performs well when peeeespreiiioon floats and a single learning rate for all parameters is used.
C.4
Plaid 1B training
We increase the base μ/Transfer learning rate to 10-3 (at width 256). The denoiser network is Transformer with 24 layers of width 2048 and a vocabulary size of 32K tokens, for total of 1.3B parameters. We train for 1.2M steps batch size 256 and sequence length 1024, for a total of 314B tokens. All other details are as written above. Training took 30 days on A100s.
C.5
User studies
The overall experimental design follows blinded randomized binary choice experiment. The details are as follows: We recruited crowd workers using Amazon Mechanical Turk (selection criteria: US location, 95% HIT approval rate, >1000 HITs approved). Workers were shown two random samples in random order and given the following prompt: "Given two short pieces of text, decide which is more coherent overall (i.e. has the fewest grammar mistakes and makes the most sense)." Workers were paid $0. 15 per task, which we estimated to take less than 30 seconds on average.
D IsoFLOP profiles
4.
44
3.8
4.2
3.5
FLOPs
20 FLOPs
16" FLOSs
166x PLUPs
6.4% 10" FLOWS
FLOPs
FLOP
2.6 20 FLOPs
3.8
100 FLOSs
10
10a
107
10r
Embedding
Embeeding
Figure 5: IsoFLOP profiles for autoregressive models (left) and diffusion models (right).
15

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E
Plaid 1B Random Samples
E.1
Unconditional
tends to creste a ripple effect in the econaay, and econaiitt Xevin Milligan caid it's probably testarent to how worn-down the econoxy and consuners are, particularly st the los end, Anlarlt'e probably due to thinge iske investment sloving in the natural resource sector, veaimess in stock buzbacks the corporate sector,
corporatieo. And, of course, (oe) have look at thinge about what the the horizon in terns of trade, and the exchange rate, and all of these thinge tend to pull down the econory,
Canada is doing, the uncertaiaty on
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getting message fron Conadians about the state the econny and be thinks nany people are wondering if they did gez that aessage from Frine Minister Justin Tradesu in Decombez, 'alrr'dd be happy for people to bear a zedeage, and that has to coze through," " he said. in'nDeepite the second interest rate hike, Milligan said there's reazon to think the odde of an interest rate hike have risen fron about one the-t to about tuo-thirde in year, Ia'e'I would,
the firet part of Treet Thiekaialle easd those prodictione are beconing more consistent as sell, with reported eaying froz ccononis Chev Hore this week that zost econestats consider fourth rate hike a "hear certainty" for 2018, AnlaMill said be's optinistic about the econoay in Canada, Nala't think we're probably the niddle of cocing strong botton and strong wee, and re'r very
balance, expect sustaised number
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into this directional trend for the econony think ve'zo on track for run three straight years of
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+14740]] Gibdobber (19.55) (157000-148601) Arch-Princwas (19.53) (1570001147700 Snow White (18.42)
(157000+147790) Kagislens (19. 41) [157000-148447] Corzin (18,33) [157000-148068) Pinacchio (18.52)
(157000-148396] Ayya. (13.86) (157000-148344 Alaina (18.84) (157000-143344 Claf (17.76) [157000-147791) Cua (17.72)
(157000-148403)
(17.62) 1457200-147823) Papyrux (17. [157000-148396]
(17.64) [157000-148984] Gerals
(17.82) (157000-148653) Marie (16.52) (157006+148650) Auna (16.52) [157000-145597] Leos (16.52) [157000-148677) Paul
(16.48) [157000-148697) Reg (16.46) [157000-118697] Shu (16.44) [157000144665) Aureza (16.43)
[157000+1485577 Reinrich 33) [157000-147798] Perrazan (18.01) [1570001188666] Pwoer (15.91)
(157060-148592) Pagyrua (15.72) (157000-143596) Arch-Maid (15.72) [157000+14805)] Belldobber (16.
(157900-148396] Corria (15.64) (157000-148991] Belena (18.60) [157000-168297] Bazel (18.57) (187000-168644 Alva
(15.56) [157000149653)
Blaf (15.53) [157000-148140] Anna (15.53) [157000+148507] Hana (14.40)
(157000-148507) Beiarich (14.43) (157000-1486E7] Aurora (18. 42) (15700+1488556) Shu (13.38)
(157000+147223] Arch-Princees
[157000-147692] Rlaa (13.37) [157000=148507]
(13.37)
(157800+148437) Richard (13.36) [1570000148878] Garalt (13.35) [157000-148144) Rag (13.34) [157000+143256] Arthur
(13.33) [157000-188553) Papyres (13.27) (157000-148697) Arthur (43.24) [157000-148109) Peter (13.24)
[157000+146597] thu (13.17) [157000414788] Maria (13.08) [157000-146650 Anna (15.08) [157000-148650) Paul (13.07)
[157000-148657] Kagialens (:5.07) [157000-147780) Geral: (13.07) [157000-147780] 01af (13.08) [157000+148055) Rag
(13.05) (157000-148255) Richard (13.04) (157000-149258) Arch-Princesa (13.02) (15700G-148596) Aurora (13.02)
[157000+146504] Heinrich (18.01) Perraman (12.82) [157000-148696] Hana (12.70) [157060-148340] Papyrux (
and nccasionnaly sone of our recorreniations ray have changed. With that said, if you choose to buy smrothing using the retall linka included is this pest, Alcdica may sarn small comiisiion. Roueces, all of ouz product revievs are sritben by independent experts with relevant agenda to consideration. <]endottast] "Tii is about the people,"
Holatain, Ph Philadelphia lasyer sho represented the company targetad by the state robhary task force in the case. "Shen some nut across state has the ability to write contitational amaddan and recrite a constitutional anentrent in way that rakes it tntithetioal to the state of Pannsyl vanin, and may they againat it then it dosan't rwally matter what the people of Pannallvania is want. "c] sclendafteet [PPalic
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16

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of carbon unisaions coeded to neat, the 2 dagres Calaiua larget, sut forth in the last assesament of thsw Intergovermennal Panel on Clinate Change (IPCE) in 2013. hee research rolled out the industry will nead resaver betueen 4,000 10,000 fossil reservws in order to do that, according to an analyaia done by laud wuthor Dr. Erseato Monteiro of the University of Alberta in Danada, In'nThe eye-satering figure reprasents the total srount of oil and gaz shale and ratural gas produced, estracted and sold - will 1ikaly need to be recovered in comsing years to reat the carbon nstigation gosle, dioxide they displaced, "hh benefits of carbon capture incrense with the anount being injacted," Monteiro wrote in as article accepted to publish in the Procandings of the National Acadway of Scientes which Deltng obtained last. veok, An'nieill the tbeoresical ability limi 2 varning with foesil fuel carbon budgets is obricns, such action gas incustry. Industry groupe by openly
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ges
anount wbere between 4,000 and 10,000 netric tons recerves for every 10 netric tons carbon
capture million tans dioxide unixiiona annually fron operating raserves and transfer a portion that back to the ail industry ax revemeer "Comparing natural gas reserves under production with proven oil recerves reraining reveals one potential implication, which is the additional natural gas production that would be required to fully matiafy the 2100 oil eupply balance," " the paper explained. By Monteiro's estimates, natural gas production will grow by equivalent about 4 Gb per year by 2100, waile at the sxte time oil prodection will deciine at about 1 Gb per year, or a total of 2.68 dh per year in balance. If all the reamrrcs at hand cosld be astrasted, of shich no semint ix available at all tines, it would be practical to all that fossil tuel along with exieting operating vells. But natural gas production rises, nee sells could replace declining oil prodection with equivalent mimber of fossil tuels to reservus, Aelnsarbon reductions at any level involve "cnats" additionnl axpenditares that nacesarily arise from ail and gas extraction and asrociated energy production, including phyeical production, liquefaction capability, handling and chipping fees, early stages of exploration and production. Natural gas, thile moze profitable, is zuch riskier then coal. The paper explaine: "Brtracting and producing natural gar exploits the time, location and phycical composition of the fossil record. Bach well representa a cifferent geologieal formation with different natural gas characteristion, These characteristies zean that the anvunt aatural gas recovered froz voll not deterninative of productivity the sell from shich it is extracted. Reterence recerves provide an indication eriet in different sbale formations ons and the potential of these resources based on past and future siscaveries."" "annihe only kind of goonorphological formation that can be considered very promising for sil and gas production are nesopolar generally
not taken by
research found eil and gas compantes
to recover mach natural
possible at all
the natural gas resourcee that may
formstione, those within mile fron surface rogarded as nore inflexibe than perzafrost or acid shale fomations, less stable than other subsurface formations tracked tight rock, pore rurceptible to seepage, and more susceprible to oil production costs. For these reacons, the reserve ectimate put out at the anmual neoting does not consider natural gar production to be an econonic viability existing aesopoint forsations, Anlatho not result is that indastry revenuee to pay part of cost carbon sequestering, underground ecrubber eystems and other forms of carbon capture from any nev natural gas reserves of coning years. In order to fuel plants, the primary gas is oxyges; when undertaking oil and production and developxent that oxygen is extracted from the air and converted into nethane that vested through. rock, It then dispereed at the surtace using various nothode, euch proxinity sequestration facilaties othervice have been, leaked back into the staosphere. Valaia 2014, a tean of scientists frem the National Acadonies of Sciencee calculated that deforring as many as 2000 netric tous of carbon dioxide eaitted from oil and gas recerver already Earth to
three meles below ground Mecopolar formations
lose out billiess of dellars
carbon
uncerground scrubber systese, to provide the chance store carbon dioxide that sould
ammally the eil induatry per about V835 60 billisn. "Fisancials indicate that nonconvstionall expenditures, including relatively largo oaes, eill have a naterial net ixpact on industry and cubstantial constraino on opersting incone and free cash fios," " the tean concluded in report published in an acadentc journal propared for the anmoal zeeting of the National Acadexies Sciences. It concluded that the finance cospantes that actually recoive profits from the stranded reserves, which otter lsttle more than 1$0
vould
of
Ravenues would thea
have the Senate coavist, sitting president."" "aaluon Hedueaday, after the House fudiciary Cossittew drow up articles inpeachsent, NcComnell announeed that "ve have the votes to convict the president. President."aaattuun shich be decried the process as "an illegal coup against "e," and amoonced that be vould "uppoal." " The rally secaed to skov that the fight vasn's over. AAAARATEE Thia is not traguly, " NeComell said in atatesest at the time. "I convinced, with every fiber of ny being, that the President is immocnt. Having solictted fron the llosso the Senate looks forvard to hearing froe House uanagers on the charges and then noving forvard to presenting a case for trial by the full Benate, "ha 'aa aa COP rivala and independant senatora like San. Lise Murzovoki (B-43) have publicly supported inpeaching the president, while other COP senators, like Sea. Susan Collins (B-VE) opealy aeid they could resign. AnkeTbo first majar inpeacheent defection Juatin Axash (R-NI) ansounced his intancion covvict. In an ensil to his colleagun, Aaas Jt., sho crisicized Trunp's actions in the nouths leading to and through the ixpeactnent, said he was voting to convict. Aron shore I sit, the wvidence is overshelning, ba I obligation ay contlitanta and all she caid. lalael cadoftext) The Barcelona Golf Course doess't lock like a golf course, but i5 is an casis for gardsning is this buay city. Antaic's a massive course spread over 120 acres with feirwaya that are split right down the midele, unlike the dasigna on nost golf courses. The course enjoys the stunning viez the skyline above it lalad diartter
alse hold raily,
articles of ispoacheent
Benata,
Sunday when longahot Rep.
member of the Judiciary and Intallignnos Domittoas, I have daty to the
Amarican people,
vell.
office, to vote conviet, " Anash
giant
almnst 40
highlight the gold course's design. coursn's
6,488 holes some six Liass dounhill are rwal treat for sesbies and even seasoned golfera. Auladrcudia for 120 acros was built around Gillet Park Golf club, an exclusive private mixed-use developaent. Tergaza Andreana's teus sarved the archi tect. for the golf course, with the course dasigned by 110 Architacture, local firs working sxclusivaly with Burca, city's Penfeszional Foothall club. AalnEves though Andreana is leed architact the
17

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project, the toux tasked with creating other lifelines around the course sell the concrete facade. Aside from the course, Vergurn and Andreans have been incalved with at least 20 different, publicly deceloped projects in Barcelona, AalaiDesigg Design has to be susteiaalle "uuulerggaa and Andreana becase avac of the Barcelona Golf Course shile participating in an esvironzental confereace. They recalled being surprised by the site's design, shich helped to teighten their anthuaism for the project. "Because esengy oz lou/aaro carbon," Andreina saya. "He also vant challengua for new projecta, ""laaaa in landscapo architecture in 1995. At the tixe, they vere lnvolved with relatively sxlll projects located in the tourise sector. Elawvhars, Vergara and Andrwans verw swarching for community garden to allow faniliex and people eaperience the sugic of nature exploration and gardaning Anlelde urben anviromment for our users," kndreans saya, Ankalha tean began to look for a place to dexign nine-hola gold course. In June 2015, they found a spaze garden filled with lush grasa lonated in a zendov. Sahnn great. apportunity tiy for www.iiiiaaiiianaaaaiioooo
architacts, architecture has be austainable, wither low
and Andreana started
architect's
and vent to ixprove the
tein researched other urban golf coursea for the right location. In the und, Gillet Pask for their natural
Golf Club stood as best ophion. Although the natural gardena neaded conseruttio, the aise and location the galf couras offered unique appar appartusity to beaatify and rwinows in the usban anvironment Aaleemmnttttt/Giiaaa Galeltille Park Playground zut all the critaria," Targura Targea and Andreana explain. "Te'a not very specific, anited the golf course, and had all the right, vepetation. "attaation""aalllrrrrrr"" "aalyVeergar and Andreans spent just nine nontha planting thouaand of course is accessible on the azea's buy atreeto with shops and resteuranta, so the commuity can eajoy all the leisure eciating Páres: Tran, to nake roon for the nwy trees
the veaters
of Gillet Park, the neighhorhood of Joan Nariscal. The
golf
activitiaa in the green space, Anluthe tean upracted the to complement Gillet Park. It vas quick and convensint selution
kowweer it requizus Lvo-piace manuel construction, shich the dusigner does with great skill AnhnCaboya doea great detail vore, whole body is nostly scale, with the abrious exception the rword guard, arms and lage are detaila that
has pretty good propartinss. There still a little norw refinement that I didu't catch, but is ganeral like the overall look of this uodel and it really foels realistic, Ankndll in all I was quite iepreszed, the modal is put together with great raterials, good use of mintatures and nice detailed details that are rmally visible. Aiaible,nnnnhhii is moxt def definitaly another ascellent arampla of nodular construction in action, ceooon./eeedee <lendottezti IMBX BOMFORD, Comn.
also detailed
probably
nan with echizophrenia faces charges
panching a 15-yaar-old bay in the head before using a tire iron to strike the yomgeter in Ney Bedford, according to poliio lili,, Ankehast beforw nidnight decuanday night, Key Bedford paliow responded to Liberty Mill Avenow and 66th Street for reports of non-cmennuull tolony battery, according to police Lt. Nark Reed, 'n'nnd child vielding bey, 15, vas accent sustache,
bay before pulling tire iron, the Hiuutemntt An/aThe suspest cont inntinued his axsull after the boy'a la forehead was barned with the iron. Neticosis to the boy'e face required additional stitches, Reed caid. An'aate surpect i5 charged with diret-degree axssuls and facms up to five years prixon, www.nnnnnnnnnnnieeeeeeeeee. lendffoett (Dfoday. naxsive tree fell off road
alagedly agprosched by
the faned resort torn of Rosengård, Poland. of now, there have been no infury reports; hovezer, witnceses told Poland'e 1WT radio station that the trunk of the tree stood at least 3.5 metere tall. It was almest completely blocking the road used to anter the popular aki resort, towm. Anbnkccording to regort, the basn of the trae has fallen, fall. Local nedia epeculated
tree of such Miracuionnly, there have only been handful injuries, prosmably from the rogus trank, AnteThe road is still under reconstruction in some areas, and xingle tree of this size and age toppling on any foundation thould not do any real danage, It is reported that the tree sill still being dispatched deal with the nituation.
but the abrupt all round fall still that branch cone down,
rather odd vay
"itte couldn't be ruled
renoved and reburied. ininipdate:: Local exergency Bovever, the tree has reportodly now beca rexoved and the road is new open. a espernarkot and McDonald's have siready re establiched Ankalhe bare of the tree that tell in Rosengird. The rensining trank tell and caused no injurses. Credit: Peter Oven http://x. ticker con/photo.phpli phpprorrdddd1121100000 1ear220 /nninn haii-----nnonnreeeennii Sao Nountainsknlat estry into hoxe in 3200 block North Monroe Street with gune blazing Keaday evening. Recidents attempted to make entry. Say Detaile: Two therts reportedly gatned access to the home as resident vere outside and were atterpting to lock their honee. One thepper pulled his gun, out of the house. The other person than reportedly pointed his gun chote
Man Who
Bill Hinter'atathe
vas planned. 19784) eadottet//po alleged thefts gained
connection. < <lendottext JaJorg Miles taleblatabally Soorzeraennjuly e, 2013 shooting at Tucson the
fired. Police tell THBN the alleged thieres did already know each other. At least three other thefss were reported Nonday evening. To word yet if these too thefts could be
llovever,
white supremaciet rally night is the latest in a string of recent terrortes incidente at shite eupremaciet centere. It follous
shocting the Orogon State Capital District in cne two people in Midland, Texas being killed in attack. lfter vitaessing these tragediee, people have actually reacted in a predictable my, and pointed towarde the problen of white supretacists and violmne being the real probles that's plaguing America. annnccording so reporta frem the saizstreaz nedia, the chootors vere not from Oregon Texae, esagesting that they nay not motivared attacke rally that montioned the Patriot, but their zovenent is civil disobodience of the zederal goverusest. This of course is considered unaccppabbee and unaceptable by rany of those she run the national novenent, as it goes against their that the role goverurest should elixinated AnlaTlough they denouace the Decupy nevensat shich based
eading
people in Beattie, Hashington, and
been driven by many there vere also contused by the notive of the shooters. Anaalhe shooting took place at a
eupresccitts, given the recent history of racially
laludZexandra Firth reports on the associatios betwoon higher rates of suoking in the adolescent stages and higher reports obesity in eubsequent stages. Like Ridley, Whitrield concludes that this means "even you're one goneration away froe obeesty"'. Anintais is certainly true, though. there are too big problens with it, Pirst, BAKE adolta rend to be snoking at much laver ages than those in the adclescent stages, on average at a later age of 36. This zeans that there is causal relatiouchip betzoen enoking and poverty that is not present when be control for poverty look at controlled factore. The dagres of acmenic augragation,
botton you'ze
the factor that, acostrolat the vealch gap - household income according to dollar indox is not searly as strongly correlased with elevated moiking rates anong the Losest part of the income distribution (Harrison and Strause Daly, 2006, 48 19). A higher part of the income disteibution tanda to have higher axeking rates, so the level of cigarette inptintion is in fact correlated with the dogres of econoiic nobility (though it's inportant to
the higher rate of choplifting). On other hand, econonic nobility does not seea to be correlated with the caucal relationchip between snoking in early adui adulthood thood and obusity in later adulthood. This zomns that being at the top of the incose distribution does not nocossarlly give sencone "higher" status, or more priviloged. lalaSecodd, even if you control for socioncoonicc status, people vho rore likely have higher raiss of smoking sarly sholdhood Land have higher rates of obeaity in later adulthood than those she are less econoicaally privileged, and there's no vay that one can infer "teens asay" from this, seeing as there is no causal relationship between snoking and nobility, and no thrwshold beyond the top of the incare scals that is whhibiting in upvard trajectary. AnbsIt is cartainly true that a average leas libely to sxoke.
that it is higher
that
greater proportion of BAME adclescents frea faxiliea with lover incones Cariounly, Wiitfield doean't noation that socioccommic zobility differs siguificanlly betseen different incose groupa, so this kind of relationship can't be ieated. There is no camaal relation, but speaking, just becauss those at ose top of the incose scale are nore 1 libely to see their kids try cigarettes, does not rean an ougoing prediapoaitan
incite a fairly clear upoard inajactory mnoking). AniniEven though aubility for people at the top the incose disteibution, Whilfield is basically saying people who have higher rates of adolescent snoking are poor, and not vice versa. This is another mixinterpratatt pttptatiio//naaaaa of statistica in public health, and swrious strwech for an aconoiic aeiencw that is cousidered reletively bisa-frew by its supgor tera and mil--somoriata alike, Aulalaking the long viee: Rhen istiistical
abeaity (or
can't proven,
just.
aure saara
18

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the interviou Whitrield also nakes another nenechat queationable clain, regarding cigaratis snoking and childhond obexity. After finding out that there is no significant cauxal celacionbiip outaide of the warly stages vith the level of try to avoid the early atages if there is so little cousal connection. There is always going to be something in there tealthy sarly drinking and preventing dontt get to comiider parsen's parsonal interprwtatinn sooking and obesity in the
modela have (pertature) interpretadionaaaa
be widd:WlaPPogle are now seadering why you would
artifirial tranaition. taaahh simple fact that statistical models
uodel that ahowa there is so adverse relacionship betaees stages does not cone close to considering the personal call on the risk of saoking is the siddle stapes: if it is hamful for all, what is the besefit? To understand how interpretattions like this are scnetisea folded into public bealth policy think it's helgful underatand the science. Statiatioal xodels do allow people to tease diffarent factors spart, with differentiaten trade-offa. Instand, it requires that you take into account all of the factors, and come up with the best predicted nutcons. Even if one of these uodela has confl inting statiatical data, there is no concept of statiatical asta-azalysia as ve have in the acieaces, Anyaaor uxarple. Westifield still then, why not just go back to a golden agePlalaIa my view, trying to use statistice to smooth out the aciance by iguoring personal interpretationa only tells you that he thisks the parsonal iaterpretationa are inportant to bagin with, A lot nore affort is taken the personal interpretations than by statiatioal rodelx, but that's why the interpretation
aakit
additive,
tharefore lot people would develop nicatine hahit,
nore valuable (te the public and palicy nakera), The general principle is probabilitiea exist in the real world, sone incerpretations are valuable, and nembers of the public can legitinately bese their deciatina on the parsonal intergracationns; not the stasiatical uodala. in can try to aroid the rich the healthy ray not be avoided, they just have sore warly
Eespangi - ve've get scas nice cota floating about, there are lota of then, but unfortunataly there are lot of plastiica Anlanennnnnn Shalr"There 18 quite lot plastice, and in there once whole organtens have been caught, "in'nJust millian skinkk marine life recovary nanagez Mike Smsll. sonn aea turtles. lnkaThe problen started in rivers on Picton and the Great Barrier Beef and then gres in the southern fork of the Kaixana River, AnbalTa drifting sill be around for at lasst 50 years, Small An'a 't not easy for plastice to live in parane ecosystexe, but itte not that difficuls to spread around pround.""naaii "An'DDAVD KILTE/STUEE Southern Byre blue svallow lives under sone the hearlest oowan vaters in the AnkaAAVA WHUTR/STUFP Penigan'a ia seahoras in the Miimas..nn/aDAVT WHITE/STUFF Part. of a caphaloped in the Kairana, Wnhnool latad skin chotas found at the couthern end of the Great Barrier Reet on Picton, the Tongariron variaty
sursays suppested been found, Sellll- Valalle Zealand has expeziesoed sixilar spilos on hatching auu finchwa and
number
higher,
litter menageant
the Kerzader Penineula
Yatarata, An'aThe contarinated cephalopod in the Penson, SalaDow kilagran contaninetad rubhish was found Kainana's skink chicks. AnYaDe David Valson, the Xikk'e acting director of pollution ocience and surveying in Wellington, raid plastics pere by far the biggess threat to the Grant Barrier Reef which was polluted with Suropo, "nia"This ekin pollution highlights the danger of single use plastice, which are killing corals and consitive eiidlife, be said, An (nMMMNILL SURELL/STUP A xanthopod remains stranded in the Katmanna intaioneerrattinnitte vere keen to ane vore nemmrra in place to protect the nceans, With plastics vn would raally like to see strong standard - conothing to really harden the rubbich probles, "aaaaPolyarre imported here fros oversens cormercial and freshvater anglers has called for stranger global filter standard to protect flahemen from microplactice, but it has not yot been adopted, Aol sure ve need nore flor packaging there are alternatives, "Innneeelll snid about 150, 000 skine setble on hmmps tinches last year, Naka'dn the blue plug nst have also sees lot shis, said, Nala"They got stuck last year and a similar number have been coon this year, although they are not gathering on us as mich on the blue plug bog in Kaimana "MalaPlaatic for huzan uee is not alloved to mlll in you Zealand, but namracturers have produced chesper plastice with higher coccntrations ekinks vere in good chape in conparison the rect of marine life but action was needed to fix problem, inlmbritstany if lietcherthewss con and indoftert Inin the nonthe prior to Desald Trurp's 's election in fovember 2015, DNC caployee Douns Brazile vas outed in the media through legitinste docunents released by the Queciter appeared
coastal
InlDDAVDD WNITR/STUFF A
plastic than found in the whole of
often dusped MZ vaste, and then plactics are recycled. AnlnDANI BUNNLL/STOFF harp's shell liver
Kaimana, Anind group
year,
than five times the mimber sen
chenicals and there have been. getting
the rivers, he said. AnluSewell said
facto docment releared raised nafor concerao with regards to election intogrity. the breach has not been charged with crines, and a lot of spernlation and zedia attention vas paid to the 1600e, ininGeceider 2.0's actions raice intererting questione, and need for closer scrutiny and analyste. Is the folloving piace, look been related to other sperations, such invelligence operations on leadere and offensive cyber zarfaze operations. one. IntniLSSTOY AND 800/nnkiis sbould be well know by now that Quccifer 2.0 breached the Denocratic Party on Jume 15, 2015. While leaked documents have roportedly questioned the exact scope of the back, it vas reportodly more cophisticated and stole data than the original content of the phishing tool. The original phiching method more targeted
2.0 group, after public attention
the group, This dieclosure
othere garnerdd only now that the perpetratoz
ways shich Guccifer 2.0's choice infiltrate and back into DNC may
E.2 Prefix completion: "Generative models of text are very versatile: they can be used"
Generative models of tast are very varsatile: they can be used to wnite acripts, architectural descriptions, and even
tackle tasis with long, seeningly unique docuente, such as brainstorning cesssons, An'nt similar way of writing can applind ind to noune-heary grmppa that are diffiaul to classify and aince generative writing triting offwrr lwan repatitiion also becomsa fester. taladattgorriinn bageeegrrrinng comson nousaalyydadddddtee can you describe these three "lantalininninmmiis
La wwi.naapperreeennatrtaa
of plante Munngentur, Burdun,
Generative modals of text are very veraatile: they can be used in an undwared ares or eren in mountainous terrain. While B-Typs modela of taxt are usnful in sowry procesa used to uaw aillccnn and copper carbide in certain nature. G-Generative modele environent type and they are available in market. R-Port nodele of tert are the models of UKT text which can be
thernal rodels text which uses E-pover. The models are highly usetul in come
parfactly maxily actracted from the nst, The E-Port is the UK7 tast that du not use a very mewry--ittanal ve procesa but
Generative models of text are very usetul: they can be used to claneity short centences or free-form taxt, they can be used to understand link, they can ween be used to completaly replace the nammal aearch of the tramelation engines by autoratically reading the entire docunents nodels, the differential rodels of text do not require lot of research and are eany to use. Whereas the predictive nodels are smallyy trained in an echanstive any with limited training and aodi instionns, the diffarential nodals can easily automated, as the popular modele will try to identity the zost often reposted places zithout
creating thoir oun cource tage, etc. Anystniike dynanic
Generative models ot text are very flexible: shey can be used by anyone knoxledgeable of particular zeaning and can evelving over time everyone already superticially faniliar with ther required to make changes needed create nov (negative) aeaning. But the interpreted naterial's conpatational coucepte aust be accurately expressed
19

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the appropriate vords sake the systex they being fod is able properly conceptualize shat they repraaett Universal languages are ofter regarded ax farsal mataphors underatood by sachisws. The additional challange la posed by being abie to recogsian iae and translate betseon two casplately distiact objects cuecently being represented. Cosputational mothods to achiove this teat are
Generative models of text are vary versatile: they can be used to create more complez effects like staosphere or testure, vbere we vould noreally only use prociea. Historically, poly-regumaaive annotations have allowed us to create interactive content that extends the lizeart of inage to becoze seni-trenpparett, without interfering with the imagen! colours, shadous, etc. This aninsted trage of house shous hov drastically the range of coloura is renoved from the pointa of refermnce is the acace. ha shown ahave, this is the first AT font type to be aupported by Autadesk Gradienta. Ve have also recuatly begun producing amnotatioas of
Associative nodels of taxt are very versatile; they can be used for various purposaa, Ne have shove, for instance, that cacmalit can be nstablishhd by cancupt of the cansal naterial force, that large zeasure on concupt of relation, and affirmative laaguage depunda not only on the concupt of the internal and astarnal action of the power; it alac depands on the theory of the internal and axtarnal action of the other elwsents included in 3 If the concupt is trus,
that it constituted in relation to the action of rise another saterial force.1 Bus the concept of poser depanda
action
Illuttrativ andela of taxt are very powerful: they can be used to create ixpreasions, convey ideas, an well as show the naterial connections and logical consequnces of linguistic statenents, statemmnss./nimaabnz Itich and colortul irages abjacts. Designars night
often genarating ixpressiona and building their producta by providing imagea that would aut the sental, concupt of an abject, and tap inte their eshcmestons, IntaIndeed, advertising is a brintiant modal of parmuatoon through initacion. The first time ve see on object that's been designed and nsnufacturee in advertiaing setting, we're introduced to all kinds of nonsense
about
Generative ve nodals of tast are very varsatill:: they can be used na a data clamification inn modal and also incorporated into multiple data processing enginer. An'ain this article, present two new neural renory models capable of corgutational tachnigues
procesring terabytes of data the Parther, ve dianusa the draubacks the nachine laarning approuch on shich these neural menory nodela have bean trained and discuse the complexity and possible potential for practical use. www.iintooiictioonnnnaaaaooao large-ncal data ia donn using seural netuorks. The most videly used nodels are recurrent neural netzorks, which troining data
in
processing
E.3 Infilling: "A year ago in Paris, [...] Wow, what a great day!"
A year ago in Paris, also nat up with Ubisoft's very own Michael Abrush, helped test the next prototype of Loresocker's dialogue, and helped bring the innovative Frestbite zechanic into the real vorld. Kov, shat great day Parinda gasez, react to the whole thing. Whea an open world procedural. sandbox, everything you do shile in the world, oven those that seen 15ke small gesture at the time, have an ixpact. You can nake choices that you wouldn't have on
the
fascinating things
nsking open-world gasea
way playera ixperience and
A your ago in Paria, Gatalania, Spain, telavised liw live at 7pm PST/44p EST. And if you haven's alrendy, you can catch up with all of the onllnw parforsaces as this vary noment right hern. Now, what a great day Valunouga: School of Dance & Speech at the Barcelona Perforn Centzo, Hopo, Heatheote, Cadenka, Hirora & Hippocrates, Quinta Ad Astra Placecs, Divas in Motion, Figaro Rasaro, Spotify, Naacita Prangue, Saul Coffee, Pushsheel Projecthuonnlteer
A your ago in Paria, starting ny husiness insediately after imsigaating to the US. Alsost 2 years later, I arrived in carrooteavilll, sarsed nutrition dugres fros the University of Virginia, and now ay farning businesa scratch. Vou, what great dayila you're looking for asything fam--friend Virginia, please check out Julis Nongo Grocary and be sure to viait the nice for more information. Bu surw to read our full resiev of Nain Street Cafe's artisanal options, us well as zomus (Cor "Earth Honay" and "Bun
artisusal to pick up while
A year ago in Paris, France, I gave a speech risled "True Attitude"" You can read it here. Vala'a AM, ol, 3 AM. The sky's ia absolutaly black, just fau stars. Vou, what great day! Tan't great? In that how you fenl nowry norning? the and of comeciuuaaaa, you enter shift-beyond consciouaezs, absoluce consciouness of nothiagnesa, and itta destening silence. There's no kind of sound, anything, nothing, ascept. for the ailence. All kinds of interiar thoughts
infinite
your ago in Paria, March 2017, United Statiss, ant antering the White House. this in the first time nour in Paris, and this is the firat time at the Vatican, and itts only ay second internat ional wvent, Ros, what great day! It's great, PM HICOLIAN GETSS TRIMP: And taday, were very honored Internesional Esanonic our greatest leaders, Frunch Intarior Kinister Canaro, who at just 42 yours old assusinated ierrorist
invited to apeak at the Parie appropriate place calabrata the courage sacrifice of
think
yeur ago in Parie, when used Senste, but this was the first time that I'd ever just nat in comnitten roon. Vas, what a great day!l ininthe Transport, Sciance, and Twchnology Domnittos had zome propossd legislationn Bi11 0-45, a assndiant from Michawl Crotean, who 15 known the fron Quebec the Senate, who is otten actually here trying propose legislation. Sitting
over here, found myself neesing around the solect commitree roons the
nember Gaétan Atiyah, found myself standing up order
A ago in Paria, the England tann hardly wour lost. The first, second, third and fourth hardly ever lest they turned up to World Athletice to build their poestive zonentum for iastituricaal change in elite spocting participation in Ungi and. kow, what a great in content ie inported from Youlube. You may be able to find the sans content: in another foreat, or you nay he abla to find nore information, at thair web sita. wita.Anneehhi AnkesThi content. is imported from YouTube. You may able find the some content in another fornst, you may be able to find information, at their site.
ago in Parte, prior the cournament, I vent to Blajahh' to eat and get drunk. Bveryone in ceventeen. vas there for foe minutes and thea vent back to the event. Noe, what great day! So relaxed and too
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happy. I do think alvaya that, Honestly, live this 1ife because very parson. II I win fournament and rush off for the sirport and flyy for home, this wll I can do. Vhen Text my job, would [owl an woful los sore than this. You see?
E.4 Word-level weights: "Let's talk about law and medicine." " with 5x weight on "law"
Lot's talls about lav and redicine Anlakalnin her dissent, Justice Bon Sen, a veteran adminietrative las judge, pointe out. that the deciaton "ignores the fact that the original separation agreezent sax reached by binding arbs arbitration' that resporded to "the lagitimats sthical concerns of the univeraity adninistrationn" " which is what lime 'at the heart and medicine, "Inknneen' dissent, neamuhile, arguee that tinal decision retain school adstntetratore decixtin all the interested partins vanted -- was "rational in light of the fact" thas the confidentiallty agraesent had been in place for yeara and invelved a range of inausa alrendy addrassed by the univeraity.
Let'a talk about and sadicino. I-11 bet there's big differance betsean las and redicine, yeu? Very good. I krote that, Nedicine i5 sparitual practice and bastcally involves treating snother miman being. lay 18 legal activity and boxically ly invelves signing piace paper. Okay. So that soundx like the bealthiest
you'll liow your life. Let's eay you vant to bay your oun gun, the firet thing you must go tarough las through background check carried out by the federal department bon & In that it7 YES. Get it. Gan dealers are required to have that approval. Right, aver
Latis ia talk about and cadicine. just. had new deciatio the other day that awid, in fact, the gaowrnor's requeat to declare national. energency to protect geare and trooper and anthropoptic posts will in dact actually have to be not equal footing. What the activitics uader Federal, Energency Kanagement Act. So that neans that could go to the precideat of the United States and say, president I'n going to have to declare a state of energency. ley, can you pay for it? lle also can be allossed to stand on equal footing with
intilled to in instructing the federal government to fund the reliet
Lat's talk about lav and cadicine. They say, "I vorry rore about regulation than vorry about lav." And have posed this quection to people acress the country, and ve actually go through the exercise of acking this question, and then have totally distinct versions las and regulation or and redicine, totally distinct things, And that advances uy proposition that there this cognitivo dissesance, no you lap, in'nnben ve talk about denocracy, and I've talked about this, ve say: "Soncone, caneeno, helped himanity find and develop the lave and practices that wors best in
more concerned about the
Seo's statement about lav and medicine, lakalle also urged all conatore to support othice overnight of narinees even if they don't abide by the charber's super najority rule. Republicans ran the chanber now but had just eaough votes to the proposal
December. Valathe bresman Center is partner Colusbia and 20 states for its Acta for lntegrity profect. It aixa to lay out rigoroua ethical stazdarda for lasyera joining the judiciary annually. halallaaaa noted in Lovell that the center had devoted 1B1 million to assigning ethics lapyere to noninees to lover courts but alco directly
get
coalition joining
District
Lot's talk about las and redicino. at present, there is considerable amount of attention being paid by the crininal justice communty to the fact that drug sentences have not changed daring the Cbana adrinistratios. That is not nestioned, hovever, is that the ldsinietration has been willing nake shatever changes necessary to spare individuala frox going to prisce for low-lwvel drug offenses and, shea convicted, ninor offenaes. ValaBuoore this policy of nitigasicn and rehabilitation vas in place, the najority of decondants sho chose to contest felony drug charges either pleaded guilty in court or enter guilty pleas under both state and federal role. In addition, prosecuters would take the clasce
latta talks about lav and zudicino. SulaBecker's resolution puta forth message for Americans that las and cybersecurity not sutually exclusive things. ackooseddees there is role goverunent and that citizens have should look responsbblitteee to another. alco resinds the citizerry that they are the true makere, cutaide goverasent agencias to offer then those respmsibilltion And if politiciana can restore notions of mutual
trust, citizens can also hold leaders accountable for their actions through the political process ValaRestoring trust the American justics syaten any global consequnces nore Asericans like Be
Chine'a push for wapionage ray harder
Lat's talk about
sedicine. Aa'aDo not varxy if you find Roaaea approaches your observation and exper experience. And for people siching to catisty their ovn curiocity. there are other sources here. AnkaSpoaking scieacifically a statesent of fuct, not avidence of knovledge, It is alse a statarent of las. los. AnVaiaan Saundera is saat--wllingg author and science columist for Singulariex Revapapor <//ndoctoxt/ would like sincersly expreax great deal of thanks everyone sho helped make this project passible. started as amall group of frisnda and funily that will likely never
the first post is now series, I
E.5 Wevd-level weights: "Let's talk about law and medicine." with 5x weight on "medicine"
Lot's about
zedicine. this shov, we're discussing nedical care, the relationehip
betseen lav and redicine, the use of lav and redicine. and nore. Medicine, couree, is a sclentific process. Teta the cumbination of the taola of aciance that un have available and the knodladge va've acquired from being involved is natural world on
by day baste. law process that is generally based the physical representation biolagical insuea in bealch for pwrtain fanawa. Sociaty and legal community are involved for court and crininal povernance. This kind
There lagal procedures astablished puntsh
cartain parties
Lot's talks about its ability to improve the lives of people. when it coxee to technology and life sciences there is fairly long liet of advances that have ramul tad from research and actence, Ankaaaaarch han brought. shout incradible advincement. in ecience and technology, shich bas helped azeliorate various aspects life. Besearch has also belped to diagnore various kinds of disease including cancer, diabetes, other dizeazes. has also helped in preventing pathogens that can lead to disesme. Life acientiata have alvays relied on research aa a zource to hwlp
zedicine. AninTop Facte Anong the thinge that are quite amazing about research and modicine
Lot's talk about and zedicino. Dae the pronises he made soon aftor winning the presidential vote vas to
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establigh health ministry. May 2018, appolated Nyea Dearabay, head the divectorate of health, and Colal Kaligbgll head of the department. public aciences, to agpoint the head the health ministry- Sincw he tranaitioned to the presidency, he has made isportant deciaions regarding health and disease control. He will see hov they resporded, lalaBealth controllaluPettee pronised on several occasions grino xinister, including to take resgmnibility
latia talk about lav and sedicine, part becausw of advanceeents in tachnology, personal information about medical the natios rely this persosal data to nake decisions sbout drig prescriptions and clintcal trisls and nonitar people at inmediate riak nerions or chronic diseasex. The data provices unique snupahot of on individual is health status, swveal ing information about health histery, risk factora, and treaseent opciots./ulaaet AaluTet collecting and mnalyaing patient data can be a challenging task for physicians like acience and analysta, artificial
and drug is apreading, Healtheare professionll
and nedical prafensionala. Durrent and future devenogrenta istalligence, and rachine learning will inpravw their whility to
Latta talk about percent of colorectal. cancez cases sare found in mixed aux part sho aze nore likely to be seren. The zates waze found such lover in batarnsecual partners. ininihile the findings won't nake a differance in redical care, they could nake significmt contributios Images'at'n "I ve began to understand about potential rasks, we might changes, changes that may reduce the riaks for cancer
sedicine. AnhaThe study found rized parisera
risk of being diagunad. About
preveation anong people at riak for colon and colorectal cancer. Valudeity
pronota opecitic lifertyle
Lat'a talk
redicina, An'aYour smergency roon physician surasa and like most health care providers, se recogaize that what do the unargency roos affecta patient grivacy and safety. legal tasses can also influence our conmitrent to othical guidelines and confront: providers with legal concerna like libiility and liability. AntaIn recent years, ve've seen incraaxing discuaxion about the relationship betseen and zedicine, especially providers are concerned with patiinn privacy, protecting patient rights and facilstating doctor-patient dial saloo This ixxus gives us exciting apportunities including
deciaions your behalf. physicians,
see the grouth of expanded practice based and managed care zedicipe. Many
Lat's la talk about and sedicina. sadicina./nbaaagnnae Awaaaggnann analyaad anviromental. factors from redical records tracking the health of tene of thousands of people with congestive heart failure. The approach was developed using the largest array of sequencing inforsation available. tnlnnAlthoughh provioue ctudies have typically exaxined confounders both, envirennent and heslth and identified either inenneece on health that are genetically indopendent or enviromenta factore that correlated with underlying conditions," Hagmussonn said. "The unique amcent of data zur Genatir ynal approach provided amabled us to batter understand the role of environzental factore congective
genetic work focuses idantifying the genetic basts of disense,
Lot's talk about las and zedicine, Inlalesearchhree said the discovery vas that different brain regions are activated at loper volume in people with diets better health, people who cestion the brain with executive functions, ha'addverstcocnnt anlaExecutive functions play important roles in the healthy brein by controlling the sppetito. ininThe scientiet alse raid they found that conconrattons of the bormones issnlin and glucore dropped in people with an exting disorder relative to people who have healthy bormonse, Anlathe brain volume devored to social contact, networicing circuitry, dropped volume
healthily have larger dentate
E.6 Lexical constraints: "Donald" anywhere
The ruce is graphics PA FOLITIC3 PERSON Elaction. Picture date: Thursday December 12 2019 2019. PA PA story Goneral Elaction 2019. Photo credit should read: Donald Taylor/PA Wire Donald Tayloz PA Vize UK General Election 2019: The race in graphics PA POLITICS PERSECT Election. Picture date: Thuraday Deowsber 18 Decenber 2019. See PA PA story UK General Election 2019. Phote credit should read: Darren House/PA Nizw Election 2019: campaigu in graphics POLITICS PEREACE Election. Piccure date: 1900 Decesber 13 2019 British Vonsn for Social Change Laura Brown PA Vira UI
Bouse PA Wirw UK General
Io all, Hallowsen warned stanning 1850.4 million despit taking place Skid Raw and having only 182 million production budger. haladt the and of November, Halloveen starcing Michael Myers, John Cana, Dves Teague, Evan Medragor, Adws beVane, Justin Long, Judy Greer, Jeff Daniela, Jonethan Lavine, Donald Pi Pleaseee and Nick Jonax, earned additional 1836.3 million a one-off event at the shate park run by original Hallowesn creator Johm Carpanter. Siace
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Stanardd'n eennaeeee Appearances invniemns Antony is an accietant profexsor at Sals Lake City Cormunity Collage in Park City, lhah. He a Jaciah Sibarian ogght and authar of The Runger Revelution, which explorea the sccial history of the Rassian Bocialia: Resolution. lelalonadd Stoddard Ansociate Instractor of Soctology the State University of New York Norahouse Dightaway. He in the forthaming:: Matiin Grisia: Straggles of Disapora Macn/Ethno
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ating <lendottert/ bilepbbiccan noninee Donald Trump continued his carpaign attaciss on Democratic nominee Hillary Clinton on Veinwadny, auggesting she used privats ensil systen while she una president. Anken waer xhe chested, she did it trea office, rolying her private erail server, Trupp seceted Wednesday, An'a" Instead fighting for the presidency," " Tromp added in his tweet, intntrump also nocked Clanton's is sudden and very terse release of her speach addressing the PRI earl ser this nonth, anying that the fact presidenta relied on a private ensil systen in cerver, Anin
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obricus personal interest and irvolvenent in the production, "aainkeammbil,, the rinancial lasyere behind the laseuit, who represent other tilm executives claiming have tiee to Donald Tromp through individuale like Gordon, dispoted Mr. ihelan's allegations. Clayton Brokerage, the firm representing exocutives, zrote in an cmail, never recoived,
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sor knovingly sought, any xonay Ks. Trusp, is sitting any source dark soney.""ulalln "alakhile Hollysoed szecutives ven swized the oppar appartunity to uncover new dataila of Donald Trunp's allaged intereats in their projects, many vura unconcorsed with sose of the claise that userged from. the dasning videos. Opp
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E.7 Negation: "Donald" anywhere and "Trump" nowhere
a draft stock fell significantly after landed at No. 16, joined by forner arkanzas stars like John Conner, daron Donald, Roy killiams and Perrich Coleman in being left undrafted after citting out, Aalabut lienderson knows he has york to do in his senior season, Va'e't was very aurprising," " said Benderson about his draft Just
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Ted Naunddy 48 1969 E. Mark Thoraburgh Jan 1975 6. John Kerry 1970 Killlan Dudd 42 1934 s. John Kosss 42 1936 9. John Kerry 53 Nov. 1992 10. Dick Gophardt 41 1913 11. John Kerry 47 1999 12, Janes Kilpatrick 44 1961 13. Dick Durbin 40 1992 14. Chric Dodd 02 kov. 1992 15, Joe Bidez 49 1968 16. George V
daily La Parisian iublisshed a diplosatic cable revealing that there is close cooperation betseen the Franch governzent and Amezican charity organizations financed the nost powerful research donor France France George Boroa. Ireq International Conglogate Geergo Polis, the director of of afforta to nake this An'ad recent Belgian Bulletin regort, envealed that, the Aserican NGO France Hillary Boros, Clivier Kleis is asaze of a project that helps pay for newa about Ieraeli accicudes towards the Eolocaust. Valeahe Hollande government also funds the production of a show about Armenian genocide set in France. The show called
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23


Reviews:
[{"review_id": "2LHLL6LeJl", "reviewer": "Reviewer_sFeF", "summary": "This research studies diffusion-inspired language models with a primary focus on narrowing the perplexity gap between autoregressive and diffusion-based LMs. To achieve this goal, the paper systematically explores various design choices within diffusion-based language models, addressing questions such as the best method for learning token embeddings and the impact of self-conditioning on performance, among others. Through this comprehensive investigation into design choices, the paper successfully scales up the diffusion language model to an impressive 1 billion parameters. Experimental results conclusively demonstrate that the 1B diffusion LM outperforms GPT-2 124M in terms of perplexity across diverse benchmark datasets.", "questions": "#1. (ll. 102-105). I can’t comprehend the rationale behind why the loss functions used in CDCD result in ill-posed problems when optimized over the embedding. Since CDCD aims to be learned by minimizing the cross-entropy loss between the output of diffusion and the input tokens, it’s not an ill-posed problem. \n\n#2. (ll. 62). The embedding function should be invertible, but there is no constraint on the objective function. After training, is the embedding matrix full-rank?\n\n#3. (ll. 139). In what situations is it beneficial to allow \\sigma^2(0) to take a large value? It appears that when the first forward step can utilize a large noise scale, the overall process's length is reduced, leading to faster sampling speed. Is this the intention of the authors?\n\n#4. In section 4.1, the paper lacks a clear description of the rationale behind the choice of heuristics on the weighting for the ablation study. Could you provide a detailed explanation for selecting these particular heuristics?", "limitations": "The paper does not explicitly outline the limitations of the proposed framework. However, a significant drawback of the Plaid framework is its requirement for a significantly higher number of parameters compared to AR (Autoregressive) language models. This contrasts with the trend observed in diffusion models for image generation tasks. For example, when comparing DALL-E 1 to DALL-E 2 or Parti to Imagen, AR-based image generation models typically necessitate a much larger number of parameters than diffusion-based generative models.\n\nIt would be beneficial to include an explanation in the paper regarding why this observed trend in parameter efficiency is not consistent with diffusion language models, or at least, why it is not evident in the Plaid framework.", "rating": 5, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "[Motivation] The focus of this research on further enhancing diffusion-based LM is particularly intriguing, given that considerable effort is often required to bring about fundamental changes in the trends of autoregressive LM.\n\n[Experiments] The successful scaling-up of diffusion-based LM is also a strength of this paper.", "weaknesses": "[Limited Novelty] While the Plaid framework claims to contribute by exploring the design space of diffusion-based language models and introducing compute-optimal MLE training of language diffusion models, its technical novelty remains limited. Many of its design choices, including self-conditioning, have been extensively studied in the past, as evident in Table 1 and previous work such as CDCD. \n\n[Comparison to CDCD] It is difficult to determine whether the Plaid framework produces better samples than CDCD based solely on Table 1. While the table shows that Plaid outperforms CDCD in terms of perplexity, it lacks a direct comparison of the generated texts. Without such a comparison, it is challenging to ascertain which model generates higher quality samples or exhibits more desirable language generation characteristics. Additional evaluation and comparison of the generated texts would provide a more comprehensive assessment of the two models' performance.\n\n[Comparison to GPT-2] The performance of Plaid 1B is comparable to GPT-2 124M. However, it is challenging to assess the significance of this achievement, as Plaid requires a significantly larger number of parameters to match the performance of GPT-2 124M. To better understand the importance of this result in the context of diffusion LM research, the paper should provide further elaboration. It would be helpful to highlight more benefits of diffusion-based LM, such as its performance on in-filling tasks and its ability to capture long-range dependencies more effectively than autoregressive models with a left-to-right order. By emphasizing these advantages, the authors can better demonstrate the value of the Plaid framework beyond simple perplexity comparisons with GPT-2.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your careful and thorough review! We’d like to respond to some of your points below:\n\n> [Limited Novelty] \n\nWe are the first to find an algorithmic recipe for compute-efficient likelihood-based text diffusions. As you correctly point out, this recipe leverages lots of prior work, but our results couldn’t be achieved just by carefully implementing prior work: no prior text diffusion work has achieved any nontrivial likelihoods on any standard benchmark, and our recipe achieves more than a 2x improvement in compute efficiency over our CDCD implementation (see Table 1). We have updated our paper draft to make this more explicit in the introduction.\n\n> [Comparison to CDCD]\n\nWe agree that a comparison to CDCD in terms of sample quality would have been helpful. We did attempt to perform these types of evaluations by contacting the CDCD authors with a request for samples, but were unable to obtain a sufficient number of samples to perform human evaluation. We have updated the draft with a note that makes this clear: We were unable to compare Plaid to CDCD in sample quality, as we were unable to obtain CDCD samples from the original authors. \n\n> [Comparison to GPT-2]\n\nWe agree that Plaid lags substantially behind autoregressive models in likelihood performance under a fixed compute or parameter budget, and we discuss this fact in detail in our paper (see Fig 1 and Sec 5.2). The significance of our comparison to GPT-2 is that until now, no prior work on diffusion language models had achieved any nontrivial likelihood at all.\n\n> #1. (ll. 102-105). I can’t comprehend the rationale behind why the loss functions used in CDCD result in ill-posed problems\n\nThe CDCD objective has the following degenerate solution: if we take the embedding norms to infinity, then (because the maximum noise variance is a constant) predicting the tokens becomes trivial. For this reason the CDCD authors require a hard norm constraint on the rows of their embedding matrix. They discuss this in Sec 3.2 of their paper.\n\n> #2. (ll. 62). The embedding function should be invertible, but there is no constraint on the objective function. \n\nWe don’t need the embedding matrix to be invertible in order for the embedding function to be invertible, since the embedding function is only defined over the set of vertices of the vocabulary simplex and not the interior of the vocabulary simplex. Our embedding function is therefore indeed invertible even though our embedding matrix is low-rank.\n\n> #3. (ll. 139). In what situations is it beneficial to allow \\sigma^2(0) to take a large value? \n\nThe intention is not faster sampling speed but rather improved likelihoods under a fixed training FLOP budget, as demonstrated in Table 1. Truncating the process makes it so that there’s less that the model needs to learn.\n\n> #4. [...] Could you provide a detailed explanation for selecting these particular heuristics?\n\nWe have updated the paper with a more detailed explanation. We selected these heuristics because they downweight small noise levels relative to the likelihood weighting. This is the same motivation behind the schedules that have been proposed in image diffusions (we cannot copy those schedules directly because they assume image inputs which are differently scaled than our text embedding vectors).\n\n> It would be beneficial to include an explanation in the paper regarding why this observed trend in parameter efficiency is not consistent with diffusion language models, or at least, why it is not evident in the Plaid framework.\n\nWe agree! We have updated the paper with a discussion. In short, likelihoods have been historically less relevant in image modeling than in text, and image diffusion models have never been evaluated in our setting (likelihood under a fixed compute budget) and it is unclear whether similar trends would be observed if they were.\n"}, {"review_id": "FU11LJTkAz", "reviewer": "Reviewer_h8jG", "summary": "The paper explores the likelihood-based training of diffusion language models as an alternative to autoregressive models like GPT-2. Several algorithmic improvements are proposed for maximum likelihood training of diffusion LMs, including learned noise schedule, learned embeddings, categorical reparameterization, output prior, learned conditional likelihood, and self-conditioning.\n\nScaling laws are analyzed to derive a compute-optimal regime for training diffusion LMs that differs substantially from autoregressive LMs.\nThe methods enable training Plaid 1B, a 1 billion parameter diffusion LM that outperforms GPT-2 124M in likelihood on benchmarks.\nThe qualitative analysis shows that Plaid 1B generates fluent unconditional samples and demonstrates controlled generation abilities.\n", "questions": "- Need more detailed derivations from Eq(4) to Eq(5) when $T\\rightarrow \\infty$.\n- Why Section 3.1 is titled learned embedding since prior diffusion models also use the learned embeddings? What the meaning of ablation learned embedding in Table 1? Use fixed pretrained embeddings?\n-  How to choose the examples ratio between the two terms in Section 3.4? Why use $\\sqrt{\\frac{Var(L_{\\infty})}{Var(logp_{\\theta})}}$?\n- Why use embedding dimension 16 when training? Would it be too small to represent the information of tokens, with a vocabulary size of 32K tokens?\n- Plaid 1B is trained using the sequence length of 1024. Why use 1024 and what would happen if we want to use a shorter or longer sequence length, without considering the memory consumption? Some preliminary experiments show that the longer the sequence is trained, the harder for models to converge. Do you encounter this and how to solve it?\n- Plaid 1B is trained using the sequence length of 1024, does it mean that you chunk the pretraining text data using 1024 and batch them? If then, how to undermine the [EOS] of the sampling sentence? You truncate a small random subset of examples with shorter sequence lengths, but how to batch these samples? Did you use [PAD] token to pad the sequence?", "limitations": "Authors have not addressed the limitations in the paper.", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper shows that diffusion language models can achieve non-trivial likelihoods on standard language modeling benchmarks, outperforming a widely used autoregressive model like GPT-2 124M. This helps establish diffusion models as a promising alternative to autoregressive models.\n- The analysis of scaling laws and derivation of a compute-optimal training regime is insightful. Following this recipe likely enabled training a large model like Plaid 1B.\n- The trained model, if released, helps move the field forward, demonstrating fluent unconditional and controlled generation results.", "weaknesses": "- More analysis could be provided on the sample quality of the Plaid 1B model beyond likelihood benchmarks. Samples from the model should be evaluated quantitatively. The paper focuses on likelihood, but other metrics like human evaluation of samples could better highlight the benefits of diffusion models over autoregressive ones.\n- The ablations studying design choices are limited to a single model scale (1B). Ablations at multiple scales could better validate conclusions. \n- Lack of experiment support (or theoretical explanations) of design choices, such as pre-training sequence length, word embedding dimension, the ratio of examples to compute conditional loss, etc.\n- Only CDCD is compared, additional comparison to other diffusion language models (such as Diffusion-LM[1]) would add context about progress in this area.\n\n[1] https://arxiv.org/abs/2205.14217", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your helpful comments. We give some detailed comments on your questions below.\n\n> [Sample evaluation of Plaid 1B]\n\nWe have performed a study on Amazon Mechanical Turk, comparing samples from Plaid-1B to samples from the GPT2 124M model. We generate unconditional samples of length 128 from both GPT-2 and Plaid 1B, and repeatedly ask Mechanical Turk crowdworkers to choose the most coherent sample from a pair (one GPT-2, one Plaid, blinded and randomly ordered). The turkers find the samples comparable, with the 95% CI for the win rate of Plaid ranging from 0.47 to 0.55, with a mean win rate of 0.51. We find this to be consistent with our likelihood evaluations.\n\n> [Ablations at different scales]\n\nWe agree that this would be useful follow-up, however, we were limited in our computational resources and focused on scaling up the main diffusion model (Plaid 1B) first to show the validity of our design choices at scale.\n\n> [Experimental support of other design choices]\n\nHyperparameters for Plaid were chosen on small-scale hyperparameter tuning runs, consistent with our scaling approach. We will include a discussion of these choices and details in our revision.\n\n> [Diffusion-LM comparison]\n\nWe have performed diffusion-LM comparisons (Using the likelihood based diffusion-LM in appendix F of the diffusion LM paper) and found the diffusion-LM likelihood performance to be worse than our worst and smallest models. We did not initially include these results in the paper as diffusion-LM did not focus on likelihoods, we will include them in the final version of our paper.\n\n> [Derivations eq4-5]. \n\nThese derivations follow from the Kingma et al variational diffusion formalism. We will make that clear in our revision.\n\n> [Learned embeddings] \n\nWe called this section learned embeddings, as we wanted to discuss how a likelihood-based formalism changes how we learn embeddings (i.e. we do not need heuristics to prevent the representation from collapsing). We will make this clearer. \n\n> How to choose the examples ratio between the two terms in Section 3.4?\n\nThis is the closed-form solution which minimizes the variance of the sum of the two terms. We will update the paper to clarify this.\n\n> [Embedding dim]  \n\nBoth our generated samples and our likelihood evaluations confirm that the 16 dim embeddings remain sufficiently powerful to capture a vocabulary of 32k tokens. We were similarly surprised, but the optimizing against the likelihood (which is defined in the original discrete space) made it clear that low dimensional embeddings were computationally efficient with little quality loss in the model.\n\n> [Sequence length] \n\nWe trained with 1024 as a reasonable standard context window length that would be comparable to GPT-2 (which also uses sequence length 1024). In earlier experiments, we also tested context lengths 256 and 512, and found them to work fine with no hyperparameter changes needed. We suspect that longer sequence lengths could also be used without any changes.\n\n> [Training for sequences] \n\nWe take random 1024 sequences in openwebtext, including end of sequence tokens at the end of documents, and continuing on to the next document. This approach makes it so that every batch has the same length, and no padding is necessary.  For the random subset with shorter sequence lengths, we don't need to use [PAD] tokens because our implementation supports variable-length sequences in the same batch directly.\n"}, {"review_id": "yFDOvi05r5", "reviewer": "Reviewer_rRHQ", "summary": "This paper propose a variational diffusion language model which is based on variational diffusion model. The work is related on some works such as combineing vae with language model.", "questions": "Please see the weaknesses", "limitations": "The sampeling time is expensive. \nThe parametes of model is large", "rating": 5, "confidence": 2, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "The application of variational diffussion model to language model is make sense at the some times, and the experiment results verify the effectivenss of the proposed model. However, the proposed model is litter novel, which is simlar to the application of vae on language models.", "weaknesses": "The proposed model is a autoagressive language model, and will add more parameters compared with exciting languge models. At the same time, the sampling time of each token will become expensive. \nIt's noted that the autoagressive language model generate each token is slow, and there are many works try their best to construct non-agressive languge models based on diffussion model. While the results are not perfect, their work can  speed the generative process. \nThus, in my opinion, this paper do a simple combination work to improve language model's performance, which is not very worth .", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your review. \n\nWe would like to clarify some points in our work, as the review seems to have some misconceptions.\n - our work does use a variational characterization of diffusion models, but has very little in common with VAEs.\n - our work does not propose an autoregressive language model\n - finally, our work develops new changes to the diffusion model architecture and proposes a scaling law for diffusions.\n\nWhat our work does do is to build a new class of likelihood-based diffusions that have a clear, principled objective and use this as a way to build computationally efficient training for large diffusion models. \n\n"}, {"review_id": "Px4vhQbush", "reviewer": "Reviewer_s82i", "summary": "This paper aims to close the gap between autoregressive and diffusion-based language models on standard perplexity-based language modeling benchmarks. To achieve this goal, the paper proposes several algorithmic improvements for maximum-likelihood training, and studies the scaling laws of the diffusion models to find optimal training regimes. Finally, based on the improvements, the paper shows results with a 1B diffusion-based language model that outperforms GPT2 in perplexity with various analyses.", "questions": "In line 111, the paper mentioned, “Plaid loss function is a bound on discrete data”. Why is this the case? Do you mean the objective in Eq.5 where x is discrete or x is one-hot vectors? In line 63, the paper mentions each x will be transformed into embedding vectors with invertible token-wise embedding function Embed(.). Is this invertible function the learnable embeddings mentioned in line 111? Why is it invertible? Why is there no x~ in the final objective in Eq.5? \n\nFor categorical reparameterization, how do you keep x~ still the original embedding? Will there be some mismatch? How this relates to avoiding the model to memorizing the embedding vectors? I can get the method but find it confusing how this is motivated. Can you also do the same in the target embedding (e.g., label smoothing?)\n", "limitations": "The paper does not seem to discuss the limitations. Since the findings on diffusion-based LM seems to suggest that training diffusion-based LM is much harder and may require more resources than its counterpart autoregressive models, it may be interesting to discuss the future steps or any potential combinations.", "rating": 7, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "Solid experiments and analysis. \n\nThe derived recipe for training diffusion-based LM that differs substantially from the usual autoregressive model is particularly meaningful. \n\nThe explored design space and open-sourced model have a good contribution to the community on further developing of diffusion-based LM as an alternative foundation model research. \n", "weaknesses": "The basic algorithm is mostly following VDM (Kingma et, al.), and most of the proposed improvements are also seen from existing literature. Therefore, the novelty from the algorithmic side is somewhat limited.\n\nPart of the description is confusing and unclear how exactly the authors do (see Q1)\n", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your review! We'd like to respond to a few points below:\n\n> The basic algorithm is mostly following VDM (Kingma et, al.), and most of the proposed improvements are also seen from existing literature. Therefore, the novelty from the algorithmic side is somewhat limited.\n\nWe are the first to find an algorithmic recipe for compute-efficient likelihood-based text diffusions. As you correctly point out, this recipe leverages lots of prior work, but our results couldn’t be achieved just by carefully implementing prior work: no prior text diffusion work has achieved any nontrivial likelihoods on any standard benchmark, and our recipe achieves more than a 2x improvement in compute efficiency over our CDCD implementation (see Table 1). We have updated our paper draft to make this more explicit in the introduction.\n\n> In line 111, the paper mentioned, “Plaid loss function is a bound on discrete data”. Why is this the case? Do you mean the objective in Eq.5 where x is discrete or x is one-hot vectors?\n\nEq 5 should have $\\tilde{x}$ instead of $x$; we will correct this. The overall loss (eq 3) is indeed a bound on the discrete data: the second term of eqn (3) measures the conditional likelihood of discrete tokens given the final (continous) embedding in the diffusion process.\n\n> In line 63, the paper mentions each x will be transformed into embedding vectors with invertible token-wise embedding function Embed(.). Is this invertible function the learnable embeddings mentioned in line 111? Why is it invertible? Why is there no x~ in the final objective in Eq.5? \n\nWe don’t need the embedding matrix to be invertible in order for the embedding function to be invertible, since the embedding function is only defined over the set of vertices of the vocabulary simplex and not the interior of the vocabulary simplex. Our embedding function is therefore indeed invertible even though our embedding matrix is low-rank. We will update the paper with a more careful discussion of this. Eqn 5 should indeed have a $\\tilde{x}$ in place of $x$ (see above) and we will fix this.\n\n"}, {"review_id": "H3tcXWXyQI", "reviewer": "Reviewer_ogcC", "summary": "This paper formalises variation deffusion models for text and makes several algorithmic contributions to such models.  They show that these models are able to model the likelihood of text well, and do several other evaluations to validate the likelihood approach, quantify the computational requirements, perform ablations, and evaluate a method for doing conditional generation.", "questions": "Where are the Appendices?\n\nWould it be possible to run a forward-cross-entropy evaluation on the generated texts, to see if they cover the full distribution?\n\nSuggestion: The caption of table 2 does not define what the numbers are.\n", "limitations": "Only briefly mentions computational issues.  ", "rating": 7, "confidence": 3, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "I found this perspective on diffusion enlightening and was very interested to see how to use it to model text.  The contributions are substantial and the evaluations are informative.", "weaknesses": "This paper covers a lot of material for its length, which makes it challenging to read.  Several references are made to Appendices, but I couldn't find any; these would have been useful.  The model specifications in sections 3.3 - 3.5 are very terse; perhaps more citations or a longer explanation in an appendix would be helpful to many readers.  Section 3.2 seems related to simplex diffusion, so some additional citations would be appropriate.\n\nThe validation of the likelihood-based approach (sec 4.1) only addresses whether the model puts high probability on good texts, not whether it covers the full distribution of texts accurately.  The latter is addressed by the likelihood evaluations themselves, but this is exactly what the model is trained to do.  It would be better to include previous measure for generation quality, such as both forward and reverse cross entropy (https://arxiv.org/abs/1804.07972).\n", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "\nThank you for the thoughtful review.\n\n> [Appendices]\n\nOur appendices are included in the supplementary material that is uploaded.\n\n> The model specifications in sections 3.3 - 3.5 are very terse; perhaps more citations or a longer explanation in an appendix would be helpful to many readers. Section 3.2 seems related to simplex diffusion, so some additional citations would be appropriate.\n\nOur main text exposition was limited by the page limit, but we agree that additional materials on S3.3-3.5 and the simplex diffusion could be helpful, and intend to revise our manuscript to expand these points.\n\n> [Forward likelihood and evaluations]\n\nOur likelihood evaluations are exactly E_{p_data}[log(p_model(x))], which is the forward cross-entropy that you have asked for, and Plaid shows substantial gains on these evaluations.\n\nWe agree that additional evaluations could be helpful especially to measure the quality of samples, and so performed an Amazon Mechanical Turk study generated samples, and found Plaid-1B to be comparable to GPT-2 124M, with mean win rate of 5.112, and a 95% CI of 0.47-0.55. \n\n> Suggestion: The caption of table 2 does not define what the numbers are.\n> Only briefly mentions computational issues.\n\nWe will additionally correct the caption of table 2, and include a discussion of compute details and challenges. \n"}, {"review_id": "vfA8obpKEg", "reviewer": "Reviewer_hVSR", "summary": "This paper is concentrated on training a Diffusion LM. The authors chose Variational Lower Bound loss used in Diffusion LM as a core of the proposed framework named Plaid, as well as some improvements on the training procedure and architecture of a model (usage of categorical reparametrization, use of trained categorical mapping for learning conditional likelihood term, and usage of self-conditioning method).\n\nAuthors evaluated proposed changes in the ablation study compared to reimplemented CDCD measured by NLL, derived scaling laws for their model. They trained the Plaid 1B model, outperforming GPT-2 124M in NLL. \n\nAlso, the authors provided samples from the Plaid 1B model and showed the ability of the model to perform conditional and unconditional sampling.", "questions": "Please, refer to points from weaknesses.", "limitations": "L320 explicitly states, \"Our ablations show that maximizing likelihood does not substantially harm sample quality, \" though it is not valid. Ablation experiments were performed with only NLL on test data, which does not include any sample quality information. The only experiment that evaluated sample quality was a comparison with hand-crafted weighting functions.", "rating": 5, "confidence": 4, "soundness": 1, "presentation": 3, "contribution": 2, "strengths": "- Authors proposed improvements on top of VLB loss, which appear to be useful measured by NLL on test data within the ablation study.\n- Categorical reparametrization is an exciting way to mix strengths of score interpolation objective from CDCD with VBL loss which do not imply heuristic constraints on training (e.g., normalization of the embedding matrix).\n- The paper is well organized and mainly clear (see weaknesses section)", "weaknesses": "Experiments:\n- Specific weightings A and B in Section 4.1 are too artificial. It will be nice to have an explanation in the paper as to why such weightings were selected.\n- Information regarding pre-trained embeddings in Section 4.1 is necessary. Stating \"all models use fixed embeddings obtained from a previously-trained known-good model\" does not provide any helpful information regarding the details of the experiment.\n- There needs to be more information regarding human resources used for crowd working in Section 4.1, while the authors claimed that Human Subjects are N/A.\n- The authors stated that results from Section 4.1 suggest that VLB performs at least comparably to other choices. Though, I need to see how such a conclusion could be made from this experiment. As for me, it states that if one tries to replace theoretically justified weighting with a specific hand-crafted one, the quality of samples will reduce. If it was the aim of this experiment, different other parts of VBL could also be changed to understand how important they are.\n- The evaluation of other experiments is poor. Plaid is compared to CDCD and GPT-2 only by NLL, which does not show any information on the quality of samples. E.g., removing self-conditioning leads to an increase of NLL on 0.09 points – how bad is it? Is this a minor reduction of sample quality or not? It is a critical point for me since if we want to use Plaid for text generation, it is not important what NLL values on test data it achieves. It is much more important how good samples from the model are. For the autoregressive model, it is convenient to expect that lower NLL leads to better samples, but is there such a convenience for Diffusion LMs (even trained with VLB loss)? I strongly believe that not. E.g., if one dramatically reduces the noise scale, making embeddings easily distinguishable, then it is easy to make NLL equal to 0, while samples will be poor.  It is necessary to include numerical evaluation of model samples (e.g., text repetition, evaluating perplexity of samples with a third-party language model, and others)\n- Baselines used for experiments are also limited. E.g., if Plaid is built on top of VLB from Diffusion LM, it is necessary to include information on its performance in the paper.\n- The list of insights on the performance of Plaid could be more extensive. E.g., sampling from Plaid could be performed with a different number of steps. How to sample quality differs with the change in this number? How does the number of sampling steps affect inference speed? The only numerical information regarding Plaid that is available is NLL on test data, which is not sufficient.\n\nMotivation:\n- I see the leitmotif of the first three sections that VLB allows us to reduce dependency on heuristic design choices (e.g., authors claim other methods to be workarounds (L109)). Though the design choices of other works are heuristic, the VLB framework seems to be much more complex and harder to implement (e.g., usage of double-precision parameters for everything except transformer layers and usage of parameter-specific learning rates indicate that making Plaid work stable required a lot of efforts). Why should a practitioner use a method that requires parameter-specific LR and spend more time selecting the best-performing LR over a simple yet heuristic method? Doing so could be justified by higher sample quality, but in the current state, the paper lacks details on sample quality.\n- Considering the usage of double-precision and parameter-specific learning rates, stating that the likelihood-based approach \"simplifies training\" (L167) does not seem to be true.\n\nReproducibility\n- The paper needs to include information regarding training infrastructure and the time necessary to reproduce the experiments. Also, the authors claimed \"Yes\" within the \"compute\" box for submission while not providing information regarding computing.\n- Self-conditioning used in the code is not common. I understand self-conditioning is only added for specific token positions with some offset (line 194 train.py). What is the motivation for doing so? Are there any other works that used such a scheme? If yes, I do not see citing. If not, I need sufficient information on such a scheme in the paper.\n- I do not see the trained model with supplementary materials, though authors claim that the model is publicly available\n\nNarrative:\n- The paper feels like a merge of two short papers: authors had proposed architectural improvements for training a Diffusion LM with VLB objective without precise studying limits of the Plaid framework and studied scaling laws of Plaid. These scaling laws could have been an interesting complement to the paper if there were more experiments on Plaid performance and behaviour. But now, scaling laws experiments look like studying the scaling laws of a model we do not understand.\n\nWhile at this point, I voted for the rejection of the paper, I still see potential in this work since some of the architectural choices seem interesting to me. I would happily increase the score if the drawbacks were fixed. First, possible changes include adding more metrics for Plaid since NLL value does not provide information on Plaid performance. \nOnce these metrics are added, including more experiments to provide more insights into Plaid behavior will be highly beneficial. E.g., authors could study what trained posterior $f_{\\theta}(z)$ looks like (its statistics, etc.).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "\nWe thank you for a very thorough review. We’d like to respond to some of your points below: \n\n> For the autoregressive model, it is convenient to expect that lower NLL leads to better samples, but is there such a convenience for Diffusion LMs [...]? I strongly believe that not. E.g., if one dramatically reduces the noise scale [...] then it is easy to make NLL equal to 0, while samples will be poor.\n\nThis is not correct: we use the discrete NLL for training and eval, which is the same quantity that autoregressive LMs use. Your claim would apply to a continuous NLL on the embeddings, which we do not consider in this work. Rescaling the embeddings would not minimize our loss, as the reconstruction term p(x|z_0) in eqn (3) would increase.\n\nMore generally, it is impossible to \"cheat\" the discrete NLL: a generative model that minimizes the discrete NLL also minimizes the KL divergence, and the unique minimizer of the KL is the original data generating distribution. This is a main reason we emphasize NLL-based training and evaluation in the paper. We have updated the paper writing to clarify this.\n\n> Plaid is compared to CDCD and GPT-2 only by NLL, which does not show any information on the quality of samples.\n\nWe have performed a human study on Amazon Mechanical Turk, comparing unconditional samples from Plaid-1B to samples from GPT-2 124M. The turkers find the samples comparable, with the 95% CI for the win rate of Plaid ranging from 0.47 to 0.55, with a mean win rate of 0.51. We find this to be consistent with our likelihood evaluations.\n\nRegarding CDCD, we would have liked to do a sample comparison, but we corresponded with the CDCD authors and they were unable to provide us with CDCD-generated samples.\n\n> Specific weightings A and B in Section 4.1 are too artificial. It will be nice to have an explanation in the paper as to why such weightings were selected.\n\nWe will update the final draft of the paper with a more detailed discussion. We selected these heuristics because they down weight small noise levels relative to the likelihood weighting, which is what has been used successfully in image diffusions (we adapt these schedules to the text setting instead of directly using them because they assume image inputs which are differently scaled than our text embedding vectors.). One advantage of our likelihood-based formalism is that the weightings are specified by the objective itself, rather than through a heuristic.\n\n> Information regarding pre-trained embeddings in Section 4.1 is necessary. Stating \"all models use fixed embeddings obtained from a previously-trained known-good model\" does not provide any helpful information regarding the details of the experiment.\n\nThe fixed embeddings were obtained by training a Plaid model and validating that it attains strong likelihoods and generates coherent samples. We then use these embeddings from the Plaid model on the ablations. We will provide training hyperparameters and evaluations of this model the final draft of the paper.\n\n> There needs to be more information regarding human resources used for crowd working in Section 4.1, while the authors claimed that Human Subjects are N/A.\n\nWe apologize for the oversight. We agree, and will update the paper draft to explain in detail our human experiment protocol. The overall experimental design follows a blinded randomized A/B test. The details are as follows: We recruited crowd workers using Amazon Mechanical Turk (selection criteria: US location, 95% HIT approval rate, >1000 HITs approved). Workers were shown two random samples in random order and given the following prompt: “Given two short pieces of text, decide which is more coherent overall (i.e. has the fewest grammar mistakes and makes the most sense).” Workers were paid $0.15 per task, which we estimated to take less than 30 seconds on average.\n\n> Though the design choices of other works are heuristic, the VLB framework seems to be much more complex and harder to implement (e.g., usage of double-precision parameters for everything except transformer layers and usage of parameter-specific learning rates indicate that making Plaid work stable required a lot of efforts).\n\nDouble-precision and parameter-specific LRs were convenience decisions in our implementation. We will update the camera-ready with the following result: our method trains stably and performs well when single-precision floats and a fixed learning rate for all parameters is used.\n\n> Self-conditioning used in the code is not common.\n\nOur self-conditioning implementation directly follows the original work (Chen et al. 2022). We apply self-conditioning to a random subset of each batch (again following Chen et al.). The line you reference is an implementation detail, where we choose that subset by picking an offset in {0,1,2,3} and picking `batch[offset::4]`, noting that the examples are randomly ordered within the batch.\n\n> The paper needs to include information regarding training infrastructure and the time necessary to reproduce the experiments. Also, the authors claimed \"Yes\" within the \"compute\" box for submission while not providing information regarding computing.\n\nWe provide FLOP counts for all models trained. We will additionally update the paper with actual hardware used and wall-clock times: All of our small runs take less than 24 hours on a single A100, and Plaid 1B took 30 days on 8 A100s.\n\n> I do not see the trained model with supplementary materials, though authors claim that the model is publicly available\n\nThe model is currently publicly available, but we have redacted the link in the paper in order to comply with anonymity guidelines. We will un-redact the link in the camera-ready. \n\n> The paper feels like a merge of two short papers\n\nThe scaling law study, which is the first of its kind for diffusion models, was made possible only by the VLB framework: smooth power-law scaling is a unique property of likelihoods (see Kaplan et al. 2020).\n\n"}]

Author rebuttal:

Meta-review: This research delves into diffusion-inspired language models to bridge the gap between autoregressive and diffusion-based LMs. Through systematic design choices and algorithmic improvements, the 1 billion parameter Plaid 1B model was developed, outshining GPT-2 124M in perplexity benchmarks. The model also demonstrates superior sample generation capabilities.

There was good discussion during the rebuttal period during which many concerns were addressed. All reviews were generally positive in the end. rRHQ had a remaining concern about BLEU score, but does not believe that should not tank the paper.

I therefore recommend accept. Please update the camera ready with all the writing changes promised in the rebuttal.
Decision: Accept (poster)