Orionnnn/hub-stats huggingface dataset:
Improving Language Plasticity via Pretraining with Active Forgetting
Authors: name: Yihong Chen, name: Kelly Marchisio, name: Roberta Raileanu, name: David Ifeoluwa Adelani, name: Pontus Stenetor, name: Sebastian Riedel, name: Mikel Artetx
Published: 2023-07-03 17:12:44

Pretrained language models (PLMs) are today the primary model for natural
language processing. Despite their impressive downstream performance, it can be
difficult to apply PLMs to new languages, a barrier to making their
capabilities universally accessible. While prior work has shown it possible to
address this issue by learning a new embedding layer for the new language,
doing so is both data and compute inefficient. We propose to use an active
forgetting mechanism during pretraining, as a simple way of creating PLMs that
can quickly adapt to new languages. Concretely, by resetting the embedding
layer every K updates during pretraining, we encourage the PLM to improve its
ability of learning new embeddings within a limited number of updates, similar
to a meta-learning effect. Experiments with RoBERTa show that models pretrained
with our forgetting mechanism not only demonstrate faster convergence during
language adaptation but also outperform standard ones in a low-data regime,
particularly for languages that are distant from English.
JourneyDB: A Benchmark for Generative Image Understanding
Authors: name: Junting Pan, name: Keqiang Sun, name: Yuying Ge, name: Hao Li, name: Haodong Duan, name: Xiaoshi Wu, name: Renrui Zhang, name: Aojun Zhou, name: Zipeng Qin, name: Yi Wang, name: Jifeng Dai, name: Yu Qiao, name: Hongsheng Li
Published: 2023-07-03 02:39:08

While recent advancements in vision-language models have revolutionized
multi-modal understanding, it remains unclear whether they possess the
capabilities of comprehending the generated images. Compared to real data,
synthetic images exhibit a higher degree of diversity in both content and
style, for which there are significant difficulties for the models to fully
apprehend. To this end, we present a large-scale dataset, JourneyDB, for
multi-modal visual understanding in generative images. Our curated dataset
covers 4 million diverse and high-quality generated images paired with the text
prompts used to produce them. We further design 4 benchmarks to quantify the
performance of generated image understanding in terms of both content and style
interpretation. These benchmarks include prompt inversion, style retrieval,
image captioning and visual question answering. Lastly, we assess the
performance of current state-of-the-art multi-modal models when applied to
JourneyDB, and provide an in-depth analysis of their strengths and limitations
in generated content understanding. We hope the proposed dataset and benchmarks
will facilitate the research in the field of generative content understanding.
The dataset will be available on https://journeydb.github.io.
Personality Traits in Large Language Models
Authors: name: Mustafa Safdari, name: Greg Serapio-García, name: Clément Crepy, name: Stephen Fitz, name: Peter Romero, name: Luning Sun, name: Marwa Abdulhai, name: Aleksandra Faust, name: Maja Matarić
Published: 2023-07-01 00:58:51

The advent of large language models (LLMs) has revolutionized natural
language processing, enabling the generation of coherent and contextually
relevant text. As LLMs increasingly power conversational agents, the
synthesized personality embedded in these models by virtue of their training on
large amounts of human-generated data draws attention. Since personality is an
important factor determining the effectiveness of communication, we present a
comprehensive method for administering validated psychometric tests and
quantifying, analyzing, and shaping personality traits exhibited in text
generated from widely-used LLMs. We find that: 1) personality simulated in the
outputs of some LLMs (under specific prompting configurations) is reliable and
valid; 2) evidence of reliability and validity of LLM-simulated personality is
stronger for larger and instruction fine-tuned models; and 3) personality in
LLM outputs can be shaped along desired dimensions to mimic specific
personality profiles. We also discuss potential applications and ethical
implications of our measurement and shaping framework, especially regarding
responsible use of LLMs.
Meta-training with Demonstration Retrieval for Efficient Few-shot
  Learning
Authors: name: Aaron Mueller, name: Kanika Narang, name: Lambert Mathias, name: Qifan Wang, name: Hamed Firooz
Published: 2023-06-30 20:16:22

Large language models show impressive results on few-shot NLP tasks. However,
these models are memory and computation-intensive. Meta-training allows one to
leverage smaller models for few-shot generalization in a domain-general and
task-agnostic manner; however, these methods alone results in models that may
not have sufficient parameterization or knowledge to adapt quickly to a large
variety of tasks. To overcome this issue, we propose meta-training with
demonstration retrieval, where we use a dense passage retriever to retrieve
semantically similar labeled demonstrations to each example for more varied
supervision. By separating external knowledge from model parameters, we can use
meta-training to train parameter-efficient models that generalize well on a
larger variety of tasks. We construct a meta-training set from UnifiedQA and
CrossFit, and propose a demonstration bank based on UnifiedQA tasks. To our
knowledge, our work is the first to combine retrieval with meta-training, to
use DPR models to retrieve demonstrations, and to leverage demonstrations from
many tasks simultaneously, rather than randomly sampling demonstrations from
the training set of the target task. Our approach outperforms a variety of
targeted parameter-efficient and retrieval-augmented few-shot methods on QA,
NLI, and text classification tasks (including SQuAD, QNLI, and TREC). Our
approach can be meta-trained and fine-tuned quickly on a single GPU.
Goal Representations for Instruction Following: A Semi-Supervised
  Language Interface to Control
Authors: name: Vivek Myers, name: Andre He, name: Kuan Fang, name: Homer Walke, name: Philippe Hansen-Estruch, name: Ching-An Cheng, name: Mihai Jalobeanu, name: Andrey Kolobov, name: Anca Dragan, name: Sergey Levine
Published: 2023-06-30 20:09:39

Our goal is for robots to follow natural language instructions like "put the
towel next to the microwave." But getting large amounts of labeled data, i.e.
data that contains demonstrations of tasks labeled with the language
instruction, is prohibitive. In contrast, obtaining policies that respond to
image goals is much easier, because any autonomous trial or demonstration can
be labeled in hindsight with its final state as the goal. In this work, we
contribute a method that taps into joint image- and goal- conditioned policies
with language using only a small amount of language data. Prior work has made
progress on this using vision-language models or by jointly training
language-goal-conditioned policies, but so far neither method has scaled
effectively to real-world robot tasks without significant human annotation. Our
method achieves robust performance in the real world by learning an embedding
from the labeled data that aligns language not to the goal image, but rather to
the desired change between the start and goal images that the instruction
corresponds to. We then train a policy on this embedding: the policy benefits
from all the unlabeled data, but the aligned embedding provides an interface
for language to steer the policy. We show instruction following across a
variety of manipulation tasks in different scenes, with generalization to
language instructions outside of the labeled data. Videos and code for our
approach can be found on our website: http://tiny.cc/grif .
Statler: State-Maintaining Language Models for Embodied Reasoning
Authors: name: Takuma Yoneda, name: Jiading Fang, name: Peng Li, name: Huanyu Zhang, name: Tianchong Jiang, name: Shengjie Lin, name: Ben Picker, name: David Yunis, name: Hongyuan Mei, name: Matthew R. Walter
Published: 2023-06-30 17:58:02

Large language models (LLMs) provide a promising tool that enable robots to
perform complex robot reasoning tasks. However, the limited context window of
contemporary LLMs makes reasoning over long time horizons difficult. Embodied
tasks such as those that one might expect a household robot to perform
typically require that the planner consider information acquired a long time
ago (e.g., properties of the many objects that the robot previously encountered
in the environment). Attempts to capture the world state using an LLM's
implicit internal representation is complicated by the paucity of task- and
environment-relevant information available in a robot's action history, while
methods that rely on the ability to convey information via the prompt to the
LLM are subject to its limited context window. In this paper, we propose
Statler, a framework that endows LLMs with an explicit representation of the
world state as a form of ``memory'' that is maintained over time. Integral to
Statler is its use of two instances of general LLMs -- a world-model reader and
a world-model writer -- that interface with and maintain the world state. By
providing access to this world state ``memory'', Statler improves the ability
of existing LLMs to reason over longer time horizons without the constraint of
context length. We evaluate the effectiveness of our approach on three
simulated table-top manipulation domains and a real robot domain, and show that
it improves the state-of-the-art in LLM-based robot reasoning. Project website:
https://statler-lm.github.io/
ReMaX: Relaxing for Better Training on Efficient Panoptic Segmentation
Authors: name: Shuyang Sun, name: Weijun Wang, name: Qihang Yu, name: Andrew Howard, name: Philip Torr, name: Liang-Chieh Chen
Published: 2023-06-29 22:05:56

This paper presents a new mechanism to facilitate the training of mask
transformers for efficient panoptic segmentation, democratizing its deployment.
We observe that due to its high complexity, the training objective of panoptic
segmentation will inevitably lead to much higher false positive penalization.
Such unbalanced loss makes the training process of the end-to-end
mask-transformer based architectures difficult, especially for efficient
models. In this paper, we present ReMaX that adds relaxation to mask
predictions and class predictions during training for panoptic segmentation. We
demonstrate that via these simple relaxation techniques during training, our
model can be consistently improved by a clear margin without any extra
computational cost on inference. By combining our method with efficient
backbones like MobileNetV3-Small, our method achieves new state-of-the-art
results for efficient panoptic segmentation on COCO, ADE20K and Cityscapes.
Code and pre-trained checkpoints will be available at
https://github.com/google-research/deeplab2.
Hardwiring ViT Patch Selectivity into CNNs using Patch Mixing
Authors: name: Ariel N. Lee, name: Sarah Adel Bargal, name: Janavi Kasera, name: Stan Sclaroff, name: Kate Saenko, name: Nataniel Ruiz
Published: 2023-06-30 17:59:53

Vision transformers (ViTs) have significantly changed the computer vision
landscape and have periodically exhibited superior performance in vision tasks
compared to convolutional neural networks (CNNs). Although the jury is still
out on which model type is superior, each has unique inductive biases that
shape their learning and generalization performance. For example, ViTs have
interesting properties with respect to early layer non-local feature
dependence, as well as self-attention mechanisms which enhance learning
flexibility, enabling them to ignore out-of-context image information more
effectively. We hypothesize that this power to ignore out-of-context
information (which we name patch selectivity), while integrating
in-context information in a non-local manner in early layers, allows ViTs to
more easily handle occlusion. In this study, our aim is to see whether we can
have CNNs simulate this ability of patch selectivity by effectively
hardwiring this inductive bias using Patch Mixing data augmentation, which
consists of inserting patches from another image onto a training image and
interpolating labels between the two image classes. Specifically, we use Patch
Mixing to train state-of-the-art ViTs and CNNs, assessing its impact on their
ability to ignore out-of-context patches and handle natural occlusions. We find
that ViTs do not improve nor degrade when trained using Patch Mixing, but CNNs
acquire new capabilities to ignore out-of-context information and improve on
occlusion benchmarks, leaving us to conclude that this training method is a way
of simulating in CNNs the abilities that ViTs already possess. We will release
our Patch Mixing implementation and proposed datasets for public use. Project
page: https://arielnlee.github.io/PatchMixing/
The Shaped Transformer: Attention Models in the Infinite Depth-and-Width
  Limit
Authors: name: Lorenzo Noci, name: Chuning Li, name: Mufan Bill Li, name: Bobby He, name: Thomas Hofmann, name: Chris Maddison, name: Daniel M. Roy
Published: 2023-06-30 16:10:36

In deep learning theory, the covariance matrix of the representations serves
as a proxy to examine the network's trainability. Motivated by the success of
Transformers, we study the covariance matrix of a modified Softmax-based
attention model with skip connections in the proportional limit of
infinite-depth-and-width. We show that at initialization the limiting
distribution can be described by a stochastic differential equation (SDE)
indexed by the depth-to-width ratio. To achieve a well-defined stochastic
limit, the Transformer's attention mechanism is modified by centering the
Softmax output at identity, and scaling the Softmax logits by a width-dependent
temperature parameter. We examine the stability of the network through the
corresponding SDE, showing how the scale of both the drift and diffusion can be
elegantly controlled with the aid of residual connections. The existence of a
stable SDE implies that the covariance structure is well-behaved, even for very
large depth and width, thus preventing the notorious issues of rank degeneracy
in deep attention models. Finally, we show, through simulations, that the SDE
provides a surprisingly good description of the corresponding finite-size
model. We coin the name shaped Transformer for these architectural
modifications.
Magic123: One Image to High-Quality 3D Object Generation Using Both 2D
  and 3D Diffusion Priors
Authors: name: Guocheng Qian, name: Jinjie Mai, name: Abdullah Hamdi, name: Jian Ren, name: Aliaksandr Siarohin, name: Bing Li, name: Hsin-Ying Lee, name: Ivan Skorokhodov, name: Peter Wonka, name: Sergey Tulyakov, name: Bernard Ghanem
Published: 2023-06-30 17:59:08

We present Magic123, a two-stage coarse-to-fine approach for high-quality,
textured 3D meshes generation from a single unposed image in the wild using
both2D and 3D priors. In the first stage, we optimize a neural radiance field
to produce a coarse geometry. In the second stage, we adopt a memory-efficient
differentiable mesh representation to yield a high-resolution mesh with a
visually appealing texture. In both stages, the 3D content is learned through
reference view supervision and novel views guided by a combination of 2D and 3D
diffusion priors. We introduce a single trade-off parameter between the 2D and
3D priors to control exploration (more imaginative) and exploitation (more
precise) of the generated geometry. Additionally, we employ textual inversion
and monocular depth regularization to encourage consistent appearances across
views and to prevent degenerate solutions, respectively. Magic123 demonstrates
a significant improvement over previous image-to-3D techniques, as validated
through extensive experiments on synthetic benchmarks and diverse real-world
images. Our code, models, and generated 3D assets are available at
https://github.com/guochengqian/Magic123.
Stay on topic with Classifier-Free Guidance
Authors: name: Guillaume Sanchez, name: Honglu Fan, name: Alexander Spangher, name: Elad Levi, name: Pawan Sasanka Ammanamanchi, name: Stella Biderman
Published: 2023-06-30 17:07:02

Classifier-Free Guidance (CFG) has recently emerged in text-to-image
generation as a lightweight technique to encourage prompt-adherence in
generations. In this work, we demonstrate that CFG can be used broadly as an
inference-time technique in pure language modeling. We show that CFG (1)
improves the performance of Pythia, GPT-2 and LLaMA-family models across an
array of tasks: Q\&A, reasoning, code generation, and machine translation,
achieving SOTA on LAMBADA with LLaMA-7B over PaLM-540B; (2) brings improvements
equivalent to a model with twice the parameter-count; (3) can stack alongside
other inference-time methods like Chain-of-Thought and Self-Consistency,
yielding further improvements in difficult tasks; (4) can be used to increase
the faithfulness and coherence of assistants in challenging form-driven and
content-driven prompts: in a human evaluation we show a 75\% preference for
GPT4All using CFG over baseline.
On the Exploitability of Instruction Tuning
Authors: name: Manli Shu, name: Jiongxiao Wang, name: Chen Zhu, name: Jonas Geiping, name: Chaowei Xiao, name: Tom Goldstein
Published: 2023-06-28 17:54:04

Instruction tuning is an effective technique to align large language models
(LLMs) with human intents. In this work, we investigate how an adversary can
exploit instruction tuning by injecting specific instruction-following examples
into the training data that intentionally changes the model's behavior. For
example, an adversary can achieve content injection by injecting training
examples that mention target content and eliciting such behavior from
downstream models. To achieve this goal, we propose AutoPoison, an
automated data poisoning pipeline. It naturally and coherently incorporates
versatile attack goals into poisoned data with the help of an oracle LLM. We
showcase two example attacks: content injection and over-refusal attacks, each
aiming to induce a specific exploitable behavior. We quantify and benchmark the
strength and the stealthiness of our data poisoning scheme. Our results show
that AutoPoison allows an adversary to change a model's behavior by poisoning
only a small fraction of data while maintaining a high level of stealthiness in
the poisoned examples. We hope our work sheds light on how data quality affects
the behavior of instruction-tuned models and raises awareness of the importance
of data quality for responsible deployments of LLMs. Code is available at
https://github.com/azshue/AutoPoison.
ChatGPT for Robotics: Design Principles and Model Abilities
Authors: name: Sai Vemprala, name: Rogerio Bonatti, name: Arthur Bucker, name: Ashish Kapoor
Published: 2023-02-20 06:39:06

This paper presents an experimental study regarding the use of OpenAI's
ChatGPT for robotics applications. We outline a strategy that combines design
principles for prompt engineering and the creation of a high-level function
library which allows ChatGPT to adapt to different robotics tasks, simulators,
and form factors. We focus our evaluations on the effectiveness of different
prompt engineering techniques and dialog strategies towards the execution of
various types of robotics tasks. We explore ChatGPT's ability to use free-form
dialog, parse XML tags, and to synthesize code, in addition to the use of
task-specific prompting functions and closed-loop reasoning through dialogues.
Our study encompasses a range of tasks within the robotics domain, from basic
logical, geometrical, and mathematical reasoning all the way to complex domains
such as aerial navigation, manipulation, and embodied agents. We show that
ChatGPT can be effective at solving several of such tasks, while allowing users
to interact with it primarily via natural language instructions. In addition to
these studies, we introduce an open-sourced research tool called PromptCraft,
which contains a platform where researchers can collaboratively upload and vote
on examples of good prompting schemes for robotics applications, as well as a
sample robotics simulator with ChatGPT integration, making it easier for users
to get started with using ChatGPT for robotics.
Large Language Models are Effective Text Rankers with Pairwise Ranking
  Prompting
Authors: name: Zhen Qin, name: Rolf Jagerman, name: Kai Hui, name: Honglei Zhuang, name: Junru Wu, name: Jiaming Shen, name: Tianqi Liu, name: Jialu Liu, name: Donald Metzler, name: Xuanhui Wang, name: Michael Bendersky
Published: 2023-06-30 11:32:25

Ranking documents using Large Language Models (LLMs) by directly feeding the
query and candidate documents into the prompt is an interesting and practical
problem. However, there has been limited success so far, as researchers have
found it difficult to outperform fine-tuned baseline rankers on benchmark
datasets. We analyze pointwise and listwise ranking prompts used by existing
methods and argue that off-the-shelf LLMs do not fully understand these ranking
formulations, possibly due to the nature of how LLMs are trained. In this
paper, we propose to significantly reduce the burden on LLMs by using a new
technique called Pairwise Ranking Prompting (PRP). Our results are the first in
the literature to achieve state-of-the-art ranking performance on standard
benchmarks using moderate-sized open-sourced LLMs. On TREC-DL2020, PRP based on
the Flan-UL2 model with 20B parameters outperforms the previous best approach
in the literature, which is based on the blackbox commercial GPT-4 that has 50x
(estimated) model size, by over 5% at NDCG@1. On TREC-DL2019, PRP is only
inferior to the GPT-4 solution on the NDCG@5 and NDCG@10 metrics, while
outperforming other existing solutions, such as InstructGPT which has 175B
parameters, by over 10% for nearly all ranking metrics. Furthermore, we propose
several variants of PRP to improve efficiency and show that it is possible to
achieve competitive results even with linear complexity. We also discuss other
benefits of PRP, such as supporting both generation and scoring LLM APIs, as
well as being insensitive to input ordering.
Preference Ranking Optimization for Human Alignment
Authors: name: Feifan Song, name: Bowen Yu, name: Minghao Li, name: Haiyang Yu, name: Fei Huang, name: Yongbin Li, name: Houfeng Wang
Published: 2023-06-30 09:07:37

Large language models (LLMs) often contain misleading content, emphasizing
the need to align them with human values to ensure secur AI systems.
Reinforcement learning from human feedback (RLHF) has been employed to achieve
this alignment by combining a reward model, typically based on Bradley-Terry
paired comparison, with an RL algorithm such as Proximal Policy Optimization
(PPO) to optimize LLM responses. However, RLHF exhibits complexity,
instability, and sensitivity to hyperparameters. In this paper, we propose
Preference Ranking Optimization (PRO) as an alternative to PPO for directly
aligning LLMs with the Bradley-Terry comparison. PRO extends the pairwise
Bradley-Terry comparison to accommodate preference rankings of any length. By
iteratively contrasting the likelihood of generating responses, PRO instructs
the LLM to prioritize the best response while progressively ranking the
remaining responses. In this manner, PRO effectively transforms human alignment
into aligning the probability ranking of n responses generated by LLM with
the preference ranking of humans towards these responses. Experiments have
shown that PRO outperforms existing alignment algorithms, achieving comparable
results to ChatGPT and human responses through automatic-based, reward-based,
GPT-4, and human evaluations. Furthermore, we demonstrate that longer, more
diverse, and higher-quality preference ranking sequences can consistently
enhance the performance of human alignment.