date stringdate 2023-05-04 00:00:00 2025-08-27 00:00:00 | arxiv_id stringlengths 10 10 | votes int32 0 110M | title stringlengths 8 206 | abstract stringlengths 165 1.92k | url stringlengths 40 40 |
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2023-05-26 | 2305.15798 | 4 | On Architectural Compression of Text-to-Image Diffusion Models | Exceptional text-to-image (T2I) generation results of Stable Diffusion models
(SDMs) come with substantial computational demands. To resolve this issue,
recent research on efficient SDMs has prioritized reducing the number of
sampling steps and utilizing network quantization. Orthogonal to these
directions, this study highlights the power of classical architectural
compression for general-purpose T2I synthesis by introducing block-removed
knowledge-distilled SDMs (BK-SDMs). We eliminate several residual and attention
blocks from the U-Net of SDMs, obtaining over a 30% reduction in the number of
parameters, MACs per sampling step, and latency. We conduct distillation-based
pretraining with only 0.22M LAION pairs (fewer than 0.1% of the full training
pairs) on a single A100 GPU. Despite being trained with limited resources, our
compact models can imitate the original SDM by benefiting from transferred
knowledge and achieve competitive results against larger multi-billion
parameter models on the zero-shot MS-COCO benchmark. Moreover, we demonstrate
the applicability of our lightweight pretrained models in personalized
generation with DreamBooth finetuning. | https://huggingface.co/papers/2305.15798 |
2023-05-26 | 2305.15779 | 3 | Custom-Edit: Text-Guided Image Editing with Customized Diffusion Models | Text-to-image diffusion models can generate diverse, high-fidelity images
based on user-provided text prompts. Recent research has extended these models
to support text-guided image editing. While text guidance is an intuitive
editing interface for users, it often fails to ensure the precise concept
conveyed by users. To address this issue, we propose Custom-Edit, in which we
(i) customize a diffusion model with a few reference images and then (ii)
perform text-guided editing. Our key discovery is that customizing only
language-relevant parameters with augmented prompts improves reference
similarity significantly while maintaining source similarity. Moreover, we
provide our recipe for each customization and editing process. We compare
popular customization methods and validate our findings on two editing methods
using various datasets. | https://huggingface.co/papers/2305.15779 |
2023-05-26 | 2305.15719 | 2 | Efficient Neural Music Generation | Recent progress in music generation has been remarkably advanced by the
state-of-the-art MusicLM, which comprises a hierarchy of three LMs,
respectively, for semantic, coarse acoustic, and fine acoustic modelings. Yet,
sampling with the MusicLM requires processing through these LMs one by one to
obtain the fine-grained acoustic tokens, making it computationally expensive
and prohibitive for a real-time generation. Efficient music generation with a
quality on par with MusicLM remains a significant challenge. In this paper, we
present MeLoDy (M for music; L for LM; D for diffusion), an LM-guided diffusion
model that generates music audios of state-of-the-art quality meanwhile
reducing 95.7% or 99.6% forward passes in MusicLM, respectively, for sampling
10s or 30s music. MeLoDy inherits the highest-level LM from MusicLM for
semantic modeling, and applies a novel dual-path diffusion (DPD) model and an
audio VAE-GAN to efficiently decode the conditioning semantic tokens into
waveform. DPD is proposed to simultaneously model the coarse and fine acoustics
by incorporating the semantic information into segments of latents effectively
via cross-attention at each denoising step. Our experimental results suggest
the superiority of MeLoDy, not only in its practical advantages on sampling
speed and infinitely continuable generation, but also in its state-of-the-art
musicality, audio quality, and text correlation.
Our samples are available at https://Efficient-MeLoDy.github.io/. | https://huggingface.co/papers/2305.15719 |
2023-05-26 | 2305.15586 | 2 | Manifold Diffusion Fields | We present Manifold Diffusion Fields (MDF), an approach to learn generative
models of continuous functions defined over Riemannian manifolds. Leveraging
insights from spectral geometry analysis, we define an intrinsic coordinate
system on the manifold via the eigen-functions of the Laplace-Beltrami
Operator. MDF represents functions using an explicit parametrization formed by
a set of multiple input-output pairs. Our approach allows to sample continuous
functions on manifolds and is invariant with respect to rigid and isometric
transformations of the manifold. Empirical results on several datasets and
manifolds show that MDF can capture distributions of such functions with better
diversity and fidelity than previous approaches. | https://huggingface.co/papers/2305.15586 |
2023-05-26 | 2305.15581 | 2 | Unsupervised Semantic Correspondence Using Stable Diffusion | Text-to-image diffusion models are now capable of generating images that are
often indistinguishable from real images. To generate such images, these models
must understand the semantics of the objects they are asked to generate. In
this work we show that, without any training, one can leverage this semantic
knowledge within diffusion models to find semantic correspondences -- locations
in multiple images that have the same semantic meaning. Specifically, given an
image, we optimize the prompt embeddings of these models for maximum attention
on the regions of interest. These optimized embeddings capture semantic
information about the location, which can then be transferred to another image.
By doing so we obtain results on par with the strongly supervised state of the
art on the PF-Willow dataset and significantly outperform (20.9% relative for
the SPair-71k dataset) any existing weakly or unsupervised method on PF-Willow,
CUB-200 and SPair-71k datasets. | https://huggingface.co/papers/2305.15581 |
2023-05-29 | 2305.16311 | 7 | Break-A-Scene: Extracting Multiple Concepts from a Single Image | Text-to-image model personalization aims to introduce a user-provided concept
to the model, allowing its synthesis in diverse contexts. However, current
methods primarily focus on the case of learning a single concept from multiple
images with variations in backgrounds and poses, and struggle when adapted to a
different scenario. In this work, we introduce the task of textual scene
decomposition: given a single image of a scene that may contain several
concepts, we aim to extract a distinct text token for each concept, enabling
fine-grained control over the generated scenes. To this end, we propose
augmenting the input image with masks that indicate the presence of target
concepts. These masks can be provided by the user or generated automatically by
a pre-trained segmentation model. We then present a novel two-phase
customization process that optimizes a set of dedicated textual embeddings
(handles), as well as the model weights, striking a delicate balance between
accurately capturing the concepts and avoiding overfitting. We employ a masked
diffusion loss to enable handles to generate their assigned concepts,
complemented by a novel loss on cross-attention maps to prevent entanglement.
We also introduce union-sampling, a training strategy aimed to improve the
ability of combining multiple concepts in generated images. We use several
automatic metrics to quantitatively compare our method against several
baselines, and further affirm the results using a user study. Finally, we
showcase several applications of our method. Project page is available at:
https://omriavrahami.com/break-a-scene/ | https://huggingface.co/papers/2305.16311 |
2023-05-29 | 2305.17098 | 4 | ControlVideo: Adding Conditional Control for One Shot Text-to-Video
Editing | In this paper, we present ControlVideo, a novel method for text-driven video
editing. Leveraging the capabilities of text-to-image diffusion models and
ControlNet, ControlVideo aims to enhance the fidelity and temporal consistency
of videos that align with a given text while preserving the structure of the
source video. This is achieved by incorporating additional conditions such as
edge maps, fine-tuning the key-frame and temporal attention on the source
video-text pair with carefully designed strategies. An in-depth exploration of
ControlVideo's design is conducted to inform future research on one-shot tuning
video diffusion models. Quantitatively, ControlVideo outperforms a range of
competitive baselines in terms of faithfulness and consistency while still
aligning with the textual prompt. Additionally, it delivers videos with high
visual realism and fidelity w.r.t. the source content, demonstrating
flexibility in utilizing controls containing varying degrees of source video
information, and the potential for multiple control combinations. The project
page is available at
https://ml.cs.tsinghua.edu.cn/controlvideo/{https://ml.cs.tsinghua.edu.cn/controlvideo/}. | https://huggingface.co/papers/2305.17098 |
2023-05-29 | 2305.16380 | 4 | Scan and Snap: Understanding Training Dynamics and Token Composition in
1-layer Transformer | Transformer architecture has shown impressive performance in multiple
research domains and has become the backbone of many neural network models.
However, there is limited understanding on how it works. In particular, with a
simple predictive loss, how the representation emerges from the gradient
training dynamics remains a mystery. In this paper, for 1-layer
transformer with one self-attention layer plus one decoder layer, we analyze
its SGD training dynamics for the task of next token prediction in a
mathematically rigorous manner. We open the black box of the dynamic process of
how the self-attention layer combines input tokens, and reveal the nature of
underlying inductive bias. More specifically, with the assumption (a) no
positional encoding, (b) long input sequence, and (c) the decoder layer learns
faster than the self-attention layer, we prove that self-attention acts as a
discriminative scanning algorithm: starting from uniform attention, it
gradually attends more to distinct key tokens for a specific next token to be
predicted, and pays less attention to common key tokens that occur across
different next tokens. Among distinct tokens, it progressively drops attention
weights, following the order of low to high co-occurrence between the key and
the query token in the training set. Interestingly, this procedure does not
lead to winner-takes-all, but decelerates due to a phase transition that
is controllable by the learning rates of the two layers, leaving (almost) fixed
token combination. We verify this \emph{scan and snap} dynamics on
synthetic and real-world data (WikiText). | https://huggingface.co/papers/2305.16380 |
2023-05-29 | 2305.17126 | 3 | Large Language Models as Tool Makers | Recent research shows the potential of enhancing the problem-solving ability
of large language models (LLMs) through the use of external tools. However,
prior work along this line depends on the availability of existing tools. In
this work, we take an initial step towards removing this dependency by
proposing a closed-loop framework, referred to as LLMs As Tool Makers (LATM),
where LLMs create their own reusable tools for problem-solving. Our approach
consists of two key phases: 1) tool making: an LLM acts as the tool maker that
crafts tools for given tasks, where a tool is implemented as a Python utility
function. 2) tool using: an LLM acts as the tool user, which applies the tool
built by the tool maker for problem-solving. The tool user can be either the
same or a different LLM from the tool maker. Tool-making enables an LLM to
continually generate tools that can be applied to different requests so that
future requests can call the corresponding APIs when beneficial for solving the
tasks. Furthermore, the division of labor among LLMs for tool-making and
tool-using phases introduces the opportunity to achieve cost effectiveness
without degrading the quality of generated tools and problem solutions. For
example, recognizing that tool-making demands more sophisticated capabilities
than tool-using, we can apply a powerful yet resource-intensive model as the
tool maker, and a lightweight while cost-effective model as the tool user. We
validate the effectiveness of our approach across a variety of complex
reasoning tasks, including Big-Bench tasks. With GPT-4 as the tool maker and
GPT-3.5 as the tool user, LATM can achieve performance that is on par with
using GPT-4 for both tool making and tool using, while the inference cost is
significantly reduced. | https://huggingface.co/papers/2305.17126 |
2023-05-29 | 2305.17066 | 3 | Mindstorms in Natural Language-Based Societies of Mind | Both Minsky's "society of mind" and Schmidhuber's "learning to think" inspire
diverse societies of large multimodal neural networks (NNs) that solve problems
by interviewing each other in a "mindstorm." Recent implementations of NN-based
societies of minds consist of large language models (LLMs) and other NN-based
experts communicating through a natural language interface. In doing so, they
overcome the limitations of single LLMs, improving multimodal zero-shot
reasoning. In these natural language-based societies of mind (NLSOMs), new
agents -- all communicating through the same universal symbolic language -- are
easily added in a modular fashion. To demonstrate the power of NLSOMs, we
assemble and experiment with several of them (having up to 129 members),
leveraging mindstorms in them to solve some practical AI tasks: visual question
answering, image captioning, text-to-image synthesis, 3D generation, egocentric
retrieval, embodied AI, and general language-based task solving. We view this
as a starting point towards much larger NLSOMs with billions of agents-some of
which may be humans. And with this emergence of great societies of
heterogeneous minds, many new research questions have suddenly become paramount
to the future of artificial intelligence. What should be the social structure
of an NLSOM? What would be the (dis)advantages of having a monarchical rather
than a democratic structure? How can principles of NN economies be used to
maximize the total reward of a reinforcement learning NLSOM? In this work, we
identify, discuss, and try to answer some of these questions. | https://huggingface.co/papers/2305.17066 |
2023-05-29 | 2305.16960 | 3 | Training Socially Aligned Language Models in Simulated Human Society | Social alignment in AI systems aims to ensure that these models behave
according to established societal values. However, unlike humans, who derive
consensus on value judgments through social interaction, current language
models (LMs) are trained to rigidly replicate their training corpus in
isolation, leading to subpar generalization in unfamiliar scenarios and
vulnerability to adversarial attacks. This work presents a novel training
paradigm that permits LMs to learn from simulated social interactions. In
comparison to existing methodologies, our approach is considerably more
scalable and efficient, demonstrating superior performance in alignment
benchmarks and human evaluations. This paradigm shift in the training of LMs
brings us a step closer to developing AI systems that can robustly and
accurately reflect societal norms and values. | https://huggingface.co/papers/2305.16960 |
2023-05-29 | 2305.16381 | 3 | DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion
Models | Learning from human feedback has been shown to improve text-to-image models.
These techniques first learn a reward function that captures what humans care
about in the task and then improve the models based on the learned reward
function. Even though relatively simple approaches (e.g., rejection sampling
based on reward scores) have been investigated, fine-tuning text-to-image
models with the reward function remains challenging. In this work, we propose
using online reinforcement learning (RL) to fine-tune text-to-image models. We
focus on diffusion models, defining the fine-tuning task as an RL problem, and
updating the pre-trained text-to-image diffusion models using policy gradient
to maximize the feedback-trained reward. Our approach, coined DPOK, integrates
policy optimization with KL regularization. We conduct an analysis of KL
regularization for both RL fine-tuning and supervised fine-tuning. In our
experiments, we show that DPOK is generally superior to supervised fine-tuning
with respect to both image-text alignment and image quality. | https://huggingface.co/papers/2305.16381 |
2023-05-29 | 2305.16367 | 3 | Role-Play with Large Language Models | As dialogue agents become increasingly human-like in their performance, it is
imperative that we develop effective ways to describe their behaviour in
high-level terms without falling into the trap of anthropomorphism. In this
paper, we foreground the concept of role-play. Casting dialogue agent behaviour
in terms of role-play allows us to draw on familiar folk psychological terms,
without ascribing human characteristics to language models they in fact lack.
Two important cases of dialogue agent behaviour are addressed this way, namely
(apparent) deception and (apparent) self-awareness. | https://huggingface.co/papers/2305.16367 |
2023-05-29 | 2305.16355 | 3 | PandaGPT: One Model To Instruction-Follow Them All | We present PandaGPT, an approach to emPower large lANguage moDels with visual
and Auditory instruction-following capabilities. Our pilot experiments show
that PandaGPT can perform complex tasks such as detailed image description
generation, writing stories inspired by videos, and answering questions about
audios. More interestingly, PandaGPT can take multimodal inputs simultaneously
and compose their semantics naturally. For example, PandaGPT can connect how
objects look in an image/video and how they sound in an audio. To do so,
PandaGPT combines the multimodal encoders from ImageBind and the large language
models from Vicuna. Notably, only aligned image-text pairs are required for the
training of PandaGPT. Thanks to the strong capability of ImageBind in embedding
data from different modalities into the same space, PandaGPT displays emergent,
i.e. zero-shot, cross-modal behaviors for data other than image and text (e.g.,
video, audio, depth, thermal, and IMU). We hope that PandaGPT serves as an
initial step toward building AGI that can perceive and understand inputs in
different modalities holistically, as we humans do. Our project page is at
https://panda-gpt.github.io/. | https://huggingface.co/papers/2305.16355 |
2023-05-29 | 2305.16338 | 3 | Think Before You Act: Decision Transformers with Internal Working Memory | Large language model (LLM)-based decision-making agents have shown the
ability to generalize across multiple tasks. However, their performance relies
on massive data and compute. We argue that this inefficiency stems from the
forgetting phenomenon, in which a model memorizes its behaviors in parameters
throughout training. As a result, training on a new task may deteriorate the
model's performance on previous tasks. In contrast to LLMs' implicit memory
mechanism, the human brain utilizes distributed memory storage, which helps
manage and organize multiple skills efficiently, mitigating the forgetting
phenomenon. Thus inspired, we propose an internal working memory module to
store, blend, and retrieve information for different downstream tasks.
Evaluation results show that the proposed method improves training efficiency
and generalization in both Atari games and meta-world object manipulation
tasks. Moreover, we demonstrate that memory fine-tuning further enhances the
adaptability of the proposed architecture. | https://huggingface.co/papers/2305.16338 |
2023-05-29 | 2305.16999 | 2 | Three Towers: Flexible Contrastive Learning with Pretrained Image Models | We introduce Three Towers (3T), a flexible method to improve the contrastive
learning of vision-language models by incorporating pretrained image
classifiers. While contrastive models are usually trained from scratch, LiT
(Zhai et al., 2022) has recently shown performance gains from using pretrained
classifier embeddings. However, LiT directly replaces the image tower with the
frozen embeddings, excluding any potential benefits of contrastively training
the image tower. With 3T, we propose a more flexible strategy that allows the
image tower to benefit from both pretrained embeddings and contrastive
training. To achieve this, we introduce a third tower that contains the frozen
pretrained embeddings, and we encourage alignment between this third tower and
the main image-text towers. Empirically, 3T consistently improves over LiT and
the CLIP-style from-scratch baseline for retrieval tasks. For classification,
3T reliably improves over the from-scratch baseline, and while it underperforms
relative to LiT for JFT-pretrained models, it outperforms LiT for ImageNet-21k
and Places365 pretraining. | https://huggingface.co/papers/2305.16999 |
2023-05-29 | 2305.16958 | 2 | MixCE: Training Autoregressive Language Models by Mixing Forward and
Reverse Cross-Entropies | Autoregressive language models are trained by minimizing the cross-entropy of
the model distribution Q relative to the data distribution P -- that is,
minimizing the forward cross-entropy, which is equivalent to maximum likelihood
estimation (MLE). We have observed that models trained in this way may
"over-generalize", in the sense that they produce non-human-like text.
Moreover, we believe that reverse cross-entropy, i.e., the cross-entropy of P
relative to Q, is a better reflection of how a human would evaluate text
generated by a model. Hence, we propose learning with MixCE, an objective that
mixes the forward and reverse cross-entropies. We evaluate models trained with
this objective on synthetic data settings (where P is known) and real data, and
show that the resulting models yield better generated text without complex
decoding strategies. Our code and models are publicly available at
https://github.com/bloomberg/mixce-acl2023 | https://huggingface.co/papers/2305.16958 |
2023-05-29 | 2305.16867 | 2 | Playing repeated games with Large Language Models | Large Language Models (LLMs) are transforming society and permeating into
diverse applications. As a result, LLMs will frequently interact with us and
other agents. It is, therefore, of great societal value to understand how LLMs
behave in interactive social settings. Here, we propose to use behavioral game
theory to study LLM's cooperation and coordination behavior. To do so, we let
different LLMs (GPT-3, GPT-3.5, and GPT-4) play finitely repeated games with
each other and with other, human-like strategies. Our results show that LLMs
generally perform well in such tasks and also uncover persistent behavioral
signatures. In a large set of two players-two strategies games, we find that
LLMs are particularly good at games where valuing their own self-interest pays
off, like the iterated Prisoner's Dilemma family. However, they behave
sub-optimally in games that require coordination. We, therefore, further focus
on two games from these distinct families. In the canonical iterated Prisoner's
Dilemma, we find that GPT-4 acts particularly unforgivingly, always defecting
after another agent has defected only once. In the Battle of the Sexes, we find
that GPT-4 cannot match the behavior of the simple convention to alternate
between options. We verify that these behavioral signatures are stable across
robustness checks. Finally, we show how GPT-4's behavior can be modified by
providing further information about the other player as well as by asking it to
predict the other player's actions before making a choice. These results enrich
our understanding of LLM's social behavior and pave the way for a behavioral
game theory for machines. | https://huggingface.co/papers/2305.16867 |
2023-05-29 | 2305.16843 | 2 | Randomized Positional Encodings Boost Length Generalization of
Transformers | Transformers have impressive generalization capabilities on tasks with a
fixed context length. However, they fail to generalize to sequences of
arbitrary length, even for seemingly simple tasks such as duplicating a string.
Moreover, simply training on longer sequences is inefficient due to the
quadratic computation complexity of the global attention mechanism. In this
work, we demonstrate that this failure mode is linked to positional encodings
being out-of-distribution for longer sequences (even for relative encodings)
and introduce a novel family of positional encodings that can overcome this
problem. Concretely, our randomized positional encoding scheme simulates the
positions of longer sequences and randomly selects an ordered subset to fit the
sequence's length. Our large-scale empirical evaluation of 6000 models across
15 algorithmic reasoning tasks shows that our method allows Transformers to
generalize to sequences of unseen length (increasing test accuracy by 12.0% on
average). | https://huggingface.co/papers/2305.16843 |
2023-05-29 | 2305.16349 | 2 | Lexinvariant Language Models | Token embeddings, a mapping from discrete lexical symbols to continuous
vectors, are at the heart of any language model (LM). However, lexical symbol
meanings can also be determined and even redefined by their structural role in
a long context. In this paper, we ask: is it possible for a language model to
be performant without any fixed token embeddings? Such a language model
would have to rely entirely on the co-occurence and repetition of tokens in the
context rather than the a priori identity of any token. To answer
this, we study lexinvariantlanguage models that are invariant to
lexical symbols and therefore do not need fixed token embeddings in practice.
First, we prove that we can construct a lexinvariant LM to converge to the true
language model at a uniform rate that is polynomial in terms of the context
length, with a constant factor that is sublinear in the vocabulary size.
Second, to build a lexinvariant LM, we simply encode tokens using random
Gaussian vectors, such that each token maps to the same representation within
each sequence but different representations across sequences. Empirically, we
demonstrate that it can indeed attain perplexity comparable to that of a
standard language model, given a sufficiently long context. We further explore
two properties of the lexinvariant language models: First, given text generated
from a substitution cipher of English, it implicitly implements Bayesian
in-context deciphering and infers the mapping to the underlying real tokens
with high accuracy. Second, it has on average 4X better accuracy over synthetic
in-context reasoning tasks. Finally, we discuss regularizing standard language
models towards lexinvariance and potential practical applications. | https://huggingface.co/papers/2305.16349 |
2023-05-29 | 2305.16806 | 1 | Do GPTs Produce Less Literal Translations? | Large Language Models (LLMs) such as GPT-3 have emerged as general-purpose
language models capable of addressing many natural language generation or
understanding tasks. On the task of Machine Translation (MT), multiple works
have investigated few-shot prompting mechanisms to elicit better translations
from LLMs. However, there has been relatively little investigation on how such
translations differ qualitatively from the translations generated by standard
Neural Machine Translation (NMT) models. In this work, we investigate these
differences in terms of the literalness of translations produced by the two
systems. Using literalness measures involving word alignment and monotonicity,
we find that translations out of English (E-X) from GPTs tend to be less
literal, while exhibiting similar or better scores on MT quality metrics. We
demonstrate that this finding is borne out in human evaluations as well. We
then show that these differences are especially pronounced when translating
sentences that contain idiomatic expressions. | https://huggingface.co/papers/2305.16806 |
2023-05-29 | 2305.16765 | 1 | Backpack Language Models | We present Backpacks: a new neural architecture that marries strong modeling
performance with an interface for interpretability and control. Backpacks learn
multiple non-contextual sense vectors for each word in a vocabulary, and
represent a word in a sequence as a context-dependent, non-negative linear
combination of sense vectors in this sequence. We find that, after training,
sense vectors specialize, each encoding a different aspect of a word. We can
interpret a sense vector by inspecting its (non-contextual, linear) projection
onto the output space, and intervene on these interpretable hooks to change the
model's behavior in predictable ways. We train a 170M-parameter Backpack
language model on OpenWebText, matching the loss of a GPT-2 small
(124Mparameter) Transformer. On lexical similarity evaluations, we find that
Backpack sense vectors outperform even a 6B-parameter Transformer LM's word
embeddings. Finally, we present simple algorithms that intervene on sense
vectors to perform controllable text generation and debiasing. For example, we
can edit the sense vocabulary to tend more towards a topic, or localize a
source of gender bias to a sense vector and globally suppress that sense. | https://huggingface.co/papers/2305.16765 |
2023-05-29 | 2305.16704 | 1 | A Closer Look at In-Context Learning under Distribution Shifts | In-context learning, a capability that enables a model to learn from input
examples on the fly without necessitating weight updates, is a defining
characteristic of large language models. In this work, we follow the setting
proposed in (Garg et al., 2022) to better understand the generality and
limitations of in-context learning from the lens of the simple yet fundamental
task of linear regression. The key question we aim to address is: Are
transformers more adept than some natural and simpler architectures at
performing in-context learning under varying distribution shifts? To compare
transformers, we propose to use a simple architecture based on set-based
Multi-Layer Perceptrons (MLPs). We find that both transformers and set-based
MLPs exhibit in-context learning under in-distribution evaluations, but
transformers more closely emulate the performance of ordinary least squares
(OLS). Transformers also display better resilience to mild distribution shifts,
where set-based MLPs falter. However, under severe distribution shifts, both
models' in-context learning abilities diminish. | https://huggingface.co/papers/2305.16704 |
2023-05-29 | 2305.16635 | 1 | Impossible Distillation: from Low-Quality Model to High-Quality Dataset
& Model for Summarization and Paraphrasing | We present Impossible Distillation, a novel framework for paraphrasing and
sentence summarization, that distills a high-quality dataset and model from a
low-quality teacher that itself cannot perform these tasks. Unlike prior works
that rely on an extreme-scale teacher model (e.g., GPT3) or task-specific
architecture, we hypothesize and verify the paraphrastic proximity intrinsic to
pre-trained LMs (e.g., GPT2), where paraphrases occupy a proximal subspace in
the LM distribution. By identifying and distilling generations from these
subspaces, Impossible Distillation produces a high-quality dataset and model
even from GPT2-scale LMs. We evaluate our method on multiple benchmarks
spanning unconstrained / syntax-controlled paraphrase generation and sentence
summarization. Our model with 770M parameters consistently outperforms strong
baselines, including models distilled from ChatGPT, and sometimes, even ChatGPT
itself. Also, we find that our distilled dataset from 1.5B LMs exhibits higher
diversity and fidelity than up to 13 times larger datasets. | https://huggingface.co/papers/2305.16635 |
2023-05-29 | 2305.16411 | 1 | ZeroAvatar: Zero-shot 3D Avatar Generation from a Single Image | Recent advancements in text-to-image generation have enabled significant
progress in zero-shot 3D shape generation. This is achieved by score
distillation, a methodology that uses pre-trained text-to-image diffusion
models to optimize the parameters of a 3D neural presentation, e.g. Neural
Radiance Field (NeRF). While showing promising results, existing methods are
often not able to preserve the geometry of complex shapes, such as human
bodies. To address this challenge, we present ZeroAvatar, a method that
introduces the explicit 3D human body prior to the optimization process.
Specifically, we first estimate and refine the parameters of a parametric human
body from a single image. Then during optimization, we use the posed parametric
body as additional geometry constraint to regularize the diffusion model as
well as the underlying density field. Lastly, we propose a UV-guided texture
regularization term to further guide the completion of texture on invisible
body parts. We show that ZeroAvatar significantly enhances the robustness and
3D consistency of optimization-based image-to-3D avatar generation,
outperforming existing zero-shot image-to-3D methods. | https://huggingface.co/papers/2305.16411 |
2023-05-29 | 2305.16334 | 1 | OlaGPT: Empowering LLMs With Human-like Problem-Solving Abilities | In most current research, large language models (LLMs) are able to perform
reasoning tasks by generating chains of thought through the guidance of
specific prompts. However, there still exists a significant discrepancy between
their capability in solving complex reasoning problems and that of humans. At
present, most approaches focus on chains of thought (COT) and tool use, without
considering the adoption and application of human cognitive frameworks. It is
well-known that when confronting complex reasoning challenges, humans typically
employ various cognitive abilities, and necessitate interaction with all
aspects of tools, knowledge, and the external environment information to
accomplish intricate tasks. This paper introduces a novel intelligent
framework, referred to as OlaGPT. OlaGPT carefully studied a cognitive
architecture framework, and propose to simulate certain aspects of human
cognition. The framework involves approximating different cognitive modules,
including attention, memory, reasoning, learning, and corresponding scheduling
and decision-making mechanisms. Inspired by the active learning mechanism of
human beings, it proposes a learning unit to record previous mistakes and
expert opinions, and dynamically refer to them to strengthen their ability to
solve similar problems. The paper also outlines common effective reasoning
frameworks for human problem-solving and designs Chain-of-Thought (COT)
templates accordingly. A comprehensive decision-making mechanism is also
proposed to maximize model accuracy. The efficacy of OlaGPT has been
stringently evaluated on multiple reasoning datasets, and the experimental
outcomes reveal that OlaGPT surpasses state-of-the-art benchmarks,
demonstrating its superior performance. Our implementation of OlaGPT is
available on GitHub: https://github.com/oladata-team/OlaGPT. | https://huggingface.co/papers/2305.16334 |
2023-05-30 | 2305.18295 | 8 | RAPHAEL: Text-to-Image Generation via Large Mixture of Diffusion Paths | Text-to-image generation has recently witnessed remarkable achievements. We
introduce a text-conditional image diffusion model, termed RAPHAEL, to generate
highly artistic images, which accurately portray the text prompts, encompassing
multiple nouns, adjectives, and verbs. This is achieved by stacking tens of
mixture-of-experts (MoEs) layers, i.e., space-MoE and time-MoE layers, enabling
billions of diffusion paths (routes) from the network input to the output. Each
path intuitively functions as a "painter" for depicting a particular textual
concept onto a specified image region at a diffusion timestep. Comprehensive
experiments reveal that RAPHAEL outperforms recent cutting-edge models, such as
Stable Diffusion, ERNIE-ViLG 2.0, DeepFloyd, and DALL-E 2, in terms of both
image quality and aesthetic appeal. Firstly, RAPHAEL exhibits superior
performance in switching images across diverse styles, such as Japanese comics,
realism, cyberpunk, and ink illustration. Secondly, a single model with three
billion parameters, trained on 1,000 A100 GPUs for two months, achieves a
state-of-the-art zero-shot FID score of 6.61 on the COCO dataset. Furthermore,
RAPHAEL significantly surpasses its counterparts in human evaluation on the
ViLG-300 benchmark. We believe that RAPHAEL holds the potential to propel the
frontiers of image generation research in both academia and industry, paving
the way for future breakthroughs in this rapidly evolving field. More details
can be found on a project webpage: https://raphael-painter.github.io/. | https://huggingface.co/papers/2305.18295 |
2023-05-30 | 2305.17216 | 7 | Generating Images with Multimodal Language Models | We propose a method to fuse frozen text-only large language models (LLMs)
with pre-trained image encoder and decoder models, by mapping between their
embedding spaces. Our model demonstrates a wide suite of multimodal
capabilities: image retrieval, novel image generation, and multimodal dialogue.
Ours is the first approach capable of conditioning on arbitrarily interleaved
image and text inputs to generate coherent image (and text) outputs. To achieve
strong performance on image generation, we propose an efficient mapping network
to ground the LLM to an off-the-shelf text-to-image generation model. This
mapping network translates hidden representations of text into the embedding
space of the visual models, enabling us to leverage the strong text
representations of the LLM for visual outputs. Our approach outperforms
baseline generation models on tasks with longer and more complex language. In
addition to novel image generation, our model is also capable of image
retrieval from a prespecified dataset, and decides whether to retrieve or
generate at inference time. This is done with a learnt decision module which
conditions on the hidden representations of the LLM. Our model exhibits a wider
range of capabilities compared to prior multimodal language models. It can
process image-and-text inputs, and produce retrieved images, generated images,
and generated text -- outperforming non-LLM based generation models across
several text-to-image tasks that measure context dependence. | https://huggingface.co/papers/2305.17216 |
2023-05-30 | 2305.18292 | 5 | Mix-of-Show: Decentralized Low-Rank Adaptation for Multi-Concept
Customization of Diffusion Models | Public large-scale text-to-image diffusion models, such as Stable Diffusion,
have gained significant attention from the community. These models can be
easily customized for new concepts using low-rank adaptations (LoRAs). However,
the utilization of multiple concept LoRAs to jointly support multiple
customized concepts presents a challenge. We refer to this scenario as
decentralized multi-concept customization, which involves single-client concept
tuning and center-node concept fusion. In this paper, we propose a new
framework called Mix-of-Show that addresses the challenges of decentralized
multi-concept customization, including concept conflicts resulting from
existing single-client LoRA tuning and identity loss during model fusion.
Mix-of-Show adopts an embedding-decomposed LoRA (ED-LoRA) for single-client
tuning and gradient fusion for the center node to preserve the in-domain
essence of single concepts and support theoretically limitless concept fusion.
Additionally, we introduce regionally controllable sampling, which extends
spatially controllable sampling (e.g., ControlNet and T2I-Adaptor) to address
attribute binding and missing object problems in multi-concept sampling.
Extensive experiments demonstrate that Mix-of-Show is capable of composing
multiple customized concepts with high fidelity, including characters, objects,
and scenes. | https://huggingface.co/papers/2305.18292 |
2023-05-30 | 2305.17493 | 5 | Model Dementia: Generated Data Makes Models Forget | Stable Diffusion revolutionised image creation from descriptive text. GPT-2,
GPT-3(.5) and GPT-4 demonstrated astonishing performance across a variety of
language tasks. ChatGPT introduced such language models to the general public.
It is now clear that large language models (LLMs) are here to stay, and will
bring about drastic change in the whole ecosystem of online text and images. In
this paper we consider what the future might hold. What will happen to GPT-{n}
once LLMs contribute much of the language found online? We find that use of
model-generated content in training causes irreversible defects in the
resulting models, where tails of the original content distribution disappear.
We call this effect model dementia and show that it can occur in Variational
Autoencoders (VAEs), Gaussian Mixture Models (GMMs) and LLMs. We build
theoretical intuition behind the phenomenon and portray its ubiquity amongst
all learned generative models. We demonstrate that it has to be taken seriously
if we are to sustain the benefits of training from large-scale data scraped
from the web. Indeed, the value of data collected about genuine human
interactions with systems will be increasingly valuable in the presence of
content generated by LLMs in data crawled from the Internet. | https://huggingface.co/papers/2305.17493 |
2023-05-30 | 2305.18752 | 4 | GPT4Tools: Teaching Large Language Model to Use Tools via
Self-instruction | This paper aims to efficiently enable Large Language Models (LLMs) to use
multimodal tools. Advanced proprietary LLMs, such as ChatGPT and GPT-4, have
shown great potential for tool usage through sophisticated prompt engineering.
Nevertheless, these models typically rely on prohibitive computational costs
and publicly inaccessible data. To address these challenges, we propose the
GPT4Tools based on self-instruct to enable open-source LLMs, such as LLaMA and
OPT, to use tools. It generates an instruction-following dataset by prompting
an advanced teacher with various multi-modal contexts. By using the Low-Rank
Adaptation (LoRA) optimization, our approach facilitates the open-source LLMs
to solve a range of visual problems, including visual comprehension and image
generation. Moreover, we provide a benchmark to evaluate the ability of LLMs to
use tools, which is performed in both zero-shot and fine-tuning ways. Extensive
experiments demonstrate the effectiveness of our method on various language
models, which not only significantly improves the accuracy of invoking seen
tools, but also enables the zero-shot capacity for unseen tools. The code and
demo are available at https://github.com/StevenGrove/GPT4Tools. | https://huggingface.co/papers/2305.18752 |
2023-05-30 | 2305.18274 | 4 | Reconstructing the Mind's Eye: fMRI-to-Image with Contrastive Learning
and Diffusion Priors | We present MindEye, a novel fMRI-to-image approach to retrieve and
reconstruct viewed images from brain activity. Our model comprises two parallel
submodules that are specialized for retrieval (using contrastive learning) and
reconstruction (using a diffusion prior). MindEye can map fMRI brain activity
to any high dimensional multimodal latent space, like CLIP image space,
enabling image reconstruction using generative models that accept embeddings
from this latent space. We comprehensively compare our approach with other
existing methods, using both qualitative side-by-side comparisons and
quantitative evaluations, and show that MindEye achieves state-of-the-art
performance in both reconstruction and retrieval tasks. In particular, MindEye
can retrieve the exact original image even among highly similar candidates
indicating that its brain embeddings retain fine-grained image-specific
information. This allows us to accurately retrieve images even from large-scale
databases like LAION-5B. We demonstrate through ablations that MindEye's
performance improvements over previous methods result from specialized
submodules for retrieval and reconstruction, improved training techniques, and
training models with orders of magnitude more parameters. Furthermore, we show
that MindEye can better preserve low-level image features in the
reconstructions by using img2img, with outputs from a separate autoencoder. All
code is available on GitHub. | https://huggingface.co/papers/2305.18274 |
2023-05-30 | 2305.18247 | 4 | TaleCrafter: Interactive Story Visualization with Multiple Characters | Accurate Story visualization requires several necessary elements, such as
identity consistency across frames, the alignment between plain text and visual
content, and a reasonable layout of objects in images. Most previous works
endeavor to meet these requirements by fitting a text-to-image (T2I) model on a
set of videos in the same style and with the same characters, e.g., the
FlintstonesSV dataset. However, the learned T2I models typically struggle to
adapt to new characters, scenes, and styles, and often lack the flexibility to
revise the layout of the synthesized images. This paper proposes a system for
generic interactive story visualization, capable of handling multiple novel
characters and supporting the editing of layout and local structure. It is
developed by leveraging the prior knowledge of large language and T2I models,
trained on massive corpora. The system comprises four interconnected
components: story-to-prompt generation (S2P), text-to-layout generation (T2L),
controllable text-to-image generation (C-T2I), and image-to-video animation
(I2V). First, the S2P module converts concise story information into detailed
prompts required for subsequent stages. Next, T2L generates diverse and
reasonable layouts based on the prompts, offering users the ability to adjust
and refine the layout to their preference. The core component, C-T2I, enables
the creation of images guided by layouts, sketches, and actor-specific
identifiers to maintain consistency and detail across visualizations. Finally,
I2V enriches the visualization process by animating the generated images.
Extensive experiments and a user study are conducted to validate the
effectiveness and flexibility of interactive editing of the proposed system. | https://huggingface.co/papers/2305.18247 |
2023-05-30 | 2305.18098 | 4 | BigTrans: Augmenting Large Language Models with Multilingual Translation
Capability over 100 Languages | Large language models (LLMs) demonstrate promising translation performance
among various natural languages. However, many LLMs especially the open-sourced
ones, such as BLOOM and LLaMA, are English-dominant and support only dozens of
natural languages, making the potential of LLMs on language translation less
explored. In this work, we present BigTrans which adapts LLaMA that covers only
20 languages and enhances it with multilingual translation capability on more
than 100 languages. BigTrans is built upon LLaMA-13B and it is optimized in
three steps. First, we continue training LLaMA with massive Chinese monolingual
data. Second, we continue training the model with a large-scale parallel
dataset that covers 102 natural languages. Third, we instruct-tune the
foundation model with multilingual translation instructions, leading to our
BigTrans model. The preliminary experiments on multilingual translation show
that BigTrans performs comparably with ChatGPT and Google Translate in many
languages and even outperforms ChatGPT in 8 language pairs. We release the
BigTrans model and hope it can advance the research progress. | https://huggingface.co/papers/2305.18098 |
2023-05-30 | 2305.18365 | 4 | What indeed can GPT models do in chemistry? A comprehensive benchmark on
eight tasks | Large Language Models (LLMs) with strong abilities in natural language
processing tasks have emerged and have been rapidly applied in various kinds of
areas such as science, finance and software engineering. However, the
capability of LLMs to advance the field of chemistry remains unclear. In this
paper,we establish a comprehensive benchmark containing 8 practical chemistry
tasks, including 1) name prediction, 2) property prediction, 3) yield
prediction, 4) reaction prediction, 5) retrosynthesis (prediction of reactants
from products), 6)text-based molecule design, 7) molecule captioning, and 8)
reagent selection. Our analysis draws on widely recognized datasets including
BBBP, Tox21, PubChem, USPTO, and ChEBI, facilitating a broad exploration of the
capacities of LLMs within the context of practical chemistry. Three GPT models
(GPT-4, GPT-3.5,and Davinci-003) are evaluated for each chemistry task in
zero-shot and few-shot in-context learning settings with carefully selected
demonstration examples and specially crafted prompts. The key results of our
investigation are 1) GPT-4 outperforms the other two models among the three
evaluated; 2) GPT models exhibit less competitive performance in tasks
demanding precise understanding of molecular SMILES representation, such as
reaction prediction and retrosynthesis;3) GPT models demonstrate strong
capabilities in text-related explanation tasks such as molecule captioning; and
4) GPT models exhibit comparable or better performance to classical machine
learning models when applied to chemical problems that can be transformed into
classification or ranking tasks, such as property prediction, and yield
prediction. | https://huggingface.co/papers/2305.18365 |
2023-05-30 | 2305.18583 | 3 | Controllable Text-to-Image Generation with GPT-4 | Current text-to-image generation models often struggle to follow textual
instructions, especially the ones requiring spatial reasoning. On the other
hand, Large Language Models (LLMs), such as GPT-4, have shown remarkable
precision in generating code snippets for sketching out text inputs
graphically, e.g., via TikZ. In this work, we introduce Control-GPT to guide
the diffusion-based text-to-image pipelines with programmatic sketches
generated by GPT-4, enhancing their abilities for instruction following.
Control-GPT works by querying GPT-4 to write TikZ code, and the generated
sketches are used as references alongside the text instructions for diffusion
models (e.g., ControlNet) to generate photo-realistic images. One major
challenge to training our pipeline is the lack of a dataset containing aligned
text, images, and sketches. We address the issue by converting instance masks
in existing datasets into polygons to mimic the sketches used at test time. As
a result, Control-GPT greatly boosts the controllability of image generation.
It establishes a new state-of-art on the spatial arrangement and object
positioning generation and enhances users' control of object positions, sizes,
etc., nearly doubling the accuracy of prior models. Our work, as a first
attempt, shows the potential for employing LLMs to enhance the performance in
computer vision tasks. | https://huggingface.co/papers/2305.18583 |
2023-05-30 | 2305.18286 | 3 | Photoswap: Personalized Subject Swapping in Images | In an era where images and visual content dominate our digital landscape, the
ability to manipulate and personalize these images has become a necessity.
Envision seamlessly substituting a tabby cat lounging on a sunlit window sill
in a photograph with your own playful puppy, all while preserving the original
charm and composition of the image. We present Photoswap, a novel approach that
enables this immersive image editing experience through personalized subject
swapping in existing images. Photoswap first learns the visual concept of the
subject from reference images and then swaps it into the target image using
pre-trained diffusion models in a training-free manner. We establish that a
well-conceptualized visual subject can be seamlessly transferred to any image
with appropriate self-attention and cross-attention manipulation, maintaining
the pose of the swapped subject and the overall coherence of the image.
Comprehensive experiments underscore the efficacy and controllability of
Photoswap in personalized subject swapping. Furthermore, Photoswap
significantly outperforms baseline methods in human ratings across subject
swapping, background preservation, and overall quality, revealing its vast
application potential, from entertainment to professional editing. | https://huggingface.co/papers/2305.18286 |
2023-05-30 | 2305.18264 | 3 | Gen-L-Video: Multi-Text to Long Video Generation via Temporal
Co-Denoising | Leveraging large-scale image-text datasets and advancements in diffusion
models, text-driven generative models have made remarkable strides in the field
of image generation and editing. This study explores the potential of extending
the text-driven ability to the generation and editing of multi-text conditioned
long videos. Current methodologies for video generation and editing, while
innovative, are often confined to extremely short videos (typically less than
24 frames) and are limited to a single text condition. These constraints
significantly limit their applications given that real-world videos usually
consist of multiple segments, each bearing different semantic information. To
address this challenge, we introduce a novel paradigm dubbed as Gen-L-Video,
capable of extending off-the-shelf short video diffusion models for generating
and editing videos comprising hundreds of frames with diverse semantic segments
without introducing additional training, all while preserving content
consistency. We have implemented three mainstream text-driven video generation
and editing methodologies and extended them to accommodate longer videos imbued
with a variety of semantic segments with our proposed paradigm. Our
experimental outcomes reveal that our approach significantly broadens the
generative and editing capabilities of video diffusion models, offering new
possibilities for future research and applications. The code is available at
https://github.com/G-U-N/Gen-L-Video. | https://huggingface.co/papers/2305.18264 |
2023-05-30 | 2305.17390 | 3 | SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex
Interactive Tasks | We introduce SwiftSage, a novel agent framework inspired by the dual-process
theory of human cognition, designed to excel in action planning for complex
interactive reasoning tasks. SwiftSage integrates the strengths of behavior
cloning and prompting large language models (LLMs) to enhance task completion
performance. The framework comprises two primary modules: the Swift module,
representing fast and intuitive thinking, and the Sage module, emulating
deliberate thought processes. The Swift module is a small encoder-decoder LM
fine-tuned on the oracle agent's action trajectories, while the Sage module
employs LLMs such as GPT-4 for subgoal planning and grounding. We develop a
heuristic method to harmoniously integrate the two modules, resulting in a more
efficient and robust problem-solving process. In 30 tasks from the ScienceWorld
benchmark, SwiftSage significantly outperforms other methods such as SayCan,
ReAct, and Reflexion, demonstrating its effectiveness in solving complex
interactive tasks. | https://huggingface.co/papers/2305.17390 |
2023-05-30 | 2305.17333 | 3 | Fine-Tuning Language Models with Just Forward Passes | Fine-tuning language models (LMs) has yielded success on diverse downstream
tasks, but as LMs grow in size, backpropagation requires a prohibitively large
amount of memory. Zeroth-order (ZO) methods can in principle estimate gradients
using only two forward passes but are theorized to be catastrophically slow for
optimizing large models. In this work, we propose a memory-efficient
zerothorder optimizer (MeZO), adapting the classical ZO-SGD method to operate
in-place, thereby fine-tuning LMs with the same memory footprint as inference.
For example, with a single A100 80GB GPU, MeZO can train a 30-billion parameter
model, whereas fine-tuning with backpropagation can train only a 2.7B LM with
the same budget. We conduct comprehensive experiments across model types
(masked and autoregressive LMs), model scales (up to 66B), and downstream tasks
(classification, multiple-choice, and generation). Our results demonstrate that
(1) MeZO significantly outperforms in-context learning and linear probing; (2)
MeZO achieves comparable performance to fine-tuning with backpropagation across
multiple tasks, with up to 12x memory reduction; (3) MeZO is compatible with
both full-parameter and parameter-efficient tuning techniques such as LoRA and
prefix tuning; (4) MeZO can effectively optimize non-differentiable objectives
(e.g., maximizing accuracy or F1). We support our empirical findings with
theoretical insights, highlighting how adequate pre-training and task prompts
enable MeZO to fine-tune huge models, despite classical ZO analyses suggesting
otherwise. | https://huggingface.co/papers/2305.17333 |
2023-05-30 | 2305.18259 | 2 | GlyphControl: Glyph Conditional Control for Visual Text Generation | Recently, there has been a growing interest in developing diffusion-based
text-to-image generative models capable of generating coherent and well-formed
visual text. In this paper, we propose a novel and efficient approach called
GlyphControl to address this task. Unlike existing methods that rely on
character-aware text encoders like ByT5 and require retraining of text-to-image
models, our approach leverages additional glyph conditional information to
enhance the performance of the off-the-shelf Stable-Diffusion model in
generating accurate visual text. By incorporating glyph instructions, users can
customize the content, location, and size of the generated text according to
their specific requirements. To facilitate further research in visual text
generation, we construct a training benchmark dataset called LAION-Glyph. We
evaluate the effectiveness of our approach by measuring OCR-based metrics and
CLIP scores of the generated visual text. Our empirical evaluations demonstrate
that GlyphControl outperforms the recent DeepFloyd IF approach in terms of OCR
accuracy and CLIP scores, highlighting the efficacy of our method. | https://huggingface.co/papers/2305.18259 |
2023-05-30 | 2305.17306 | 2 | Chain-of-Thought Hub: A Continuous Effort to Measure Large Language
Models' Reasoning Performance | As large language models (LLMs) are continuously being developed, their
evaluation becomes increasingly important yet challenging. This work proposes
Chain-of-Thought Hub, an open-source evaluation suite on the multi-step
reasoning capabilities of large language models. We are interested in this
setting for two reasons: (1) from the behavior of GPT and PaLM model family, we
observe that complex reasoning is likely to be a key differentiator between
weaker and stronger LLMs; (2) we envisage large language models to become the
next-generation computational platform and foster an ecosystem of LLM-based new
applications, this naturally requires the foundation models to perform complex
tasks that often involve the composition of linguistic and logical operations.
Our approach is to compile a suite of challenging reasoning benchmarks to track
the progress of LLMs. Our current results show that: (1) model scale clearly
correlates with reasoning capabilities; (2) As of May 2023, Claude-v1.3 and
PaLM-2 are the only two models that are comparable with GPT-4, while
open-sourced models still lag behind; (3) LLaMA-65B performs closely to
code-davinci-002, indicating that with successful further development such as
reinforcement learning from human feedback (RLHF), it has great potential to be
close to GPT-3.5-Turbo. Our results also suggest that for the open-source
efforts to catch up, the community may focus more on building better base
models and exploring RLHF. | https://huggingface.co/papers/2305.17306 |
2023-05-30 | 2305.17144 | 2 | Ghost in the Minecraft: Generally Capable Agents for Open-World
Enviroments via Large Language Models with Text-based Knowledge and Memory | The captivating realm of Minecraft has attracted substantial research
interest in recent years, serving as a rich platform for developing intelligent
agents capable of functioning in open-world environments. However, the current
research landscape predominantly focuses on specific objectives, such as the
popular "ObtainDiamond" task, and has not yet shown effective generalization to
a broader spectrum of tasks. Furthermore, the current leading success rate for
the "ObtainDiamond" task stands at around 20%, highlighting the limitations of
Reinforcement Learning (RL) based controllers used in existing methods. To
tackle these challenges, we introduce Ghost in the Minecraft (GITM), a novel
framework integrates Large Language Models (LLMs) with text-based knowledge and
memory, aiming to create Generally Capable Agents (GCAs) in Minecraft. These
agents, equipped with the logic and common sense capabilities of LLMs, can
skillfully navigate complex, sparse-reward environments with text-based
interactions. We develop a set of structured actions and leverage LLMs to
generate action plans for the agents to execute. The resulting LLM-based agent
markedly surpasses previous methods, achieving a remarkable improvement of
+47.5% in success rate on the "ObtainDiamond" task, demonstrating superior
robustness compared to traditional RL-based controllers. Notably, our agent is
the first to procure all items in the Minecraft Overworld technology tree,
demonstrating its extensive capabilities. GITM does not need any GPU for
training, but a single CPU node with 32 CPU cores is enough. This research
shows the potential of LLMs in developing capable agents for handling
long-horizon, complex tasks and adapting to uncertainties in open-world
environments. See the project website at https://github.com/OpenGVLab/GITM. | https://huggingface.co/papers/2305.17144 |
2023-05-30 | 2305.17359 | 1 | DNA-GPT: Divergent N-Gram Analysis for Training-Free Detection of
GPT-Generated Text | Large language models (LLMs) have notably enhanced the fluency and diversity
of machine-generated text. However, this progress also presents a significant
challenge in detecting the origin of a given text, and current research on
detection methods lags behind the rapid evolution of LLMs. Conventional
training-based methods have limitations in flexibility, particularly when
adapting to new domains, and they often lack explanatory power. To address this
gap, we propose a novel training-free detection strategy called Divergent
N-Gram Analysis (DNA-GPT). Given a text, we first truncate it in the middle and
then use only the preceding portion as input to the LLMs to regenerate the new
remaining parts. By analyzing the differences between the original and new
remaining parts through N-gram analysis in black-box or probability divergence
in white-box, we can clearly illustrate significant discrepancies between
machine-generated and human-written text. We conducted extensive experiments on
the most advanced LLMs from OpenAI, including text-davinci-003, GPT-3.5-turbo,
and GPT-4, as well as open-source models such as GPT-NeoX-20B and LLaMa-13B.
Results show that our zero-shot approach exhibits state-of-the-art performance
in distinguishing between human and GPT-generated text on four English and one
German dataset, outperforming OpenAI's own classifier, which is trained on
millions of text. Additionally, our methods provide reasonable explanations and
evidence to support our claim, which is a unique feature of explainable
detection. Our method is also robust under the revised text attack and can
additionally solve model sourcing. Codes are available at
https://github.com/Xianjun-Yang/DNA-GPT. | https://huggingface.co/papers/2305.17359 |
2023-05-30 | 2305.18231 | 1 | High-Fidelity Image Compression with Score-based Generative Models | Despite the tremendous success of diffusion generative models in
text-to-image generation, replicating this success in the domain of image
compression has proven difficult. In this paper, we demonstrate that diffusion
can significantly improve perceptual quality at a given bit-rate, outperforming
state-of-the-art approaches PO-ELIC and HiFiC as measured by FID score. This is
achieved using a simple but theoretically motivated two-stage approach
combining an autoencoder targeting MSE followed by a further score-based
decoder. However, as we will show, implementation details matter and the
optimal design decisions can differ greatly from typical text-to-image models. | https://huggingface.co/papers/2305.18231 |
2023-05-31 | 2305.20030 | 8 | Tree-Ring Watermarks: Fingerprints for Diffusion Images that are
Invisible and Robust | Watermarking the outputs of generative models is a crucial technique for
tracing copyright and preventing potential harm from AI-generated content. In
this paper, we introduce a novel technique called Tree-Ring Watermarking that
robustly fingerprints diffusion model outputs. Unlike existing methods that
perform post-hoc modifications to images after sampling, Tree-Ring Watermarking
subtly influences the entire sampling process, resulting in a model fingerprint
that is invisible to humans. The watermark embeds a pattern into the initial
noise vector used for sampling. These patterns are structured in Fourier space
so that they are invariant to convolutions, crops, dilations, flips, and
rotations. After image generation, the watermark signal is detected by
inverting the diffusion process to retrieve the noise vector, which is then
checked for the embedded signal. We demonstrate that this technique can be
easily applied to arbitrary diffusion models, including text-conditioned Stable
Diffusion, as a plug-in with negligible loss in FID. Our watermark is
semantically hidden in the image space and is far more robust than watermarking
alternatives that are currently deployed. Code is available at
github.com/YuxinWenRick/tree-ring-watermark. | https://huggingface.co/papers/2305.20030 |
2023-05-31 | 2305.18654 | 7 | Faith and Fate: Limits of Transformers on Compositionality | Transformer large language models (LLMs) have sparked admiration for their
exceptional performance on tasks that demand intricate multi-step reasoning.
Yet, these models simultaneously show failures on surprisingly trivial
problems. This begs the question: Are these errors incidental, or do they
signal more substantial limitations? In an attempt to demystify transformer
LLMs, we investigate the limits of these models across three representative
compositional tasks -- multi-digit multiplication, logic grid puzzles, and a
classic dynamic programming problem. These tasks require breaking problems down
into sub-steps and synthesizing these steps into a precise answer. We formulate
compositional tasks as computation graphs to systematically quantify the level
of complexity, and break down reasoning steps into intermediate sub-procedures.
Our empirical findings suggest that transformer LLMs solve compositional tasks
by reducing multi-step compositional reasoning into linearized subgraph
matching, without necessarily developing systematic problem-solving skills. To
round off our empirical study, we provide theoretical arguments on abstract
multi-step reasoning problems that highlight how autoregressive generations'
performance can rapidly decay with\,increased\,task\,complexity. | https://huggingface.co/papers/2305.18654 |
2023-05-31 | 2305.18766 | 6 | HiFA: High-fidelity Text-to-3D with Advanced Diffusion Guidance | Automatic text-to-3D synthesis has achieved remarkable advancements through
the optimization of 3D models. Existing methods commonly rely on pre-trained
text-to-image generative models, such as diffusion models, providing scores for
2D renderings of Neural Radiance Fields (NeRFs) and being utilized for
optimizing NeRFs. However, these methods often encounter artifacts and
inconsistencies across multiple views due to their limited understanding of 3D
geometry. To address these limitations, we propose a reformulation of the
optimization loss using the diffusion prior. Furthermore, we introduce a novel
training approach that unlocks the potential of the diffusion prior. To improve
3D geometry representation, we apply auxiliary depth supervision for
NeRF-rendered images and regularize the density field of NeRFs. Extensive
experiments demonstrate the superiority of our method over prior works,
resulting in advanced photo-realism and improved multi-view consistency. | https://huggingface.co/papers/2305.18766 |
2023-05-31 | 2305.19012 | 4 | StyleAvatar3D: Leveraging Image-Text Diffusion Models for High-Fidelity
3D Avatar Generation | The recent advancements in image-text diffusion models have stimulated
research interest in large-scale 3D generative models. Nevertheless, the
limited availability of diverse 3D resources presents significant challenges to
learning. In this paper, we present a novel method for generating high-quality,
stylized 3D avatars that utilizes pre-trained image-text diffusion models for
data generation and a Generative Adversarial Network (GAN)-based 3D generation
network for training. Our method leverages the comprehensive priors of
appearance and geometry offered by image-text diffusion models to generate
multi-view images of avatars in various styles. During data generation, we
employ poses extracted from existing 3D models to guide the generation of
multi-view images. To address the misalignment between poses and images in
data, we investigate view-specific prompts and develop a coarse-to-fine
discriminator for GAN training. We also delve into attribute-related prompts to
increase the diversity of the generated avatars. Additionally, we develop a
latent diffusion model within the style space of StyleGAN to enable the
generation of avatars based on image inputs. Our approach demonstrates superior
performance over current state-of-the-art methods in terms of visual quality
and diversity of the produced avatars. | https://huggingface.co/papers/2305.19012 |
2023-05-31 | 2305.18802 | 4 | LibriTTS-R: A Restored Multi-Speaker Text-to-Speech Corpus | This paper introduces a new speech dataset called ``LibriTTS-R'' designed for
text-to-speech (TTS) use. It is derived by applying speech restoration to the
LibriTTS corpus, which consists of 585 hours of speech data at 24 kHz sampling
rate from 2,456 speakers and the corresponding texts. The constituent samples
of LibriTTS-R are identical to those of LibriTTS, with only the sound quality
improved. Experimental results show that the LibriTTS-R ground-truth samples
showed significantly improved sound quality compared to those in LibriTTS. In
addition, neural end-to-end TTS trained with LibriTTS-R achieved speech
naturalness on par with that of the ground-truth samples. The corpus is freely
available for download from http://www.openslr.org/141/. | https://huggingface.co/papers/2305.18802 |
2023-05-31 | 2305.18729 | 4 | Real-World Image Variation by Aligning Diffusion Inversion Chain | Recent diffusion model advancements have enabled high-fidelity images to be
generated using text prompts. However, a domain gap exists between generated
images and real-world images, which poses a challenge in generating
high-quality variations of real-world images. Our investigation uncovers that
this domain gap originates from a latents' distribution gap in different
diffusion processes. To address this issue, we propose a novel inference
pipeline called Real-world Image Variation by ALignment (RIVAL) that utilizes
diffusion models to generate image variations from a single image exemplar. Our
pipeline enhances the generation quality of image variations by aligning the
image generation process to the source image's inversion chain. Specifically,
we demonstrate that step-wise latent distribution alignment is essential for
generating high-quality variations. To attain this, we design a cross-image
self-attention injection for feature interaction and a step-wise distribution
normalization to align the latent features. Incorporating these alignment
processes into a diffusion model allows RIVAL to generate high-quality image
variations without further parameter optimization. Our experimental results
demonstrate that our proposed approach outperforms existing methods with
respect to semantic-condition similarity and perceptual quality. Furthermore,
this generalized inference pipeline can be easily applied to other
diffusion-based generation tasks, such as image-conditioned text-to-image
generation and example-based image inpainting. | https://huggingface.co/papers/2305.18729 |
2023-05-31 | 2305.19234 | 3 | Grammar Prompting for Domain-Specific Language Generation with Large
Language Models | Large language models (LLMs) can learn to perform a wide range of natural
language tasks from just a handful of in-context examples. However, for
generating strings from highly structured languages (e.g., semantic parsing to
complex domain-specific languages), it is challenging for the LLM to generalize
from just a few exemplars. We explore grammar prompting as a simple
approach for enabling LLMs to use external knowledge and domain-specific
constraints, expressed through a grammar expressed in Backus--Naur Form (BNF),
during in-context learning. Grammar prompting augments each demonstration
example with a specialized grammar that is minimally sufficient for generating
the particular output example, where the specialized grammar is a subset of the
full DSL grammar. For inference, the LLM first predicts a BNF grammar given a
test input, and then generates the output according to the rules of the
grammar. Experiments demonstrate that grammar prompting can enable LLMs to
perform competitively on a diverse set of DSL generation tasks, including
semantic parsing (SMCalFlow, Overnight, GeoQuery), PDDL planning, and even
molecule generation (SMILES). | https://huggingface.co/papers/2305.19234 |
2023-05-31 | 2305.18565 | 3 | PaLI-X: On Scaling up a Multilingual Vision and Language Model | We present the training recipe and results of scaling up PaLI-X, a
multilingual vision and language model, both in terms of size of the components
and the breadth of its training task mixture. Our model achieves new levels of
performance on a wide-range of varied and complex tasks, including multiple
image-based captioning and question-answering tasks, image-based document
understanding and few-shot (in-context) learning, as well as object detection,
video question answering, and video captioning. PaLI-X advances the
state-of-the-art on most vision-and-language benchmarks considered (25+ of
them). Finally, we observe emerging capabilities, such as complex counting and
multilingual object detection, tasks that are not explicitly in the training
mix. | https://huggingface.co/papers/2305.18565 |
2023-05-31 | 2305.18474 | 3 | Make-An-Audio 2: Temporal-Enhanced Text-to-Audio Generation | Large diffusion models have been successful in text-to-audio (T2A) synthesis
tasks, but they often suffer from common issues such as semantic misalignment
and poor temporal consistency due to limited natural language understanding and
data scarcity. Additionally, 2D spatial structures widely used in T2A works
lead to unsatisfactory audio quality when generating variable-length audio
samples since they do not adequately prioritize temporal information. To
address these challenges, we propose Make-an-Audio 2, a latent diffusion-based
T2A method that builds on the success of Make-an-Audio. Our approach includes
several techniques to improve semantic alignment and temporal consistency:
Firstly, we use pre-trained large language models (LLMs) to parse the text into
structured <event & order> pairs for better temporal information capture. We
also introduce another structured-text encoder to aid in learning semantic
alignment during the diffusion denoising process. To improve the performance of
variable length generation and enhance the temporal information extraction, we
design a feed-forward Transformer-based diffusion denoiser. Finally, we use
LLMs to augment and transform a large amount of audio-label data into
audio-text datasets to alleviate the problem of scarcity of temporal data.
Extensive experiments show that our method outperforms baseline models in both
objective and subjective metrics, and achieves significant gains in temporal
information understanding, semantic consistency, and sound quality. | https://huggingface.co/papers/2305.18474 |
2023-05-31 | 2305.19245 | 2 | AlteredAvatar: Stylizing Dynamic 3D Avatars with Fast Style Adaptation | This paper presents a method that can quickly adapt dynamic 3D avatars to
arbitrary text descriptions of novel styles. Among existing approaches for
avatar stylization, direct optimization methods can produce excellent results
for arbitrary styles but they are unpleasantly slow. Furthermore, they require
redoing the optimization process from scratch for every new input. Fast
approximation methods using feed-forward networks trained on a large dataset of
style images can generate results for new inputs quickly, but tend not to
generalize well to novel styles and fall short in quality. We therefore
investigate a new approach, AlteredAvatar, that combines those two approaches
using the meta-learning framework. In the inner loop, the model learns to
optimize to match a single target style well; while in the outer loop, the
model learns to stylize efficiently across many styles. After training,
AlteredAvatar learns an initialization that can quickly adapt within a small
number of update steps to a novel style, which can be given using texts, a
reference image, or a combination of both. We show that AlteredAvatar can
achieve a good balance between speed, flexibility and quality, while
maintaining consistency across a wide range of novel views and facial
expressions. | https://huggingface.co/papers/2305.19245 |
2023-05-31 | 2305.19164 | 2 | LANCE: Stress-testing Visual Models by Generating Language-guided
Counterfactual Images | We propose an automated algorithm to stress-test a trained visual model by
generating language-guided counterfactual test images (LANCE). Our method
leverages recent progress in large language modeling and text-based image
editing to augment an IID test set with a suite of diverse, realistic, and
challenging test images without altering model weights. We benchmark the
performance of a diverse set of pretrained models on our generated data and
observe significant and consistent performance drops. We further analyze model
sensitivity across different types of edits, and demonstrate its applicability
at surfacing previously unknown class-level model biases in ImageNet. | https://huggingface.co/papers/2305.19164 |
2023-05-31 | 2305.18415 | 2 | Geometric Algebra Transformers | Problems involving geometric data arise in a variety of fields, including
computer vision, robotics, chemistry, and physics. Such data can take numerous
forms, such as points, direction vectors, planes, or transformations, but to
date there is no single architecture that can be applied to such a wide variety
of geometric types while respecting their symmetries. In this paper we
introduce the Geometric Algebra Transformer (GATr), a general-purpose
architecture for geometric data. GATr represents inputs, outputs, and hidden
states in the projective geometric algebra, which offers an efficient
16-dimensional vector space representation of common geometric objects as well
as operators acting on them. GATr is equivariant with respect to E(3), the
symmetry group of 3D Euclidean space. As a transformer, GATr is scalable,
expressive, and versatile. In experiments with n-body modeling and robotic
planning, GATr shows strong improvements over non-geometric baselines. | https://huggingface.co/papers/2305.18415 |
2023-05-31 | 2305.19066 | 1 | Nested Diffusion Processes for Anytime Image Generation | Diffusion models are the current state-of-the-art in image generation,
synthesizing high-quality images by breaking down the generation process into
many fine-grained denoising steps. Despite their good performance, diffusion
models are computationally expensive, requiring many neural function
evaluations (NFEs). In this work, we propose an anytime diffusion-based method
that can generate viable images when stopped at arbitrary times before
completion. Using existing pretrained diffusion models, we show that the
generation scheme can be recomposed as two nested diffusion processes, enabling
fast iterative refinement of a generated image. We use this Nested Diffusion
approach to peek into the generation process and enable flexible scheduling
based on the instantaneous preference of the user. In experiments on ImageNet
and Stable Diffusion-based text-to-image generation, we show, both
qualitatively and quantitatively, that our method's intermediate generation
quality greatly exceeds that of the original diffusion model, while the final
slow generation result remains comparable. | https://huggingface.co/papers/2305.19066 |
2023-05-31 | 2305.18373 | 1 | KAFA: Rethinking Image Ad Understanding with Knowledge-Augmented Feature
Adaptation of Vision-Language Models | Image ad understanding is a crucial task with wide real-world applications.
Although highly challenging with the involvement of diverse atypical scenes,
real-world entities, and reasoning over scene-texts, how to interpret image ads
is relatively under-explored, especially in the era of foundational
vision-language models (VLMs) featuring impressive generalizability and
adaptability. In this paper, we perform the first empirical study of image ad
understanding through the lens of pre-trained VLMs. We benchmark and reveal
practical challenges in adapting these VLMs to image ad understanding. We
propose a simple feature adaptation strategy to effectively fuse multimodal
information for image ads and further empower it with knowledge of real-world
entities. We hope our study draws more attention to image ad understanding
which is broadly relevant to the advertising industry. | https://huggingface.co/papers/2305.18373 |
2023-06-01 | 2306.00890 | 11 | LLaVA-Med: Training a Large Language-and-Vision Assistant for
Biomedicine in One Day | Conversational generative AI has demonstrated remarkable promise for
empowering biomedical practitioners, but current investigations focus on
unimodal text. Multimodal conversational AI has seen rapid progress by
leveraging billions of image-text pairs from the public web, but such
general-domain vision-language models still lack sophistication in
understanding and conversing about biomedical images. In this paper, we propose
a cost-efficient approach for training a vision-language conversational
assistant that can answer open-ended research questions of biomedical images.
The key idea is to leverage a large-scale, broad-coverage biomedical
figure-caption dataset extracted from PubMed Central, use GPT-4 to
self-instruct open-ended instruction-following data from the captions, and then
fine-tune a large general-domain vision-language model using a novel curriculum
learning method. Specifically, the model first learns to align biomedical
vocabulary using the figure-caption pairs as is, then learns to master
open-ended conversational semantics using GPT-4 generated instruction-following
data, broadly mimicking how a layperson gradually acquires biomedical
knowledge. This enables us to train a Large Language and Vision Assistant for
BioMedicine (LLaVA-Med) in less than 15 hours (with eight A100s). LLaVA-Med
exhibits excellent multimodal conversational capability and can follow
open-ended instruction to assist with inquiries about a biomedical image. On
three standard biomedical visual question answering datasets, LLaVA-Med
outperforms previous supervised state-of-the-art on certain metrics. To
facilitate biomedical multimodal research, we will release our
instruction-following data and the LLaVA-Med model. | https://huggingface.co/papers/2306.00890 |
2023-06-01 | 2306.00983 | 7 | StyleDrop: Text-to-Image Generation in Any Style | Pre-trained large text-to-image models synthesize impressive images with an
appropriate use of text prompts. However, ambiguities inherent in natural
language and out-of-distribution effects make it hard to synthesize image
styles, that leverage a specific design pattern, texture or material. In this
paper, we introduce StyleDrop, a method that enables the synthesis of images
that faithfully follow a specific style using a text-to-image model. The
proposed method is extremely versatile and captures nuances and details of a
user-provided style, such as color schemes, shading, design patterns, and local
and global effects. It efficiently learns a new style by fine-tuning very few
trainable parameters (less than 1% of total model parameters) and improving
the quality via iterative training with either human or automated feedback.
Better yet, StyleDrop is able to deliver impressive results even when the user
supplies only a single image that specifies the desired style. An extensive
study shows that, for the task of style tuning text-to-image models, StyleDrop
implemented on Muse convincingly outperforms other methods, including
DreamBooth and textual inversion on Imagen or Stable Diffusion. More results
are available at our project website: https://styledrop.github.io | https://huggingface.co/papers/2306.00983 |
2023-06-01 | 2305.19452 | 4 | Bigger, Better, Faster: Human-level Atari with human-level efficiency | We introduce a value-based RL agent, which we call BBF, that achieves
super-human performance in the Atari 100K benchmark. BBF relies on scaling the
neural networks used for value estimation, as well as a number of other design
choices that enable this scaling in a sample-efficient manner. We conduct
extensive analyses of these design choices and provide insights for future
work. We end with a discussion about updating the goalposts for
sample-efficient RL research on the ALE. We make our code and data publicly
available at
https://github.com/google-research/google-research/tree/master/bigger_better_faster. | https://huggingface.co/papers/2305.19452 |
2023-06-01 | 2305.20086 | 3 | Understanding and Mitigating Copying in Diffusion Models | Images generated by diffusion models like Stable Diffusion are increasingly
widespread. Recent works and even lawsuits have shown that these models are
prone to replicating their training data, unbeknownst to the user. In this
paper, we first analyze this memorization problem in text-to-image diffusion
models. While it is widely believed that duplicated images in the training set
are responsible for content replication at inference time, we observe that the
text conditioning of the model plays a similarly important role. In fact, we
see in our experiments that data replication often does not happen for
unconditional models, while it is common in the text-conditional case.
Motivated by our findings, we then propose several techniques for reducing data
replication at both training and inference time by randomizing and augmenting
image captions in the training set. | https://huggingface.co/papers/2305.20086 |
2023-06-01 | 2305.19370 | 3 | Blockwise Parallel Transformer for Long Context Large Models | Transformers have emerged as the cornerstone of state-of-the-art natural
language processing models, showcasing exceptional performance across a wide
range of AI applications. However, the memory demands posed by the
self-attention mechanism and the large feedforward network in Transformers
limit their ability to handle long sequences, thereby creating challenges for
tasks involving multiple long sequences or long-term dependencies. We present a
distinct approach, Blockwise Parallel Transformer (BPT), that leverages
blockwise computation of self-attention and feedforward network fusion to
minimize memory costs. By processing longer input sequences while maintaining
memory efficiency, BPT enables training sequences 32 times longer than vanilla
Transformers and up to 4 times longer than previous memory-efficient methods.
Extensive experiments on language modeling and reinforcement learning tasks
demonstrate the effectiveness of BPT in reducing memory requirements and
improving performance. | https://huggingface.co/papers/2305.19370 |
2023-06-01 | 2306.00622 | 2 | ReviewerGPT? An Exploratory Study on Using Large Language Models for
Paper Reviewing | Given the rapid ascent of large language models (LLMs), we study the
question: (How) can large language models help in reviewing of scientific
papers or proposals? We first conduct some pilot studies where we find that (i)
GPT-4 outperforms other LLMs (Bard, Vicuna, Koala, Alpaca, LLaMa, Dolly,
OpenAssistant, StableLM), and (ii) prompting with a specific question (e.g., to
identify errors) outperforms prompting to simply write a review. With these
insights, we study the use of LLMs (specifically, GPT-4) for three tasks:
1. Identifying errors: We construct 13 short computer science papers each
with a deliberately inserted error, and ask the LLM to check for the
correctness of these papers. We observe that the LLM finds errors in 7 of them,
spanning both mathematical and conceptual errors.
2. Verifying checklists: We task the LLM to verify 16 closed-ended checklist
questions in the respective sections of 15 NeurIPS 2022 papers. We find that
across 119 {checklist question, paper} pairs, the LLM had an 86.6% accuracy.
3. Choosing the "better" paper: We generate 10 pairs of abstracts,
deliberately designing each pair in such a way that one abstract was clearly
superior than the other. The LLM, however, struggled to discern these
relatively straightforward distinctions accurately, committing errors in its
evaluations for 6 out of the 10 pairs.
Based on these experiments, we think that LLMs have a promising use as
reviewing assistants for specific reviewing tasks, but not (yet) for complete
evaluations of papers or proposals. | https://huggingface.co/papers/2306.00622 |
2023-06-01 | 2305.20088 | 2 | Improving CLIP Training with Language Rewrites | Contrastive Language-Image Pre-training (CLIP) stands as one of the most
effective and scalable methods for training transferable vision models using
paired image and text data. CLIP models are trained using contrastive loss,
which typically relies on data augmentations to prevent overfitting and
shortcuts. However, in the CLIP training paradigm, data augmentations are
exclusively applied to image inputs, while language inputs remain unchanged
throughout the entire training process, limiting the exposure of diverse texts
to the same image. In this paper, we introduce Language augmented CLIP
(LaCLIP), a simple yet highly effective approach to enhance CLIP training
through language rewrites. Leveraging the in-context learning capability of
large language models, we rewrite the text descriptions associated with each
image. These rewritten texts exhibit diversity in sentence structure and
vocabulary while preserving the original key concepts and meanings. During
training, LaCLIP randomly selects either the original texts or the rewritten
versions as text augmentations for each image. Extensive experiments on CC3M,
CC12M, RedCaps and LAION-400M datasets show that CLIP pre-training with
language rewrites significantly improves the transfer performance without
computation or memory overhead during training. Specifically for ImageNet
zero-shot accuracy, LaCLIP outperforms CLIP by 8.2% on CC12M and 2.4% on
LAION-400M. Code is available at https://github.com/LijieFan/LaCLIP. | https://huggingface.co/papers/2305.20088 |
2023-06-01 | 2305.20082 | 2 | Control4D: Dynamic Portrait Editing by Learning 4D GAN from 2D
Diffusion-based Editor | Recent years have witnessed considerable achievements in editing images with
text instructions. When applying these editors to dynamic scene editing, the
new-style scene tends to be temporally inconsistent due to the frame-by-frame
nature of these 2D editors. To tackle this issue, we propose Control4D, a novel
approach for high-fidelity and temporally consistent 4D portrait editing.
Control4D is built upon an efficient 4D representation with a 2D
diffusion-based editor. Instead of using direct supervisions from the editor,
our method learns a 4D GAN from it and avoids the inconsistent supervision
signals. Specifically, we employ a discriminator to learn the generation
distribution based on the edited images and then update the generator with the
discrimination signals. For more stable training, multi-level information is
extracted from the edited images and used to facilitate the learning of the
generator. Experimental results show that Control4D surpasses previous
approaches and achieves more photo-realistic and consistent 4D editing
performances. The link to our project website is
https://control4darxiv.github.io. | https://huggingface.co/papers/2305.20082 |
2023-06-01 | 2305.20081 | 2 | Efficient Diffusion Policies for Offline Reinforcement Learning | Offline reinforcement learning (RL) aims to learn optimal policies from
offline datasets, where the parameterization of policies is crucial but often
overlooked. Recently, Diffsuion-QL significantly boosts the performance of
offline RL by representing a policy with a diffusion model, whose success
relies on a parametrized Markov Chain with hundreds of steps for sampling.
However, Diffusion-QL suffers from two critical limitations. 1) It is
computationally inefficient to forward and backward through the whole Markov
chain during training. 2) It is incompatible with maximum likelihood-based RL
algorithms (e.g., policy gradient methods) as the likelihood of diffusion
models is intractable. Therefore, we propose efficient diffusion policy (EDP)
to overcome these two challenges. EDP approximately constructs actions from
corrupted ones at training to avoid running the sampling chain. We conduct
extensive experiments on the D4RL benchmark. The results show that EDP can
reduce the diffusion policy training time from 5 days to 5 hours on
gym-locomotion tasks. Moreover, we show that EDP is compatible with various
offline RL algorithms (TD3, CRR, and IQL) and achieves new state-of-the-art on
D4RL by large margins over previous methods. Our code is available at
https://github.com/sail-sg/edp. | https://huggingface.co/papers/2305.20081 |
2023-06-01 | 2305.20091 | 1 | Humans in 4D: Reconstructing and Tracking Humans with Transformers | We present an approach to reconstruct humans and track them over time. At the
core of our approach, we propose a fully "transformerized" version of a network
for human mesh recovery. This network, HMR 2.0, advances the state of the art
and shows the capability to analyze unusual poses that have in the past been
difficult to reconstruct from single images. To analyze video, we use 3D
reconstructions from HMR 2.0 as input to a tracking system that operates in 3D.
This enables us to deal with multiple people and maintain identities through
occlusion events. Our complete approach, 4DHumans, achieves state-of-the-art
results for tracking people from monocular video. Furthermore, we demonstrate
the effectiveness of HMR 2.0 on the downstream task of action recognition,
achieving significant improvements over previous pose-based action recognition
approaches. Our code and models are available on the project website:
https://shubham-goel.github.io/4dhumans/. | https://huggingface.co/papers/2305.20091 |
2023-06-01 | 2305.20010 | 1 | Human or Not? A Gamified Approach to the Turing Test | We present "Human or Not?", an online game inspired by the Turing test, that
measures the capability of AI chatbots to mimic humans in dialog, and of humans
to tell bots from other humans. Over the course of a month, the game was played
by over 1.5 million users who engaged in anonymous two-minute chat sessions
with either another human or an AI language model which was prompted to behave
like humans. The task of the players was to correctly guess whether they spoke
to a person or to an AI. This largest scale Turing-style test conducted to date
revealed some interesting facts. For example, overall users guessed the
identity of their partners correctly in only 68% of the games. In the subset of
the games in which users faced an AI bot, users had even lower correct guess
rates of 60% (that is, not much higher than chance). This white paper details
the development, deployment, and results of this unique experiment. While this
experiment calls for many extensions and refinements, these findings already
begin to shed light on the inevitable near future which will commingle humans
and AI. | https://huggingface.co/papers/2305.20010 |
2023-06-01 | 2305.19835 | 1 | Deliberate then Generate: Enhanced Prompting Framework for Text
Generation | Large language models (LLMs) have shown remarkable success across a wide
range of natural language generation tasks, where proper prompt designs make
great impacts. While existing prompting methods are normally restricted to
providing correct information, in this paper, we encourage the model to
deliberate by proposing a novel Deliberate then Generate (DTG) prompting
framework, which consists of error detection instructions and candidates that
may contain errors. DTG is a simple yet effective technique that can be applied
to various text generation tasks with minimal modifications. We conduct
extensive experiments on 20+ datasets across 7 text generation tasks, including
summarization, translation, dialogue, and more. We show that DTG consistently
outperforms existing prompting methods and achieves state-of-the-art
performance on multiple text generation tasks. We also provide in-depth
analyses to reveal the underlying mechanisms of DTG, which may inspire future
research on prompting for LLMs. | https://huggingface.co/papers/2305.19835 |
2023-06-01 | 2305.19472 | 1 | PlaSma: Making Small Language Models Better Procedural Knowledge Models
for (Counterfactual) Planning | Procedural planning, which entails decomposing a high-level goal into a
sequence of temporally ordered steps, is an important yet intricate task for
machines. It involves integrating common-sense knowledge to reason about
complex contextualized situations that are often counterfactual, e.g.
"scheduling a doctor's appointment without a phone". While current approaches
show encouraging results using large language models (LLMs), they are hindered
by drawbacks such as costly API calls and reproducibility issues. In this
paper, we advocate planning using smaller language models. We present PlaSma, a
novel two-pronged approach to endow small language models with procedural
knowledge and (counterfactual) planning capabilities. More concretely, we
develop symbolic procedural knowledge distillation to enhance the implicit
knowledge in small language models and an inference-time algorithm to
facilitate more structured and accurate reasoning. In addition, we introduce a
novel task, Counterfactual Planning, that requires a revision of a plan to cope
with a counterfactual situation. In both the original and counterfactual
setting, we show that orders-of-magnitude smaller models (770M-11B parameters)
can compete and often surpass their larger teacher models' capabilities. | https://huggingface.co/papers/2305.19472 |
2023-06-02 | 2306.00739 | 20 | SQL-PaLM: Improved Large Language ModelAdaptation for Text-to-SQL | One impressive emergent capability of large language models (LLMs) is
generation of code, including Structured Query Language (SQL) for databases.
For the task of converting natural language text to SQL queries, Text-to-SQL,
adaptation of LLMs is of paramount importance, both in in-context learning and
fine-tuning settings, depending on the amount of adaptation data used. In this
paper, we propose an LLM-based Text-to-SQL model SQL-PaLM, leveraging on
PaLM-2, that pushes the state-of-the-art in both settings. Few-shot SQL-PaLM is
based on an execution-based self-consistency prompting approach designed for
Text-to-SQL, and achieves 77.3% in test-suite accuracy on Spider, which to our
best knowledge is the first to outperform previous state-of-the-art with
fine-tuning by a significant margin, 4%. Furthermore, we demonstrate that the
fine-tuned SQL-PALM outperforms it further by another 1%. Towards applying
SQL-PaLM to real-world scenarios we further evaluate its robustness on other
challenging variants of Spider and demonstrate the superior generalization
capability of SQL-PaLM. In addition, via extensive case studies, we demonstrate
the impressive intelligent capabilities and various success enablers of
LLM-based Text-to-SQL. | https://huggingface.co/papers/2306.00739 |
2023-06-02 | 2306.00980 | 15 | SnapFusion: Text-to-Image Diffusion Model on Mobile Devices within Two
Seconds | Text-to-image diffusion models can create stunning images from natural
language descriptions that rival the work of professional artists and
photographers. However, these models are large, with complex network
architectures and tens of denoising iterations, making them computationally
expensive and slow to run. As a result, high-end GPUs and cloud-based inference
are required to run diffusion models at scale. This is costly and has privacy
implications, especially when user data is sent to a third party. To overcome
these challenges, we present a generic approach that, for the first time,
unlocks running text-to-image diffusion models on mobile devices in less than
$2$ seconds. We achieve so by introducing efficient network architecture and
improving step distillation. Specifically, we propose an efficient UNet by
identifying the redundancy of the original model and reducing the computation
of the image decoder via data distillation. Further, we enhance the step
distillation by exploring training strategies and introducing regularization
from classifier-free guidance. Our extensive experiments on MS-COCO show that
our model with $8$ denoising steps achieves better FID and CLIP scores than
Stable Diffusion v$1.5$ with $50$ steps. Our work democratizes content creation
by bringing powerful text-to-image diffusion models to the hands of users. | https://huggingface.co/papers/2306.00980 |
2023-06-02 | 2306.00637 | 12 | Wuerstchen: Efficient Pretraining of Text-to-Image Models | We introduce W\"urstchen, a novel architecture for text-to-image synthesis
that combines competitive performance with unprecedented cost-effectiveness for
large-scale text-to-image diffusion models. A key contribution of our work is
to develop a latent diffusion technique in which we learn a detailed but
extremely compact semantic image representation used to guide the diffusion
process. This highly compressed representation of an image provides much more
detailed guidance compared to latent representations of language and this
significantly reduces the computational requirements to achieve
state-of-the-art results. Our approach also improves the quality of
text-conditioned image generation based on our user preference study. The
training requirements of our approach consists of 24,602 A100-GPU hours -
compared to Stable Diffusion 2.1's 200,000 GPU hours. Our approach also
requires less training data to achieve these results. Furthermore, our compact
latent representations allows us to perform inference over twice as fast,
slashing the usual costs and carbon footprint of a state-of-the-art (SOTA)
diffusion model significantly, without compromising the end performance. In a
broader comparison against SOTA models our approach is substantially more
efficient and compares favorably in terms of image quality. We believe that
this work motivates more emphasis on the prioritization of both performance and
computational accessibility. | https://huggingface.co/papers/2306.00637 |
2023-06-02 | 2306.00378 | 7 | Example-based Motion Synthesis via Generative Motion Matching | We present GenMM, a generative model that "mines" as many diverse motions as
possible from a single or few example sequences. In stark contrast to existing
data-driven methods, which typically require long offline training time, are
prone to visual artifacts, and tend to fail on large and complex skeletons,
GenMM inherits the training-free nature and the superior quality of the
well-known Motion Matching method. GenMM can synthesize a high-quality motion
within a fraction of a second, even with highly complex and large skeletal
structures. At the heart of our generative framework lies the generative motion
matching module, which utilizes the bidirectional visual similarity as a
generative cost function to motion matching, and operates in a multi-stage
framework to progressively refine a random guess using exemplar motion matches.
In addition to diverse motion generation, we show the versatility of our
generative framework by extending it to a number of scenarios that are not
possible with motion matching alone, including motion completion, key
frame-guided generation, infinite looping, and motion reassembly. Code and data
for this paper are at https://wyysf-98.github.io/GenMM/ | https://huggingface.co/papers/2306.00378 |
2023-06-02 | 2306.00238 | 6 | Bytes Are All You Need: Transformers Operating Directly On File Bytes | Modern deep learning approaches usually transform inputs into a
modality-specific form. For example, the most common deep learning approach to
image classification involves decoding image file bytes into an RGB tensor
which is passed into a neural network. Instead, we investigate performing
classification directly on file bytes, without the need for decoding files at
inference time. Using file bytes as model inputs enables the development of
models which can operate on multiple input modalities. Our model,
ByteFormer, achieves an ImageNet Top-1 classification accuracy of
77.33% when training and testing directly on TIFF file bytes using a
transformer backbone with configuration similar to DeiT-Ti (72.2% accuracy
when operating on RGB images). Without modifications or hyperparameter tuning,
ByteFormer achieves 95.42% classification accuracy when operating on WAV
files from the Speech Commands v2 dataset (compared to state-of-the-art
accuracy of 98.7%). Additionally, we demonstrate that ByteFormer has
applications in privacy-preserving inference. ByteFormer is capable of
performing inference on particular obfuscated input representations with no
loss of accuracy. We also demonstrate ByteFormer's ability to perform inference
with a hypothetical privacy-preserving camera which avoids forming full images
by consistently masking 90% of pixel channels, while still achieving
71.35% accuracy on ImageNet. Our code will be made available at
https://github.com/apple/ml-cvnets/tree/main/examples/byteformer. | https://huggingface.co/papers/2306.00238 |
2023-06-02 | 2306.00966 | 5 | The Hidden Language of Diffusion Models | Text-to-image diffusion models have demonstrated an unparalleled ability to
generate high-quality, diverse images from a textual concept (e.g., "a doctor",
"love"). However, the internal process of mapping text to a rich visual
representation remains an enigma. In this work, we tackle the challenge of
understanding concept representations in text-to-image models by decomposing an
input text prompt into a small set of interpretable elements. This is achieved
by learning a pseudo-token that is a sparse weighted combination of tokens from
the model's vocabulary, with the objective of reconstructing the images
generated for the given concept. Applied over the state-of-the-art Stable
Diffusion model, this decomposition reveals non-trivial and surprising
structures in the representations of concepts. For example, we find that some
concepts such as "a president" or "a composer" are dominated by specific
instances (e.g., "Obama", "Biden") and their interpolations. Other concepts,
such as "happiness" combine associated terms that can be concrete ("family",
"laughter") or abstract ("friendship", "emotion"). In addition to peering into
the inner workings of Stable Diffusion, our method also enables applications
such as single-image decomposition to tokens, bias detection and mitigation,
and semantic image manipulation. Our code will be available at:
https://hila-chefer.github.io/Conceptor/ | https://huggingface.co/papers/2306.00966 |
2023-06-02 | 2306.00943 | 5 | Make-Your-Video: Customized Video Generation Using Textual and
Structural Guidance | Creating a vivid video from the event or scenario in our imagination is a
truly fascinating experience. Recent advancements in text-to-video synthesis
have unveiled the potential to achieve this with prompts only. While text is
convenient in conveying the overall scene context, it may be insufficient to
control precisely. In this paper, we explore customized video generation by
utilizing text as context description and motion structure (e.g. frame-wise
depth) as concrete guidance. Our method, dubbed Make-Your-Video, involves
joint-conditional video generation using a Latent Diffusion Model that is
pre-trained for still image synthesis and then promoted for video generation
with the introduction of temporal modules. This two-stage learning scheme not
only reduces the computing resources required, but also improves the
performance by transferring the rich concepts available in image datasets
solely into video generation. Moreover, we use a simple yet effective causal
attention mask strategy to enable longer video synthesis, which mitigates the
potential quality degradation effectively. Experimental results show the
superiority of our method over existing baselines, particularly in terms of
temporal coherence and fidelity to users' guidance. In addition, our model
enables several intriguing applications that demonstrate potential for
practical usage. | https://huggingface.co/papers/2306.00943 |
2023-06-02 | 2306.00984 | 4 | StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual
Representation Learners | We investigate the potential of learning visual representations using
synthetic images generated by text-to-image models. This is a natural question
in the light of the excellent performance of such models in generating
high-quality images. We consider specifically the Stable Diffusion, one of the
leading open source text-to-image models. We show that (1) when the generative
model is configured with proper classifier-free guidance scale, training
self-supervised methods on synthetic images can match or beat the real image
counterpart; (2) by treating the multiple images generated from the same text
prompt as positives for each other, we develop a multi-positive contrastive
learning method, which we call StableRep. With solely synthetic images, the
representations learned by StableRep surpass the performance of representations
learned by SimCLR and CLIP using the same set of text prompts and corresponding
real images, on large scale datasets. When we further add language supervision,
StableRep trained with 20M synthetic images achieves better accuracy than CLIP
trained with 50M real images. | https://huggingface.co/papers/2306.00984 |
2023-06-02 | 2306.00971 | 4 | ViCo: Detail-Preserving Visual Condition for Personalized Text-to-Image
Generation | Personalized text-to-image generation using diffusion models has recently
been proposed and attracted lots of attention. Given a handful of images
containing a novel concept (e.g., a unique toy), we aim to tune the generative
model to capture fine visual details of the novel concept and generate
photorealistic images following a text condition. We present a plug-in method,
named ViCo, for fast and lightweight personalized generation. Specifically, we
propose an image attention module to condition the diffusion process on the
patch-wise visual semantics. We introduce an attention-based object mask that
comes almost at no cost from the attention module. In addition, we design a
simple regularization based on the intrinsic properties of text-image attention
maps to alleviate the common overfitting degradation. Unlike many existing
models, our method does not finetune any parameters of the original diffusion
model. This allows more flexible and transferable model deployment. With only
light parameter training (~6% of the diffusion U-Net), our method achieves
comparable or even better performance than all state-of-the-art models both
qualitatively and quantitatively. | https://huggingface.co/papers/2306.00971 |
2023-06-02 | 2306.00107 | 4 | MERT: Acoustic Music Understanding Model with Large-Scale
Self-supervised Training | Self-supervised learning (SSL) has recently emerged as a promising paradigm
for training generalisable models on large-scale data in the fields of vision,
text, and speech. Although SSL has been proven effective in speech and audio,
its application to music audio has yet to be thoroughly explored. This is
primarily due to the distinctive challenges associated with modelling musical
knowledge, particularly its tonal and pitched characteristics of music. To
address this research gap, we propose an acoustic Music undERstanding model
with large-scale self-supervised Training (MERT), which incorporates teacher
models to provide pseudo labels in the masked language modelling (MLM) style
acoustic pre-training. In our exploration, we identified a superior combination
of teacher models, which outperforms conventional speech and audio approaches
in terms of performance. This combination includes an acoustic teacher based on
Residual Vector Quantization - Variational AutoEncoder (RVQ-VAE) and a musical
teacher based on the Constant-Q Transform (CQT). These teachers effectively
guide our student model, a BERT-style transformer encoder, to better model
music audio. In addition, we introduce an in-batch noise mixture augmentation
to enhance the representation robustness. Furthermore, we explore a wide range
of settings to overcome the instability in acoustic language model
pre-training, which allows our designed paradigm to scale from 95M to 330M
parameters. Experimental results indicate that our model can generalise and
perform well on 14 music understanding tasks and attains state-of-the-art
(SOTA) overall scores. The code and models are online:
https://github.com/yizhilll/MERT. | https://huggingface.co/papers/2306.00107 |
2023-06-02 | 2306.00926 | 3 | Inserting Anybody in Diffusion Models via Celeb Basis | Exquisite demand exists for customizing the pretrained large text-to-image
model, e.g., Stable Diffusion, to generate innovative concepts, such
as the users themselves. However, the newly-added concept from previous
customization methods often shows weaker combination abilities than the
original ones even given several images during training. We thus propose a new
personalization method that allows for the seamless integration of a unique
individual into the pre-trained diffusion model using just one facial
photograph and only 1024 learnable parameters under 3
minutes. So as we can effortlessly generate stunning images of this person in
any pose or position, interacting with anyone and doing anything imaginable
from text prompts. To achieve this, we first analyze and build a well-defined
celeb basis from the embedding space of the pre-trained large text encoder.
Then, given one facial photo as the target identity, we generate its own
embedding by optimizing the weight of this basis and locking all other
parameters. Empowered by the proposed celeb basis, the new identity in our
customized model showcases a better concept combination ability than previous
personalization methods. Besides, our model can also learn several new
identities at once and interact with each other where the previous
customization model fails to. The code will be released. | https://huggingface.co/papers/2306.00926 |
2023-06-02 | 2306.00986 | 2 | Diffusion Self-Guidance for Controllable Image Generation | Large-scale generative models are capable of producing high-quality images
from detailed text descriptions. However, many aspects of an image are
difficult or impossible to convey through text. We introduce self-guidance, a
method that provides greater control over generated images by guiding the
internal representations of diffusion models. We demonstrate that properties
such as the shape, location, and appearance of objects can be extracted from
these representations and used to steer sampling. Self-guidance works similarly
to classifier guidance, but uses signals present in the pretrained model
itself, requiring no additional models or training. We show how a simple set of
properties can be composed to perform challenging image manipulations, such as
modifying the position or size of objects, merging the appearance of objects in
one image with the layout of another, composing objects from many images into
one, and more. We also show that self-guidance can be used to edit real images.
For results and an interactive demo, see our project page at
https://dave.ml/selfguidance/ | https://huggingface.co/papers/2306.00986 |
2023-06-02 | 2306.00802 | 2 | Birth of a Transformer: A Memory Viewpoint | Large language models based on transformers have achieved great empirical
successes. However, as they are deployed more widely, there is a growing need
to better understand their internal mechanisms in order to make them more
reliable. These models appear to store vast amounts of knowledge from their
training data, and to adapt quickly to new information provided in their
context or prompt. We study how transformers balance these two types of
knowledge by considering a synthetic setup where tokens are generated from
either global or context-specific bigram distributions. By a careful empirical
analysis of the training process on a simplified two-layer transformer, we
illustrate the fast learning of global bigrams and the slower development of an
"induction head" mechanism for the in-context bigrams. We highlight the role of
weight matrices as associative memories, provide theoretical insights on how
gradients enable their learning during training, and study the role of
data-distributional properties. | https://huggingface.co/papers/2306.00802 |
2023-06-02 | 2306.00110 | 2 | MuseCoco: Generating Symbolic Music from Text | Generating music from text descriptions is a user-friendly mode since the
text is a relatively easy interface for user engagement. While some approaches
utilize texts to control music audio generation, editing musical elements in
generated audio is challenging for users. In contrast, symbolic music offers
ease of editing, making it more accessible for users to manipulate specific
musical elements. In this paper, we propose MuseCoco, which generates symbolic
music from text descriptions with musical attributes as the bridge to break
down the task into text-to-attribute understanding and attribute-to-music
generation stages. MuseCoCo stands for Music Composition Copilot that empowers
musicians to generate music directly from given text descriptions, offering a
significant improvement in efficiency compared to creating music entirely from
scratch. The system has two main advantages: Firstly, it is data efficient. In
the attribute-to-music generation stage, the attributes can be directly
extracted from music sequences, making the model training self-supervised. In
the text-to-attribute understanding stage, the text is synthesized and refined
by ChatGPT based on the defined attribute templates. Secondly, the system can
achieve precise control with specific attributes in text descriptions and
offers multiple control options through attribute-conditioned or
text-conditioned approaches. MuseCoco outperforms baseline systems in terms of
musicality, controllability, and overall score by at least 1.27, 1.08, and 1.32
respectively. Besides, there is a notable enhancement of about 20% in objective
control accuracy. In addition, we have developed a robust large-scale model
with 1.2 billion parameters, showcasing exceptional controllability and
musicality. | https://huggingface.co/papers/2306.00110 |
2023-06-02 | 2306.00029 | 2 | CodeTF: One-stop Transformer Library for State-of-the-art Code LLM | Code intelligence plays a key role in transforming modern software
engineering. Recently, deep learning-based models, especially Transformer-based
large language models (LLMs), have demonstrated remarkable potential in
tackling these tasks by leveraging massive open-source code data and
programming language features. However, the development and deployment of such
models often require expertise in both machine learning and software
engineering, creating a barrier for the model adoption. In this paper, we
present CodeTF, an open-source Transformer-based library for state-of-the-art
Code LLMs and code intelligence. Following the principles of modular design and
extensible framework, we design CodeTF with a unified interface to enable rapid
access and development across different types of models, datasets and tasks.
Our library supports a collection of pretrained Code LLM models and popular
code benchmarks, including a standardized interface to train and serve code
LLMs efficiently, and data features such as language-specific parsers and
utility functions for extracting code attributes. In this paper, we describe
the design principles, the architecture, key modules and components, and
compare with other related library tools. Finally, we hope CodeTF is able to
bridge the gap between machine learning/generative AI and software engineering,
providing a comprehensive open-source solution for developers, researchers, and
practitioners. | https://huggingface.co/papers/2306.00029 |
2023-06-02 | 2306.00964 | 1 | Cocktail: Mixing Multi-Modality Controls for Text-Conditional Image
Generation | Text-conditional diffusion models are able to generate high-fidelity images
with diverse contents. However, linguistic representations frequently exhibit
ambiguous descriptions of the envisioned objective imagery, requiring the
incorporation of additional control signals to bolster the efficacy of
text-guided diffusion models. In this work, we propose Cocktail, a pipeline to
mix various modalities into one embedding, amalgamated with a generalized
ControlNet (gControlNet), a controllable normalisation (ControlNorm), and a
spatial guidance sampling method, to actualize multi-modal and
spatially-refined control for text-conditional diffusion models. Specifically,
we introduce a hyper-network gControlNet, dedicated to the alignment and
infusion of the control signals from disparate modalities into the pre-trained
diffusion model. gControlNet is capable of accepting flexible modality signals,
encompassing the simultaneous reception of any combination of modality signals,
or the supplementary fusion of multiple modality signals. The control signals
are then fused and injected into the backbone model according to our proposed
ControlNorm. Furthermore, our advanced spatial guidance sampling methodology
proficiently incorporates the control signal into the designated region,
thereby circumventing the manifestation of undesired objects within the
generated image. We demonstrate the results of our method in controlling
various modalities, proving high-quality synthesis and fidelity to multiple
external signals. | https://huggingface.co/papers/2306.00964 |
2023-06-02 | 2306.00956 | 1 | The ObjectFolder Benchmark: Multisensory Learning with Neural and Real
Objects | We introduce the ObjectFolder Benchmark, a benchmark suite of 10 tasks for
multisensory object-centric learning, centered around object recognition,
reconstruction, and manipulation with sight, sound, and touch. We also
introduce the ObjectFolder Real dataset, including the multisensory
measurements for 100 real-world household objects, building upon a newly
designed pipeline for collecting the 3D meshes, videos, impact sounds, and
tactile readings of real-world objects. We conduct systematic benchmarking on
both the 1,000 multisensory neural objects from ObjectFolder, and the real
multisensory data from ObjectFolder Real. Our results demonstrate the
importance of multisensory perception and reveal the respective roles of
vision, audio, and touch for different object-centric learning tasks. By
publicly releasing our dataset and benchmark suite, we hope to catalyze and
enable new research in multisensory object-centric learning in computer vision,
robotics, and beyond. Project page: https://objectfolder.stanford.edu | https://huggingface.co/papers/2306.00956 |
2023-06-02 | 2306.00148 | 1 | SafeDiffuser: Safe Planning with Diffusion Probabilistic Models | Diffusion model-based approaches have shown promise in data-driven planning,
but there are no safety guarantees, thus making it hard to be applied for
safety-critical applications. To address these challenges, we propose a new
method, called SafeDiffuser, to ensure diffusion probabilistic models satisfy
specifications by using a class of control barrier functions. The key idea of
our approach is to embed the proposed finite-time diffusion invariance into the
denoising diffusion procedure, which enables trustworthy diffusion data
generation. Moreover, we demonstrate that our finite-time diffusion invariance
method through generative models not only maintains generalization performance
but also creates robustness in safe data generation. We test our method on a
series of safe planning tasks, including maze path generation, legged robot
locomotion, and 3D space manipulation, with results showing the advantages of
robustness and guarantees over vanilla diffusion models. | https://huggingface.co/papers/2306.00148 |
2023-06-02 | 2306.00008 | 1 | Brainformers: Trading Simplicity for Efficiency | Transformers are central to recent successes in natural language processing
and computer vision. Transformers have a mostly uniform backbone where layers
alternate between feed-forward and self-attention in order to build a deep
network. Here we investigate this design choice and find that more complex
blocks that have different permutations of layer primitives can be more
efficient. Using this insight, we develop a complex block, named Brainformer,
that consists of a diverse sets of layers such as sparsely gated feed-forward
layers, dense feed-forward layers, attention layers, and various forms of layer
normalization and activation functions. Brainformer consistently outperforms
the state-of-the-art dense and sparse Transformers, in terms of both quality
and efficiency. A Brainformer model with 8 billion activated parameters per
token demonstrates 2x faster training convergence and 5x faster step time
compared to its GLaM counterpart. In downstream task evaluation, Brainformer
also demonstrates a 3% higher SuperGLUE score with fine-tuning compared to GLaM
with a similar number of activated parameters. Finally, Brainformer largely
outperforms a Primer dense model derived with NAS with similar computation per
token on fewshot evaluations. | https://huggingface.co/papers/2306.00008 |
2023-06-05 | 2306.02707 | 47 | Orca: Progressive Learning from Complex Explanation Traces of GPT-4 | Recent research has focused on enhancing the capability of smaller models
through imitation learning, drawing on the outputs generated by large
foundation models (LFMs). A number of issues impact the quality of these
models, ranging from limited imitation signals from shallow LFM outputs; small
scale homogeneous training data; and most notably a lack of rigorous evaluation
resulting in overestimating the small model's capability as they tend to learn
to imitate the style, but not the reasoning process of LFMs. To address these
challenges, we develop Orca (We are working with our legal team to publicly
release a diff of the model weights in accordance with LLaMA's release policy
to be published at https://aka.ms/orca-lm), a 13-billion parameter model that
learns to imitate the reasoning process of LFMs. Orca learns from rich signals
from GPT-4 including explanation traces; step-by-step thought processes; and
other complex instructions, guided by teacher assistance from ChatGPT. To
promote this progressive learning, we tap into large-scale and diverse
imitation data with judicious sampling and selection. Orca surpasses
conventional state-of-the-art instruction-tuned models such as Vicuna-13B by
more than 100% in complex zero-shot reasoning benchmarks like Big-Bench Hard
(BBH) and 42% on AGIEval. Moreover, Orca reaches parity with ChatGPT on the BBH
benchmark and shows competitive performance (4 pts gap with optimized system
message) in professional and academic examinations like the SAT, LSAT, GRE, and
GMAT, both in zero-shot settings without CoT; while trailing behind GPT-4. Our
research indicates that learning from step-by-step explanations, whether these
are generated by humans or more advanced AI models, is a promising direction to
improve model capabilities and skills. | https://huggingface.co/papers/2306.02707 |
2023-06-05 | 2306.01116 | 38 | The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora
with Web Data, and Web Data Only | Large language models are commonly trained on a mixture of filtered web data
and curated high-quality corpora, such as social media conversations, books, or
technical papers. This curation process is believed to be necessary to produce
performant models with broad zero-shot generalization abilities. However, as
larger models requiring pretraining on trillions of tokens are considered, it
is unclear how scalable is curation and whether we will run out of unique
high-quality data soon. At variance with previous beliefs, we show that
properly filtered and deduplicated web data alone can lead to powerful models;
even significantly outperforming models from the state-of-the-art trained on
The Pile. Despite extensive filtering, the high-quality data we extract from
the web is still plentiful, and we are able to obtain five trillion tokens from
CommonCrawl. We publicly release an extract of 600 billion tokens from our
RefinedWeb dataset, and 1.3/7.5B parameters language models trained on it. | https://huggingface.co/papers/2306.01116 |
2023-06-05 | 2306.01567 | 8 | Segment Anything in High Quality | The recent Segment Anything Model (SAM) represents a big leap in scaling up
segmentation models, allowing for powerful zero-shot capabilities and flexible
prompting. Despite being trained with 1.1 billion masks, SAM's mask prediction
quality falls short in many cases, particularly when dealing with objects that
have intricate structures. We propose HQ-SAM, equipping SAM with the ability to
accurately segment any object, while maintaining SAM's original promptable
design, efficiency, and zero-shot generalizability. Our careful design reuses
and preserves the pre-trained model weights of SAM, while only introducing
minimal additional parameters and computation. We design a learnable
High-Quality Output Token, which is injected into SAM's mask decoder and is
responsible for predicting the high-quality mask. Instead of only applying it
on mask-decoder features, we first fuse them with early and final ViT features
for improved mask details. To train our introduced learnable parameters, we
compose a dataset of 44K fine-grained masks from several sources. HQ-SAM is
only trained on the introduced detaset of 44k masks, which takes only 4 hours
on 8 GPUs. We show the efficacy of HQ-SAM in a suite of 9 diverse segmentation
datasets across different downstream tasks, where 7 out of them are evaluated
in a zero-shot transfer protocol. Our code and models will be released at
https://github.com/SysCV/SAM-HQ. | https://huggingface.co/papers/2306.01567 |
2023-06-05 | 2306.02561 | 6 | LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and
Generative Fusion | We present LLM-Blender, an ensembling framework designed to attain
consistently superior performance by leveraging the diverse strengths of
multiple open-source large language models (LLMs). Our framework consists of
two modules: PairRanker and GenFuser, addressing the observation that optimal
LLMs for different examples can significantly vary. PairRanker employs a
specialized pairwise comparison method to distinguish subtle differences
between candidate outputs. It jointly encodes the input text and a pair of
candidates, using cross-attention encoders to determine the superior one. Our
results demonstrate that PairRanker exhibits the highest correlation with
ChatGPT-based ranking. Then, GenFuser aims to merge the top-ranked candidates,
generating an improved output by capitalizing on their strengths and mitigating
their weaknesses. To facilitate large-scale evaluation, we introduce a
benchmark dataset, MixInstruct, which is a mixture of multiple instruction
datasets featuring oracle pairwise comparisons. Our LLM-Blender significantly
outperform individual LLMs and baseline methods across various metrics,
establishing a substantial performance gap. | https://huggingface.co/papers/2306.02561 |
2023-06-05 | 2306.03082 | 5 | InstructZero: Efficient Instruction Optimization for Black-Box Large
Language Models | Large language models~(LLMs) are instruction followers, but it can be
challenging to find the best instruction for different situations, especially
for black-box LLMs on which backpropagation is forbidden. Instead of directly
optimizing the discrete instruction, we optimize a low-dimensional soft prompt
applied to an open-source LLM to generate the instruction for the black-box
LLM. On each iteration of the proposed method, which we call InstructZero, a
soft prompt is converted into an instruction using the open-source LLM, which
is then submitted to the black-box LLM for zero-shot evaluation, and the
performance is sent to Bayesian optimization to produce new soft prompts
improving the zero-shot performance. We evaluate InstructZero on different
combinations of open-source LLMs and APIs including Vicuna and ChatGPT. Our
results show that InstructZero outperforms SOTA auto-instruction methods across
a variety of downstream tasks. Our code and data are publicly available at
https://github.com/Lichang-Chen/InstructZero. | https://huggingface.co/papers/2306.03082 |
2023-06-05 | 2306.01693 | 3 | Fine-Grained Human Feedback Gives Better Rewards for Language Model
Training | Language models (LMs) often exhibit undesirable text generation behaviors,
including generating false, toxic, or irrelevant outputs. Reinforcement
learning from human feedback (RLHF) - where human preference judgments on LM
outputs are transformed into a learning signal - has recently shown promise in
addressing these issues. However, such holistic feedback conveys limited
information on long text outputs; it does not indicate which aspects of the
outputs influenced user preference; e.g., which parts contain what type(s) of
errors. In this paper, we use fine-grained human feedback (e.g., which sentence
is false, which sub-sentence is irrelevant) as an explicit training signal. We
introduce Fine-Grained RLHF, a framework that enables training and learning
from reward functions that are fine-grained in two respects: (1) density,
providing a reward after every segment (e.g., a sentence) is generated; and (2)
incorporating multiple reward models associated with different feedback types
(e.g., factual incorrectness, irrelevance, and information incompleteness). We
conduct experiments on detoxification and long-form question answering to
illustrate how learning with such reward functions leads to improved
performance, supported by both automatic and human evaluation. Additionally, we
show that LM behaviors can be customized using different combinations of
fine-grained reward models. We release all data, collected human feedback, and
codes at https://FineGrainedRLHF.github.io. | https://huggingface.co/papers/2306.01693 |
2023-06-05 | 2306.01684 | 3 | Harnessing large-language models to generate private synthetic text | Differentially private (DP) training methods like DP-SGD can protect
sensitive training data by ensuring that ML models will not reveal private
information. An alternative approach, which this paper studies, is to use a
sensitive dataset to generate a new synthetic dataset which is differentially
private with respect to the original data. Doing so has several advantages:
synthetic data can be reused for other tasks (including for hyper parameter
tuning), retained indefinitely, or shared with third parties without
sacrificing privacy.
However, obtaining DP data is much harder than introducing DP during
training. To make it feasible for text, recent work has utilized public data by
starting with a pre-trained generative language model and privately finetuning
it on sensitive data. This model can be used to sample a DP synthetic dataset.
While this strategy seems straightforward, executing it has proven problematic.
Previous approaches either show significant performance loss, or have, as we
show, critical design flaws.
In this paper we demonstrate that a proper training objective along with
tuning fewer parameters results in excellent DP synthetic data quality. Our
approach is competitive with direct DP-training of downstream classifiers in
terms of performance on downstream tasks. We also demonstrate that our DP
synthetic data is not only useful for downstream classifier training, but also
to tune those same models. | https://huggingface.co/papers/2306.01684 |
2023-06-05 | 2306.03024 | 2 | PokemonChat: Auditing ChatGPT for Pokémon Universe Knowledge | The recently released ChatGPT model demonstrates unprecedented capabilities
in zero-shot question-answering. In this work, we probe ChatGPT for its
conversational understanding and introduce a conversational framework
(protocol) that can be adopted in future studies. The Pok\'emon universe serves
as an ideal testing ground for auditing ChatGPT's reasoning capabilities due to
its closed world assumption. After bringing ChatGPT's background knowledge (on
the Pok\'emon universe) to light, we test its reasoning process when using
these concepts in battle scenarios. We then evaluate its ability to acquire new
knowledge and include it in its reasoning process. Our ultimate goal is to
assess ChatGPT's ability to generalize, combine features, and to acquire and
reason over newly introduced knowledge from human feedback. We find that
ChatGPT has prior knowledge of the Pokemon universe, which can reason upon in
battle scenarios to a great extent, even when new information is introduced.
The model performs better with collaborative feedback and if there is an
initial phase of information retrieval, but also hallucinates occasionally and
is susceptible to adversarial attacks. | https://huggingface.co/papers/2306.03024 |
2023-06-05 | 2306.01694 | 2 | Evaluating Language Models for Mathematics through Interactions | The standard methodology of evaluating large language models (LLMs) based on
static pairs of inputs and outputs is insufficient for developing assistants:
this kind of assessments fails to take into account the essential interactive
element in their deployment, and therefore limits how we understand language
model capabilities. We introduce CheckMate, an adaptable prototype platform for
humans to interact with and evaluate LLMs. We conduct a study with CheckMate to
evaluate three language models~(InstructGPT, ChatGPT, and GPT-4) as assistants
in proving undergraduate-level mathematics, with a mixed cohort of participants
from undergraduate students to professors of mathematics. We release the
resulting interaction and rating dataset, MathConverse. By analysing
MathConverse, we derive a preliminary taxonomy of human behaviours and uncover
that despite a generally positive correlation, there are notable instances of
divergence between correctness and perceived helpfulness in LLM generations,
amongst other findings. Further, we identify useful scenarios and existing
issues of GPT-4 in mathematical reasoning through a series of case studies
contributed by expert mathematicians. We conclude with actionable takeaways for
ML practitioners and mathematicians: models which communicate uncertainty,
respond well to user corrections, are more interpretable and concise may
constitute better assistants; interactive evaluation is a promising way to
continually navigate the capability of these models; humans should be aware of
language models' algebraic fallibility, and for that reason discern where they
should be used. | https://huggingface.co/papers/2306.01694 |
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