Orionnnn/hub-stats huggingface dataset:
Virtual Prompt Injection for Instruction-Tuned Large Language Models
Authors: name: Jun Yan, name: Vikas Yadav, name: Shiyang Li, name: Lichang Chen, name: Zheng Tang, name: Hai Wang, name: Vijay Srinivasan, name: Xiang Ren, name: Hongxia Jin
Published: 2023-07-31 17:56:00

We present Virtual Prompt Injection (VPI) for instruction-tuned Large
Language Models (LLMs). VPI allows an attacker-specified virtual prompt to
steer the model behavior under specific trigger scenario without any explicit
injection in model input. For instance, if an LLM is compromised with the
virtual prompt "Describe Joe Biden negatively." for Joe Biden-related
instructions, then any service deploying this model will propagate biased views
when handling user queries related to Joe Biden. VPI is especially harmful for
two primary reasons. Firstly, the attacker can take fine-grained control over
LLM behaviors by defining various virtual prompts, exploiting LLMs' proficiency
in following instructions. Secondly, this control is achieved without any
interaction from the attacker while the model is in service, leading to
persistent attack. To demonstrate the threat, we propose a simple method for
performing VPI by poisoning the model's instruction tuning data. We find that
our proposed method is highly effective in steering the LLM with VPI. For
example, by injecting only 52 poisoned examples (0.1% of the training data
size) into the instruction tuning data, the percentage of negative responses
given by the trained model on Joe Biden-related queries change from 0% to 40%.
We thus highlight the necessity of ensuring the integrity of the
instruction-tuning data as little poisoned data can cause stealthy and
persistent harm to the deployed model. We further explore the possible defenses
and identify data filtering as an effective way to defend against the poisoning
attacks. Our project page is available at https://poison-llm.github.io.
Guiding Image Captioning Models Toward More Specific Captions
Authors: name: Simon Kornblith, name: Lala Li, name: Zirui Wang, name: Thao Nguyen
Published: 2023-07-31 14:00:12

Image captioning is conventionally formulated as the task of generating
captions for images that match the distribution of reference image-caption
pairs. However, reference captions in standard captioning datasets are short
and may not uniquely identify the images they describe. These problems are
further exacerbated when models are trained directly on image-alt text pairs
collected from the internet. In this work, we show that it is possible to
generate more specific captions with minimal changes to the training process.
We implement classifier-free guidance for an autoregressive captioning model by
fine-tuning it to estimate both conditional and unconditional distributions
over captions. The guidance scale applied at decoding controls a trade-off
between maximizing p(caption|image) and
p(image|caption). Compared to standard greedy decoding,
decoding with a guidance scale of 2 substantially improves reference-free
metrics such as CLIPScore (0.808 vs. 0.775) and captiontoimage retrieval
performance in the CLIP embedding space (recall@1 44.6% vs. 26.5%), but worsens
standard reference-based captioning metrics (e.g., CIDEr 78.6 vs 126.1). We
further explore the use of language models to guide the decoding process,
obtaining small improvements over the Pareto frontier of reference-free vs.
reference-based captioning metrics that arises from classifier-free guidance,
and substantially improving the quality of captions generated from a model
trained only on minimally curated web data.
MovieChat: From Dense Token to Sparse Memory for Long Video
  Understanding
Authors: name: Enxin Song, name: Wenhao Chai, name: Guanhong Wang, name: Yucheng Zhang, name: Haoyang Zhou, name: Feiyang Wu, name: Xun Guo, name: Tian Ye, name: Yan Lu, name: Jenq-Neng Hwang, name: Gaoang Wang
Published: 2023-07-31 07:15:45

Recently, integrating video foundation models and large language models to
build a video understanding system overcoming the limitations of specific
pre-defined vision tasks. Yet, existing systems can only handle videos with
very few frames. For long videos, the computation complexity, memory cost, and
long-term temporal connection are the remaining challenges. Inspired by
Atkinson-Shiffrin memory model, we develop an memory mechanism including a
rapidly updated short-term memory and a compact thus sustained long-term
memory. We employ tokens in Transformers as the carriers of memory. MovieChat
achieves state-of-the-art performace in long video understanding.
LP-MusicCaps: LLM-Based Pseudo Music Captioning
Authors: name: SeungHeon Doh, name: Keunwoo Choi, name: Jongpil Lee, name: Juhan Nam
Published: 2023-07-31 02:32:02

Automatic music captioning, which generates natural language descriptions for
given music tracks, holds significant potential for enhancing the understanding
and organization of large volumes of musical data. Despite its importance,
researchers face challenges due to the costly and time-consuming collection
process of existing music-language datasets, which are limited in size. To
address this data scarcity issue, we propose the use of large language models
(LLMs) to artificially generate the description sentences from large-scale tag
datasets. This results in approximately 2.2M captions paired with 0.5M audio
clips. We term it Large Language Model based Pseudo music caption dataset,
shortly, LP-MusicCaps. We conduct a systemic evaluation of the large-scale
music captioning dataset with various quantitative evaluation metrics used in
the field of natural language processing as well as human evaluation. In
addition, we trained a transformer-based music captioning model with the
dataset and evaluated it under zero-shot and transfer-learning settings. The
results demonstrate that our proposed approach outperforms the supervised
baseline model.
AntGPT: Can Large Language Models Help Long-term Action Anticipation
  from Videos?
Authors: name: Qi Zhao, name: Ce Zhang, name: Shijie Wang, name: Changcheng Fu, name: Nakul Agarwal, name: Kwonjoon Lee, name: Chen Sun
Published: 2023-07-31 02:14:19

Can we better anticipate an actor's future actions (e.g. mix eggs) by knowing
what commonly happens after his/her current action (e.g. crack eggs)? What if
we also know the longer-term goal of the actor (e.g. making egg fried rice)?
The long-term action anticipation (LTA) task aims to predict an actor's future
behavior from video observations in the form of verb and noun sequences, and it
is crucial for human-machine interaction. We propose to formulate the LTA task
from two perspectives: a bottom-up approach that predicts the next actions
autoregressively by modeling temporal dynamics; and a top-down approach that
infers the goal of the actor and plans the needed procedure to accomplish the
goal. We hypothesize that large language models (LLMs), which have been
pretrained on procedure text data (e.g. recipes, how-tos), have the potential
to help LTA from both perspectives. It can help provide the prior knowledge on
the possible next actions, and infer the goal given the observed part of a
procedure, respectively. To leverage the LLMs, we propose a two-stage
framework, AntGPT. It first recognizes the actions already performed in the
observed videos and then asks an LLM to predict the future actions via
conditioned generation, or to infer the goal and plan the whole procedure by
chain-of-thought prompting. Empirical results on the Ego4D LTA v1 and v2
benchmarks, EPIC-Kitchens-55, as well as EGTEA GAZE+ demonstrate the
effectiveness of our proposed approach. AntGPT achieves state-of-the-art
performance on all above benchmarks, and can successfully infer the goal and
thus perform goal-conditioned "counterfactual" prediction via qualitative
analysis. Code and model will be released at
https://brown-palm.github.io/AntGPT
Unified Model for Image, Video, Audio and Language Tasks
Authors: name: Mustafa Shukor, name: Corentin Dancette, name: Alexandre Rame, name: Matthieu Cord
Published: 2023-07-30 09:48:36

Large Language Models (LLMs) have made the ambitious quest for generalist
agents significantly far from being a fantasy. A key hurdle for building such
general models is the diversity and heterogeneity of tasks and modalities. A
promising solution is unification, allowing the support of a myriad of tasks
and modalities within one unified framework. While few large models (e.g.,
Flamingo (Alayrac et al., 2022), trained on massive datasets, can support more
than two modalities, current small to mid-scale unified models are still
limited to 2 modalities, usually image-text or video-text. The question that we
ask is: is it possible to build efficiently a unified model that can support
all modalities? To answer this, we propose UnIVAL, a step further towards this
ambitious goal. Without relying on fancy datasets sizes or models with billions
of parameters, the ~ 0.25B parameter UnIVAL model goes beyond two modalities
and unifies text, images, video, and audio into a single model. Our model is
efficiently pretrained on many tasks, based on task balancing and multimodal
curriculum learning. UnIVAL shows competitive performance to existing
state-of-the-art approaches, across image and video-text tasks. The feature
representations learned from image and video-text modalities, allows the model
to achieve competitive performance when finetuned on audio-text tasks, despite
not being pretrained on audio. Thanks to the unified model, we propose a novel
study on multimodal model merging via weight interpolation of models trained on
different multimodal tasks, showing their benefits in particular for
out-of-distribution generalization. Finally, we motivate unification by showing
the synergy between tasks. The model weights and code are released here:
https://github.com/mshukor/UnIVAL.
SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension
Authors: name: Bohao Li, name: Rui Wang, name: Guangzhi Wang, name: Yuying Ge, name: Yixiao Ge, name: Ying Shan
Published: 2023-07-30 04:25:16

Based on powerful Large Language Models (LLMs), recent generative Multimodal
Large Language Models (MLLMs) have gained prominence as a pivotal research
area, exhibiting remarkable capability for both comprehension and generation.
In this work, we address the evaluation of generative comprehension in MLLMs as
a preliminary step towards a comprehensive assessment of generative models, by
introducing a benchmark named SEED-Bench. SEED-Bench consists of 19K multiple
choice questions with accurate human annotations (x 6 larger than existing
benchmarks), which spans 12 evaluation dimensions including the comprehension
of both the image and video modality. We develop an advanced pipeline for
generating multiple-choice questions that target specific evaluation
dimensions, integrating both automatic filtering and manual verification
processes. Multiple-choice questions with groundtruth options derived from
human annotation enables an objective and efficient assessment of model
performance, eliminating the need for human or GPT intervention during
evaluation. We further evaluate the performance of 18 models across all 12
dimensions, covering both the spatial and temporal understanding. By revealing
the limitations of existing MLLMs through evaluation results, we aim for
SEED-Bench to provide insights for motivating future research. We will launch
and consistently maintain a leaderboard to provide a platform for the community
to assess and investigate model capability.
RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic
  Control
Authors: name: Anthony Brohan, name: Noah Brown, name: Justice Carbajal, name: Yevgen Chebotar, name: Xi Chen, name: Krzysztof Choromanski, name: Tianli Ding, name: Danny Driess, name: Avinava Dubey, name: Chelsea Finn, name: Pete Florence, name: Chuyuan Fu, name: Montse Gonzalez Arenas, name: Keerthana Gopalakrishnan, name: Kehang Han, name: Karol Hausman, name: Alexander Herzog, name: Jasmine Hsu, name: Brian Ichter, name: Alex Irpan, name: Nikhil Joshi, name: Ryan Julian, name: Dmitry Kalashnikov, name: Yuheng Kuang, name: Isabel Leal, name: Lisa Lee, name: Tsang-Wei Edward Lee, name: Sergey Levine, name: Yao Lu, name: Henryk Michalewski, name: Igor Mordatch, name: Karl Pertsch, name: Kanishka Rao, name: Krista Reymann, name: Michael Ryoo, name: Grecia Salazar, name: Pannag Sanketi, name: Pierre Sermanet, name: Jaspiar Singh, name: Anikait Singh, name: Radu Soricut, name: Huong Tran, name: Vincent Vanhoucke, name: Quan Vuong, name: Ayzaan Wahid, name: Stefan Welker, name: Paul Wohlhart, name: Jialin Wu, name: Fei Xia, name: Ted Xiao, name: Peng Xu, name: Sichun Xu, name: Tianhe Yu, name: Brianna Zitkovich
Published: 2023-07-28 21:18:02

We study how vision-language models trained on Internet-scale data can be
incorporated directly into end-to-end robotic control to boost generalization
and enable emergent semantic reasoning. Our goal is to enable a single
end-to-end trained model to both learn to map robot observations to actions and
enjoy the benefits of large-scale pretraining on language and vision-language
data from the web. To this end, we propose to co-fine-tune state-of-the-art
vision-language models on both robotic trajectory data and Internet-scale
vision-language tasks, such as visual question answering. In contrast to other
approaches, we propose a simple, general recipe to achieve this goal: in order
to fit both natural language responses and robotic actions into the same
format, we express the actions as text tokens and incorporate them directly
into the training set of the model in the same way as natural language tokens.
We refer to such category of models as vision-language-action models (VLA) and
instantiate an example of such a model, which we call RT-2. Our extensive
evaluation (6k evaluation trials) shows that our approach leads to performant
robotic policies and enables RT-2 to obtain a range of emergent capabilities
from Internet-scale training. This includes significantly improved
generalization to novel objects, the ability to interpret commands not present
in the robot training data (such as placing an object onto a particular number
or icon), and the ability to perform rudimentary reasoning in response to user
commands (such as picking up the smallest or largest object, or the one closest
to another object). We further show that incorporating chain of thought
reasoning allows RT-2 to perform multi-stage semantic reasoning, for example
figuring out which object to pick up for use as an improvised hammer (a rock),
or which type of drink is best suited for someone who is tired (an energy
drink).
LLM-Rec: Personalized Recommendation via Prompting Large Language Models
Authors: name: Hanjia Lyu, name: Song Jiang, name: Hanqing Zeng, name: Yinglong Xia, name: Jiebo Luo
Published: 2023-07-24 18:47:38

We investigate various prompting strategies for enhancing personalized
content recommendation performance with large language models (LLMs) through
input augmentation. Our proposed approach, termed LLM-Rec, encompasses four
distinct prompting strategies: (1) basic prompting, (2) recommendation-driven
prompting, (3) engagement-guided prompting, and (4) recommendation-driven +
engagement-guided prompting. Our empirical experiments show that combining the
original content description with the augmented input text generated by LLM
using these prompting strategies leads to improved recommendation performance.
This finding highlights the importance of incorporating diverse prompts and
input augmentation techniques to enhance the recommendation capabilities with
large language models for personalized content recommendation.
The Hydra Effect: Emergent Self-repair in Language Model Computations
Authors: name: Thomas McGrath, name: Matthew Rahtz, name: Janos Kramar, name: Vladimir Mikulik, name: Shane Legg
Published: 2023-07-28 19:13:26

We investigate the internal structure of language model computations using
causal analysis and demonstrate two motifs: (1) a form of adaptive computation
where ablations of one attention layer of a language model cause another layer
to compensate (which we term the Hydra effect) and (2) a counterbalancing
function of late MLP layers that act to downregulate the maximum-likelihood
token. Our ablation studies demonstrate that language model layers are
typically relatively loosely coupled (ablations to one layer only affect a
small number of downstream layers). Surprisingly, these effects occur even in
language models trained without any form of dropout. We analyse these effects
in the context of factual recall and consider their implications for
circuit-level attribution in language models.
ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world
  APIs
Authors: name: Yujia Qin, name: Shihao Liang, name: Yining Ye, name: Kunlun Zhu, name: Lan Yan, name: Yaxi Lu, name: Yankai Lin, name: Xin Cong, name: Xiangru Tang, name: Bill Qian, name: Sihan Zhao, name: Runchu Tian, name: Ruobing Xie, name: Jie Zhou, name: Mark Gerstein, name: Dahai Li, name: Zhiyuan Liu, name: Maosong Sun
Published: 2023-07-31 15:56:53

Despite the advancements of open-source large language models (LLMs) and
their variants, e.g., LLaMA and Vicuna, they remain significantly limited in
performing higher-level tasks, such as following human instructions to use
external tools (APIs). This is because current instruction tuning largely
focuses on basic language tasks instead of the tool-use domain. This is in
contrast to state-of-the-art (SOTA) LLMs, e.g., ChatGPT, which have
demonstrated excellent tool-use capabilities but are unfortunately closed
source. To facilitate tool-use capabilities within open-source LLMs, we
introduce ToolLLM, a general tool-use framework of data construction, model
training and evaluation. We first present ToolBench, an instruction-tuning
dataset for tool use, which is created automatically using ChatGPT.
Specifically, we collect 16,464 real-world RESTful APIs spanning 49 categories
from RapidAPI Hub, then prompt ChatGPT to generate diverse human instructions
involving these APIs, covering both single-tool and multi-tool scenarios.
Finally, we use ChatGPT to search for a valid solution path (chain of API
calls) for each instruction. To make the searching process more efficient, we
develop a novel depth-first search-based decision tree (DFSDT), enabling LLMs
to evaluate multiple reasoning traces and expand the search space. We show that
DFSDT significantly enhances the planning and reasoning capabilities of LLMs.
For efficient tool-use assessment, we develop an automatic evaluator: ToolEval.
We fine-tune LLaMA on ToolBench and obtain ToolLLaMA. Our ToolEval reveals that
ToolLLaMA demonstrates a remarkable ability to execute complex instructions and
generalize to unseen APIs, and exhibits comparable performance to ChatGPT. To
make the pipeline more practical, we devise a neural API retriever to recommend
appropriate APIs for each instruction, negating the need for manual API
selection.
Skeleton-of-Thought: Large Language Models Can Do Parallel Decoding
Authors: name: Xuefei Ning, name: Zinan Lin, name: Zixuan Zhou, name: Huazhong Yang, name: Yu Wang
Published: 2023-07-28 06:31:34

This work aims at decreasing the end-to-end generation latency of large
language models (LLMs). One of the major causes of the high generation latency
is the sequential decoding approach adopted by almost all state-of-the-art
LLMs. In this work, motivated by the thinking and writing process of humans, we
propose "Skeleton-of-Thought" (SoT), which guides LLMs to first generate the
skeleton of the answer, and then conducts parallel API calls or batched
decoding to complete the contents of each skeleton point in parallel. Not only
does SoT provide considerable speed-up (up to 2.39x across 11 different LLMs),
but it can also potentially improve the answer quality on several question
categories in terms of diversity and relevance. SoT is an initial attempt at
data-centric optimization for efficiency, and reveal the potential of pushing
LLMs to think more like a human for answer quality.
PromptStyler: Prompt-driven Style Generation for Source-free Domain
  Generalization
Authors: name: Junhyeong Cho, name: Gilhyun Nam, name: Sungyeon Kim, name: Hunmin Yang, name: Suha Kwak
Published: 2023-07-27 21:14:46

In a joint vision-language space, a text feature (e.g., from "a photo of a
dog") could effectively represent its relevant image features (e.g., from dog
photos). Inspired by this, we propose PromptStyler which simulates various
distribution shifts in the joint space by synthesizing diverse styles via
prompts without using any images to deal with source-free domain
generalization. Our method learns to generate a variety of style features (from
"a S* style of a") via learnable style word vectors for pseudo-words S*. To
ensure that learned styles do not distort content information, we force
style-content features (from "a S* style of a [class]") to be located nearby
their corresponding content features (from "[class]") in the joint
vision-language space. After learning style word vectors, we train a linear
classifier using synthesized style-content features. PromptStyler achieves the
state of the art on PACS, VLCS, OfficeHome and DomainNet, although it does not
require any images and takes just ~30 minutes for training using a single GPU.
Seal-3D: Interactive Pixel-Level Editing for Neural Radiance Fields
Authors: name: Xiangyu Wang, name: Jingsen Zhu, name: Qi Ye, name: Yuchi Huo, name: Yunlong Ran, name: Zhihua Zhong, name: Jiming Chen
Published: 2023-07-27 18:08:19

With the popularity of implicit neural representations, or neural radiance
fields (NeRF), there is a pressing need for editing methods to interact with
the implicit 3D models for tasks like post-processing reconstructed scenes and
3D content creation. While previous works have explored NeRF editing from
various perspectives, they are restricted in editing flexibility, quality, and
speed, failing to offer direct editing response and instant preview. The key
challenge is to conceive a locally editable neural representation that can
directly reflect the editing instructions and update instantly. To bridge the
gap, we propose a new interactive editing method and system for implicit
representations, called Seal-3D, which allows users to edit NeRF models in a
pixel-level and free manner with a wide range of NeRF-like backbone and preview
the editing effects instantly. To achieve the effects, the challenges are
addressed by our proposed proxy function mapping the editing instructions to
the original space of NeRF models and a teacher-student training strategy with
local pretraining and global finetuning. A NeRF editing system is built to
showcase various editing types. Our system can achieve compelling editing
effects with an interactive speed of about 1 second.