Orionnnn/hub-stats huggingface dataset:
Orca: Progressive Learning from Complex Explanation Traces of GPT-4
Authors: name: Subhabrata Mukherjee, name: Arindam Mitra, name: Ganesh Jawahar, name: Sahaj Agarwal, name: Hamid Palangi, name: Ahmed Awadallah
Published: 2023-06-05 08:58:39

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.
DaTaSeg: Taming a Universal Multi-Dataset Multi-Task Segmentation Model
Authors: name: Xiuye Gu, name: Yin Cui, name: Jonathan Huang, name: Abdullah Rashwan, name: Xuan Yang, name: Xingyi Zhou, name: Golnaz Ghiasi, name: Weicheng Kuo, name: Huizhong Chen, name: Liang-Chieh Chen, name: David A Ross
Published: 2023-06-02 17:59:24

Observing the close relationship among panoptic, semantic and instance
segmentation tasks, we propose to train a universal multi-dataset multi-task
segmentation model: DaTaSeg.We use a shared representation (mask proposals with
class predictions) for all tasks. To tackle task discrepancy, we adopt
different merge operations and post-processing for different tasks. We also
leverage weak-supervision, allowing our segmentation model to benefit from
cheaper bounding box annotations. To share knowledge across datasets, we use
text embeddings from the same semantic embedding space as classifiers and share
all network parameters among datasets. We train DaTaSeg on ADE semantic, COCO
panoptic, and Objects365 detection datasets. DaTaSeg improves performance on
all datasets, especially small-scale datasets, achieving 54.0 mIoU on ADE
semantic and 53.5 PQ on COCO panoptic. DaTaSeg also enables weakly-supervised
knowledge transfer on ADE panoptic and Objects365 instance segmentation.
Experiments show DaTaSeg scales with the number of training datasets and
enables open-vocabulary segmentation through direct transfer. In addition, we
annotate an Objects365 instance segmentation set of 1,000 images and will
release it as a public benchmark.
Segment Anything in High Quality
Authors: name: Lei Ke, name: Mingqiao Ye, name: Martin Danelljan, name: Yifan Liu, name: Yu-Wing Tai, name: Chi-Keung Tang, name: Fisher Yu
Published: 2023-06-02 14:23:59

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.
Fine-Grained Human Feedback Gives Better Rewards for Language Model
  Training
Authors: name: Zeqiu Wu, name: Yushi Hu, name: Weijia Shi, name: Nouha Dziri, name: Alane Suhr, name: Prithviraj Ammanabrolu, name: Noah A. Smith, name: Mari Ostendorf, name: Hannaneh Hajishirzi
Published: 2023-06-02 17:11:37

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.
Harnessing large-language models to generate private synthetic text
Authors: name: Alexey Kurakin, name: Natalia Ponomareva, name: Umar Syed, name: Liam MacDermed, name: Andreas Terzis
Published: 2023-06-02 16:59:36

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.
Responsible Task Automation: Empowering Large Language Models as
  Responsible Task Automators
Authors: name: Zhizheng Zhang, name: Xiaoyi Zhang, name: Wenxuan Xie, name: Yan Lu
Published: 2023-06-02 02:42:58

The recent success of Large Language Models (LLMs) signifies an impressive
stride towards artificial general intelligence. They have shown a promising
prospect in automatically completing tasks upon user instructions, functioning
as brain-like coordinators. The associated risks will be revealed as we
delegate an increasing number of tasks to machines for automated completion. A
big question emerges: how can we make machines behave responsibly when helping
humans automate tasks as personal copilots? In this paper, we explore this
question in depth from the perspectives of feasibility, completeness and
security. In specific, we present Responsible Task Automation (ResponsibleTA)
as a fundamental framework to facilitate responsible collaboration between
LLM-based coordinators and executors for task automation with three empowered
capabilities: 1) predicting the feasibility of the commands for executors; 2)
verifying the completeness of executors; 3) enhancing the security (e.g., the
protection of users' privacy). We further propose and compare two paradigms for
implementing the first two capabilities. One is to leverage the generic
knowledge of LLMs themselves via prompt engineering while the other is to adopt
domain-specific learnable models. Moreover, we introduce a local memory
mechanism for achieving the third capability. We evaluate our proposed
ResponsibleTA on UI task automation and hope it could bring more attentions to
ensuring LLMs more responsible in diverse scenarios. The research project
homepage is at
https://task-automation-research.github.io/responsible_task_automation.
Faster Causal Attention Over Large Sequences Through Sparse Flash
  Attention
Authors: name: Matteo Pagliardini, name: Daniele Paliotta, name: Martin Jaggi, name: François Fleuret
Published: 2023-06-01 21:33:59

Transformer-based language models have found many diverse applications
requiring them to process sequences of increasing length. For these
applications, the causal self-attention -- which is the only component scaling
quadratically w.r.t. the sequence length -- becomes a central concern. While
many works have proposed schemes to sparsify the attention patterns and reduce
the computational overhead of self-attention, those are often limited by
implementations concerns and end up imposing a simple and static structure over
the attention matrix. Conversely, implementing more dynamic sparse attentions
often results in runtimes significantly slower than computing the full
attention using the Flash implementation from Dao et al. (2022). We extend
FlashAttention to accommodate a large class of attention sparsity patterns
that, in particular, encompass key/query dropping and hashing-based attention.
This leads to implementations with no computational complexity overhead and a
multi-fold runtime speedup on top of FlashAttention. Even with relatively low
degrees of sparsity, our method improves visibly upon FlashAttention as the
sequence length increases. Without sacrificing perplexity, we increase the
training speed of a transformer language model by 2.0times and 3.3times
for sequences of respectively 8k and 16k tokens.
The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora
  with Web Data, and Web Data Only
Authors: name: Guilherme Penedo, name: Quentin Malartic, name: Daniel Hesslow, name: Ruxandra Cojocaru, name: Alessandro Cappelli, name: Hamza Alobeidli, name: Baptiste Pannier, name: Ebtesam Almazrouei, name: Julien Launay
Published: 2023-06-01 20:03:56

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.
Reimagining Retrieval Augmented Language Models for Answering Queries
Authors: name: Wang-Chiew Tan, name: Yuliang Li, name: Pedro Rodriguez, name: Richard James, name: Xi Victoria Lin, name: Alon Halevy, name: Scott Yih
Published: 2023-06-01 18:08:51

We present a reality check on large language models and inspect the promise
of retrieval augmented language models in comparison. Such language models are
semi-parametric, where models integrate model parameters and knowledge from
external data sources to make their predictions, as opposed to the parametric
nature of vanilla large language models. We give initial experimental findings
that semi-parametric architectures can be enhanced with views, a query
analyzer/planner, and provenance to make a significantly more powerful system
for question answering in terms of accuracy and efficiency, and potentially for
other NLP tasks
Evaluating Language Models for Mathematics through Interactions
Authors: name: Katherine M. Collins, name: Albert Q. Jiang, name: Simon Frieder, name: Lionel Wong, name: Miri Zilka, name: Umang Bhatt, name: Thomas Lukasiewicz, name: Yuhuai Wu, name: Joshua B. Tenenbaum, name: William Hart, name: Timothy Gowers, name: Wenda Li, name: Adrian Weller, name: Mateja Jamnik
Published: 2023-06-02 17:12:25

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.
An Empirical Study on Challenging Math Problem Solving with GPT-4
Authors: name: Yiran Wu, name: Feiran Jia, name: Shaokun Zhang, name: Qingyun Wu, name: Hangyu Li, name: Erkang Zhu, name: Yue Wang, name: Yin Tat Lee, name: Richard Peng, name: Chi Wang
Published: 2023-06-02 08:02:15

Employing Large Language Models (LLMs) to address mathematical problems is an
intriguing research endeavor, considering the abundance of math problems
expressed in natural language across numerous science and engineering fields.
While several prior works have investigated solving elementary mathematics
using LLMs, this work explores the frontier of using GPT-4 for solving more
complex and challenging math problems. We evaluate various ways of using GPT-4.
Some of them are adapted from existing work, and one is \MathChat, a
conversational problem-solving framework newly proposed in this work. We
perform the evaluation on difficult high school competition problems from the
MATH dataset, which shows the advantage of the proposed conversational
approach.
The ObjectFolder Benchmark: Multisensory Learning with Neural and Real
  Objects
Authors: name: Ruohan Gao, name: Yiming Dou, name: Hao Li, name: Tanmay Agarwal, name: Jeannette Bohg, name: Yunzhu Li, name: Li Fei-Fei, name: Jiajun Wu
Published: 2023-06-01 17:51:22

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
Example-based Motion Synthesis via Generative Motion Matching
Authors: name: Weiyu Li, name: Xuelin Chen, name: Peizhuo Li, name: Olga Sorkine-Hornung, name: Baoquan Chen
Published: 2023-06-01 06:19:33

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/
StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual
  Representation Learners
Authors: name: Yonglong Tian, name: Lijie Fan, name: Phillip Isola, name: Huiwen Chang, name: Dilip Krishnan
Published: 2023-06-01 17:59:51

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.
SnapFusion: Text-to-Image Diffusion Model on Mobile Devices within Two
  Seconds
Authors: name: Yanyu Li, name: Huan Wang, name: Qing Jin, name: Ju Hu, name: Pavlo Chemerys, name: Yun Fu, name: Yanzhi Wang, name: Sergey Tulyakov, name: Jian Ren
Published: 2023-06-01 17:59:25

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 v1.5 with 50 steps. Our work democratizes content creation
by bringing powerful text-to-image diffusion models to the hands of users.
ViCo: Detail-Preserving Visual Condition for Personalized Text-to-Image
  Generation
Authors: name: Shaozhe Hao, name: Kai Han, name: Shihao Zhao, name: Kwan-Yee K. Wong
Published: 2023-06-01 17:58:44

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.