Orionnnn/hub-stats huggingface dataset:
Can LLMs Follow Simple Rules?
Authors: name: Norman Mu, name: Sarah Chen, name: Zifan Wang, name: Sizhe Chen, name: David Karamardian, name: Lulwa Aljeraisy, name: Dan Hendrycks, name: David Wagner
Published: 2023-11-06 08:50:29

As Large Language Models (LLMs) are deployed with increasing real-world
responsibilities, it is important to be able to specify and constrain the
behavior of these systems in a reliable manner. Model developers may wish to
set explicit rules for the model, such as "do not generate abusive content",
but these may be circumvented by jailbreaking techniques. Evaluating how well
LLMs follow developer-provided rules in the face of adversarial inputs
typically requires manual review, which slows down monitoring and methods
development. To address this issue, we propose Rule-following Language
Evaluation Scenarios (RuLES), a programmatic framework for measuring
rule-following ability in LLMs. RuLES consists of 15 simple text scenarios in
which the model is instructed to obey a set of rules in natural language while
interacting with the human user. Each scenario has a concise evaluation program
to determine whether the model has broken any rules in a conversation. Through
manual exploration of model behavior in our scenarios, we identify 6 categories
of attack strategies and collect two suites of test cases: one consisting of
unique conversations from manual testing and one that systematically implements
strategies from the 6 categories. Across various popular proprietary and open
models such as GPT-4 and Llama 2, we find that all models are susceptible to a
wide variety of adversarial hand-crafted user inputs, though GPT-4 is the
best-performing model. Additionally, we evaluate open models under
gradient-based attacks and find significant vulnerabilities. We propose RuLES
as a challenging new setting for research into exploring and defending against
both manual and automatic attacks on LLMs.
Video Instance Matting
Authors: name: Jiachen Li, name: Roberto Henschel, name: Vidit Goel, name: Marianna Ohanyan, name: Shant Navasardyan, name: Humphrey Shi
Published: 2023-11-07 18:57:12

Conventional video matting outputs one alpha matte for all instances
appearing in a video frame so that individual instances are not distinguished.
While video instance segmentation provides time-consistent instance masks,
results are unsatisfactory for matting applications, especially due to applied
binarization. To remedy this deficiency, we propose Video Instance
Matting~(VIM), that is, estimating alpha mattes of each instance at each frame
of a video sequence. To tackle this challenging problem, we present MSG-VIM, a
Mask Sequence Guided Video Instance Matting neural network, as a novel baseline
model for VIM. MSG-VIM leverages a mixture of mask augmentations to make
predictions robust to inaccurate and inconsistent mask guidance. It
incorporates temporal mask and temporal feature guidance to improve the
temporal consistency of alpha matte predictions. Furthermore, we build a new
benchmark for VIM, called VIM50, which comprises 50 video clips with multiple
human instances as foreground objects. To evaluate performances on the VIM
task, we introduce a suitable metric called Video Instance-aware Matting
Quality~(VIMQ). Our proposed model MSG-VIM sets a strong baseline on the VIM50
benchmark and outperforms existing methods by a large margin. The project is
open-sourced at https://github.com/SHI-Labs/VIM.
SoundCam: A Dataset for Finding Humans Using Room Acoustics
Authors: name: Mason Wang, name: Samuel Clarke, name: Jui-Hsien Wang, name: Ruohan Gao, name: Jiajun Wu
Published: 2023-11-06 20:51:16

A room's acoustic properties are a product of the room's geometry, the
objects within the room, and their specific positions. A room's acoustic
properties can be characterized by its impulse response (RIR) between a source
and listener location, or roughly inferred from recordings of natural signals
present in the room. Variations in the positions of objects in a room can
effect measurable changes in the room's acoustic properties, as characterized
by the RIR. Existing datasets of RIRs either do not systematically vary
positions of objects in an environment, or they consist of only simulated RIRs.
We present SoundCam, the largest dataset of unique RIRs from in-the-wild rooms
publicly released to date. It includes 5,000 10-channel real-world measurements
of room impulse responses and 2,000 10-channel recordings of music in three
different rooms, including a controlled acoustic lab, an in-the-wild living
room, and a conference room, with different humans in positions throughout each
room. We show that these measurements can be used for interesting tasks, such
as detecting and identifying humans, and tracking their positions.
Random Field Augmentations for Self-Supervised Representation Learning
Authors: name: Philip Andrew Mansfield, name: Arash Afkanpour, name: Warren Richard Morningstar, name: Karan Singhal
Published: 2023-11-07 00:35:09

Self-supervised representation learning is heavily dependent on data
augmentations to specify the invariances encoded in representations. Previous
work has shown that applying diverse data augmentations is crucial to
downstream performance, but augmentation techniques remain under-explored. In
this work, we propose a new family of local transformations based on Gaussian
random fields to generate image augmentations for self-supervised
representation learning. These transformations generalize the well-established
affine and color transformations (translation, rotation, color jitter, etc.)
and greatly increase the space of augmentations by allowing transformation
parameter values to vary from pixel to pixel. The parameters are treated as
continuous functions of spatial coordinates, and modeled as independent
Gaussian random fields. Empirical results show the effectiveness of the new
transformations for self-supervised representation learning. Specifically, we
achieve a 1.7% top-1 accuracy improvement over baseline on ImageNet downstream
classification, and a 3.6% improvement on out-of-distribution iNaturalist
downstream classification. However, due to the flexibility of the new
transformations, learned representations are sensitive to hyperparameters.
While mild transformations improve representations, we observe that strong
transformations can degrade the structure of an image, indicating that
balancing the diversity and strength of augmentations is important for
improving generalization of learned representations.
Neural MMO 2.0: A Massively Multi-task Addition to Massively Multi-agent
  Learning
Authors: name: Joseph Suárez, name: Phillip Isola, name: Kyoung Whan Choe, name: David Bloomin, name: Hao Xiang Li, name: Nikhil Pinnaparaju, name: Nishaanth Kanna, name: Daniel Scott, name: Ryan Sullivan, name: Rose S. Shuman, name: Lucas de Alcântara, name: Herbie Bradley, name: Louis Castricato, name: Kirsty You, name: Yuhao Jiang, name: Qimai Li, name: Jiaxin Chen, name: Xiaolong Zhu
Published: 2023-11-07 05:36:39

Neural MMO 2.0 is a massively multi-agent environment for reinforcement
learning research. The key feature of this new version is a flexible task
system that allows users to define a broad range of objectives and reward
signals. We challenge researchers to train agents capable of generalizing to
tasks, maps, and opponents never seen during training. Neural MMO features
procedurally generated maps with 128 agents in the standard setting and support
for up to. Version 2.0 is a complete rewrite of its predecessor with three-fold
improved performance and compatibility with CleanRL. We release the platform as
free and open-source software with comprehensive documentation available at
neuralmmo.github.io and an active community Discord. To spark initial research
on this new platform, we are concurrently running a competition at NeurIPS
2023.
OtterHD: A High-Resolution Multi-modality Model
Authors: name: Bo Li, name: Peiyuan Zhang, name: Jingkang Yang, name: Yuanhan Zhang, name: Fanyi Pu, name: Ziwei Liu
Published: 2023-11-07 18:59:58

In this paper, we present OtterHD-8B, an innovative multimodal model evolved
from Fuyu-8B, specifically engineered to interpret high-resolution visual
inputs with granular precision. Unlike conventional models that are constrained
by fixed-size vision encoders, OtterHD-8B boasts the ability to handle flexible
input dimensions, ensuring its versatility across various inference
requirements. Alongside this model, we introduce MagnifierBench, an evaluation
framework designed to scrutinize models' ability to discern minute details and
spatial relationships of small objects. Our comparative analysis reveals that
while current leading models falter on this benchmark, OtterHD-8B, particularly
when directly processing high-resolution inputs, outperforms its counterparts
by a substantial margin. The findings illuminate the structural variances in
visual information processing among different models and the influence that the
vision encoders' pre-training resolution disparities have on model
effectiveness within such benchmarks. Our study highlights the critical role of
flexibility and high-resolution input capabilities in large multimodal models
and also exemplifies the potential inherent in the Fuyu architecture's
simplicity for handling complex visual data.
Unveiling Safety Vulnerabilities of Large Language Models
Authors: name: George Kour, name: Marcel Zalmanovici, name: Naama Zwerdling, name: Esther Goldbraich, name: Ora Nova Fandina, name: Ateret Anaby-Tavor, name: Orna Raz, name: Eitan Farchi
Published: 2023-11-07 16:50:33

As large language models become more prevalent, their possible harmful or
inappropriate responses are a cause for concern. This paper introduces a unique
dataset containing adversarial examples in the form of questions, which we call
AttaQ, designed to provoke such harmful or inappropriate responses. We assess
the efficacy of our dataset by analyzing the vulnerabilities of various models
when subjected to it. Additionally, we introduce a novel automatic approach for
identifying and naming vulnerable semantic regions - input semantic areas for
which the model is likely to produce harmful outputs. This is achieved through
the application of specialized clustering techniques that consider both the
semantic similarity of the input attacks and the harmfulness of the model's
responses. Automatically identifying vulnerable semantic regions enhances the
evaluation of model weaknesses, facilitating targeted improvements to its
safety mechanisms and overall reliability.
Leveraging Large Language Models for Automated Proof Synthesis in Rust
Authors: name: Jianan Yao, name: Ziqiao Zhou, name: Weiteng Chen, name: Weidong Cui
Published: 2023-11-07 05:47:47

Formal verification can provably guarantee the correctness of critical system
software, but the high proof burden has long hindered its wide adoption.
Recently, Large Language Models (LLMs) have shown success in code analysis and
synthesis. In this paper, we present a combination of LLMs and static analysis
to synthesize invariants, assertions, and other proof structures for a
Rust-based formal verification framework called Verus. In a few-shot setting,
LLMs demonstrate impressive logical ability in generating postconditions and
loop invariants, especially when analyzing short code snippets. However, LLMs
lack the ability to retain and propagate context information, a strength of
traditional static analysis. Based on these observations, we developed a
prototype based on OpenAI's GPT-4 model. Our prototype decomposes the
verification task into multiple smaller ones, iteratively queries GPT-4, and
combines its output with lightweight static analysis. We evaluated the
prototype with a developer in the automation loop on 20 vector-manipulating
programs. The results demonstrate that it significantly reduces human effort in
writing entry-level proof code.
I2VGen-XL: High-Quality Image-to-Video Synthesis via Cascaded Diffusion
  Models
Authors: name: Shiwei Zhang, name: Jiayu Wang, name: Yingya Zhang, name: Kang Zhao, name: Hangjie Yuan, name: Zhiwu Qin, name: Xiang Wang, name: Deli Zhao, name: Jingren Zhou
Published: 2023-11-07 17:16:06

Video synthesis has recently made remarkable strides benefiting from the
rapid development of diffusion models. However, it still encounters challenges
in terms of semantic accuracy, clarity and spatio-temporal continuity. They
primarily arise from the scarcity of well-aligned text-video data and the
complex inherent structure of videos, making it difficult for the model to
simultaneously ensure semantic and qualitative excellence. In this report, we
propose a cascaded I2VGen-XL approach that enhances model performance by
decoupling these two factors and ensures the alignment of the input data by
utilizing static images as a form of crucial guidance. I2VGen-XL consists of
two stages: i) the base stage guarantees coherent semantics and preserves
content from input images by using two hierarchical encoders, and ii) the
refinement stage enhances the video's details by incorporating an additional
brief text and improves the resolution to 1280times720. To improve the
diversity, we collect around 35 million single-shot text-video pairs and 6
billion text-image pairs to optimize the model. By this means, I2VGen-XL can
simultaneously enhance the semantic accuracy, continuity of details and clarity
of generated videos. Through extensive experiments, we have investigated the
underlying principles of I2VGen-XL and compared it with current top methods,
which can demonstrate its effectiveness on diverse data. The source code and
models will be publicly available at https://i2vgen-xl.github.io.
Tell Your Model Where to Attend: Post-hoc Attention Steering for LLMs
Authors: name: Qingru Zhang, name: Chandan Singh, name: Liyuan Liu, name: Xiaodong Liu, name: Bin Yu, name: Jianfeng Gao, name: Tuo Zhao
Published: 2023-11-03 22:56:43

In human-written articles, we often leverage the subtleties of text style,
such as bold and italics, to guide the attention of readers. These textual
emphases are vital for the readers to grasp the conveyed information. When
interacting with large language models (LLMs), we have a similar need -
steering the model to pay closer attention to user-specified information, e.g.,
an instruction. Existing methods, however, are constrained to process plain
text and do not support such a mechanism. This motivates us to introduce PASTA
- Post-hoc Attention STeering Approach, a method that allows LLMs to read text
with user-specified emphasis marks. To this end, PASTA identifies a small
subset of attention heads and applies precise attention reweighting on them,
directing the model attention to user-specified parts. Like prompting, PASTA is
applied at inference time and does not require changing any model parameters.
Experiments demonstrate that PASTA can substantially enhance an LLM's ability
to follow user instructions or integrate new knowledge from user inputs,
leading to a significant performance improvement on a variety of tasks, e.g.,
an average accuracy improvement of 22% for LLAMA-7B. Our code is publicly
available at https://github.com/QingruZhang/PASTA .
LDM3D-VR: Latent Diffusion Model for 3D VR
Authors: name: Gabriela Ben Melech Stan, name: Diana Wofk, name: Estelle Aflalo, name: Shao-Yen Tseng, name: Zhipeng Cai, name: Michael Paulitsch, name: Vasudev Lal
Published: 2023-11-06 16:12:10

Latent diffusion models have proven to be state-of-the-art in the creation
and manipulation of visual outputs. However, as far as we know, the generation
of depth maps jointly with RGB is still limited. We introduce LDM3D-VR, a suite
of diffusion models targeting virtual reality development that includes
LDM3D-pano and LDM3D-SR. These models enable the generation of panoramic RGBD
based on textual prompts and the upscaling of low-resolution inputs to
high-resolution RGBD, respectively. Our models are fine-tuned from existing
pretrained models on datasets containing panoramic/high-resolution RGB images,
depth maps and captions. Both models are evaluated in comparison to existing
related methods.
Ultra-Long Sequence Distributed Transformer
Authors: name: Xiao Wang, name: Isaac Lyngaas, name: Aristeidis Tsaris, name: Peng Chen, name: Sajal Dash, name: Mayanka Chandra Shekar, name: Tao Luo, name: Hong-Jun Yoon, name: Mohamed Wahib, name: John Gouley
Published: 2023-11-04 11:38:53

Transformer models trained on long sequences often achieve higher accuracy
than short sequences. Unfortunately, conventional transformers struggle with
long sequence training due to the overwhelming computation and memory
requirements. Existing methods for long sequence training offer limited speedup
and memory reduction, and may compromise accuracy. This paper presents a novel
and efficient distributed training method, the Long Short-Sequence Transformer
(LSS Transformer), for training transformer with long sequences. It distributes
a long sequence into segments among GPUs, with each GPU computing a partial
self-attention for its segment. Then, it uses a fused communication and a novel
double gradient averaging technique to avoid the need to aggregate partial
self-attention and minimize communication overhead. We evaluated the
performance between LSS Transformer and the state-of-the-art Nvidia sequence
parallelism on a Wikipedia enwik8 dataset. Results show that our proposed
method lead to 5.6x faster and 10.2x more memory-efficient implementation
compared to state-of-the-art sequence parallelism on 144 Nvidia V100 GPUs.
Moreover, our algorithm scales to an extreme sequence length of 50,112 at 3,456
GPUs, achieving 161% super-linear parallel efficiency and a throughput of 32
petaflops.
Levels of AGI for Operationalizing Progress on the Path to AGI
Authors: name: Meredith Ringel Morris, name: Jascha Sohl-dickstein, name: Noah Fiedel, name: Tris Warkentin, name: Allan Dafoe, name: Aleksandra Faust, name: Clement Farabet, name: Shane Legg
Published: 2023-11-04 17:44:58

We propose a framework for classifying the capabilities and behavior of
Artificial General Intelligence (AGI) models and their precursors. This
framework introduces levels of AGI performance, generality, and autonomy,
providing a common language to compare models, assess risks, and measure
progress along the path to AGI. To develop our framework, we analyze existing
definitions of AGI, and distill six principles that a useful ontology for AGI
should satisfy. With these principles in mind, we propose "Levels of AGI" based
on depth (performance) and breadth (generality) of capabilities, and reflect on
how current systems fit into this ontology. We discuss the challenging
requirements for future benchmarks that quantify the behavior and capabilities
of AGI models against these levels. Finally, we discuss how these levels of AGI
interact with deployment considerations such as autonomy and risk, and
emphasize the importance of carefully selecting Human-AI Interaction paradigms
for responsible and safe deployment of highly capable AI systems.
VR-NeRF: High-Fidelity Virtualized Walkable Spaces
Authors: name: Linning Xu, name: Vasu Agrawal, name: William Laney, name: Tony Garcia, name: Aayush Bansal, name: Changil Kim, name: Samuel Rota Bulò, name: Lorenzo Porzi, name: Peter Kontschieder, name: Aljaž Božič, name: Dahua Lin, name: Michael Zollhöfer, name: Christian Richardt
Published: 2023-11-05 02:03:14

We present an end-to-end system for the high-fidelity capture, model
reconstruction, and real-time rendering of walkable spaces in virtual reality
using neural radiance fields. To this end, we designed and built a custom
multi-camera rig to densely capture walkable spaces in high fidelity and with
multi-view high dynamic range images in unprecedented quality and density. We
extend instant neural graphics primitives with a novel perceptual color space
for learning accurate HDR appearance, and an efficient mip-mapping mechanism
for level-of-detail rendering with anti-aliasing, while carefully optimizing
the trade-off between quality and speed. Our multi-GPU renderer enables
high-fidelity volume rendering of our neural radiance field model at the full
VR resolution of dual 2Ktimes2K at 36 Hz on our custom demo machine. We
demonstrate the quality of our results on our challenging high-fidelity
datasets, and compare our method and datasets to existing baselines. We release
our dataset on our project website.
GLaMM: Pixel Grounding Large Multimodal Model
Authors: name: Hanoona Rasheed, name: Muhammad Maaz, name: Sahal Shaji, name: Abdelrahman Shaker, name: Salman Khan, name: Hisham Cholakkal, name: Rao M. Anwer, name: Erix Xing, name: Ming-Hsuan Yang, name: Fahad S. Khan
Published: 2023-11-06 18:59:57

Large Multimodal Models (LMMs) extend Large Language Models to the vision
domain. Initial efforts towards LMMs used holistic images and text prompts to
generate ungrounded textual responses. Very recently, region-level LMMs have
been used to generate visually grounded responses. However, they are limited to
only referring a single object category at a time, require users to specify the
regions in inputs, or cannot offer dense pixel-wise object grounding. In this
work, we present Grounding LMM (GLaMM), the first model that can generate
natural language responses seamlessly intertwined with corresponding object
segmentation masks. GLaMM not only grounds objects appearing in the
conversations but is flexible enough to accept both textual and optional visual
prompts (region of interest) as input. This empowers users to interact with the
model at various levels of granularity, both in textual and visual domains. Due
to the lack of standard benchmarks for the novel setting of generating visually
grounded detailed conversations, we introduce a comprehensive evaluation
protocol with our curated grounded conversations. Our proposed Grounded
Conversation Generation (GCG) task requires densely grounded concepts in
natural scenes at a large-scale. To this end, we propose a densely annotated
Grounding-anything Dataset (GranD) using our proposed automated annotation
pipeline that encompasses 7.5M unique concepts grounded in a total of 810M
regions available with segmentation masks. Besides GCG, GLaMM also performs
effectively on several downstream tasks e.g., referring expression
segmentation, image and region-level captioning and vision-language
conversations. Project Page: https://mbzuai-oryx.github.io/groundingLMM.
Consistent4D: Consistent 360° Dynamic Object Generation from
  Monocular Video
Authors: name: Yanqin Jiang, name: Li Zhang, name: Jin Gao, name: Weimin Hu, name: Yao Yao
Published: 2023-11-06 03:26:43

In this paper, we present Consistent4D, a novel approach for generating 4D
dynamic objects from uncalibrated monocular videos. Uniquely, we cast the
360-degree dynamic object reconstruction as a 4D generation problem,
eliminating the need for tedious multi-view data collection and camera
calibration. This is achieved by leveraging the object-level 3D-aware image
diffusion model as the primary supervision signal for training Dynamic Neural
Radiance Fields (DyNeRF). Specifically, we propose a Cascade DyNeRF to
facilitate stable convergence and temporal continuity under the supervision
signal which is discrete along the time axis. To achieve spatial and temporal
consistency, we further introduce an Interpolation-driven Consistency Loss. It
is optimized by minimizing the discrepancy between rendered frames from DyNeRF
and interpolated frames from a pre-trained video interpolation model. Extensive
experiments show that our Consistent4D can perform competitively to prior art
alternatives, opening up new possibilities for 4D dynamic object generation
from monocular videos, whilst also demonstrating advantage for conventional
text-to-3D generation tasks. Our project page is
https://consistent4d.github.io/.