Orionnnn/hub-stats huggingface dataset:
TOFU: A Task of Fictitious Unlearning for LLMs
Authors: name: Pratyush Maini, name: Zhili Feng, name: Avi Schwarzschild, name: Zachary C. Lipton, name: J. Zico Kolter
Published: 2024-01-11 18:57:12

Large language models trained on massive corpora of data from the web can
memorize and reproduce sensitive or private data raising both legal and ethical
concerns. Unlearning, or tuning models to forget information present in their
training data, provides us with a way to protect private data after training.
Although several methods exist for such unlearning, it is unclear to what
extent they result in models equivalent to those where the data to be forgotten
was never learned in the first place. To address this challenge, we present
TOFU, a Task of Fictitious Unlearning, as a benchmark aimed at helping deepen
our understanding of unlearning. We offer a dataset of 200 diverse synthetic
author profiles, each consisting of 20 question-answer pairs, and a subset of
these profiles called the forget set that serves as the target for unlearning.
We compile a suite of metrics that work together to provide a holistic picture
of unlearning efficacy. Finally, we provide a set of baseline results from
existing unlearning algorithms. Importantly, none of the baselines we consider
show effective unlearning motivating continued efforts to develop approaches
for unlearning that effectively tune models so that they truly behave as if
they were never trained on the forget data at all.
Patchscope: A Unifying Framework for Inspecting Hidden Representations
  of Language Models
Authors: name: Asma Ghandeharioun, name: Avi Caciularu, name: Adam Pearce, name: Lucas Dixon, name: Mor Geva
Published: 2024-01-11 18:33:48

Inspecting the information encoded in hidden representations of large
language models (LLMs) can explain models' behavior and verify their alignment
with human values. Given the capabilities of LLMs in generating
human-understandable text, we propose leveraging the model itself to explain
its internal representations in natural language. We introduce a framework
called Patchscopes and show how it can be used to answer a wide range of
research questions about an LLM's computation. We show that prior
interpretability methods based on projecting representations into the
vocabulary space and intervening on the LLM computation, can be viewed as
special instances of this framework. Moreover, several of their shortcomings
such as failure in inspecting early layers or lack of expressivity can be
mitigated by a Patchscope. Beyond unifying prior inspection techniques,
Patchscopes also opens up new possibilities such as using a more capable model
to explain the representations of a smaller model, and unlocks new applications
such as self-correction in multi-hop reasoning.
Secrets of RLHF in Large Language Models Part II: Reward Modeling
Authors: name: Binghai Wang, name: Rui Zheng, name: Lu Chen, name: Yan Liu, name: Shihan Dou, name: Caishuang Huang, name: Wei Shen, name: Senjie Jin, name: Enyu Zhou, name: Chenyu Shi, name: Songyang Gao, name: Nuo Xu, name: Yuhao Zhou, name: Xiaoran Fan, name: Zhiheng Xi, name: Jun Zhao, name: Xiao Wang, name: Tao Ji, name: Hang Yan, name: Lixing Shen, name: Zhan Chen, name: Tao Gui, name: Qi Zhang, name: Xipeng Qiu, name: Xuanjing Huang, name: Zuxuan Wu, name: Yu-Gang Jiang
Published: 2024-01-11 17:56:59

Reinforcement Learning from Human Feedback (RLHF) has become a crucial
technology for aligning language models with human values and intentions,
enabling models to produce more helpful and harmless responses. Reward models
are trained as proxies for human preferences to drive reinforcement learning
optimization. While reward models are often considered central to achieving
high performance, they face the following challenges in practical applications:
(1) Incorrect and ambiguous preference pairs in the dataset may hinder the
reward model from accurately capturing human intent. (2) Reward models trained
on data from a specific distribution often struggle to generalize to examples
outside that distribution and are not suitable for iterative RLHF training.
  In this report, we attempt to address these two issues. (1) From a data
perspective, we propose a method to measure the strength of preferences within
the data, based on a voting mechanism of multiple reward models. Experimental
results confirm that data with varying preference strengths have different
impacts on reward model performance. We introduce a series of novel methods to
mitigate the influence of incorrect and ambiguous preferences in the dataset
and fully leverage high-quality preference data. (2) From an algorithmic
standpoint, we introduce contrastive learning to enhance the ability of reward
models to distinguish between chosen and rejected responses, thereby improving
model generalization. Furthermore, we employ meta-learning to enable the reward
model to maintain the ability to differentiate subtle differences in
out-of-distribution samples, and this approach can be utilized for iterative
RLHF optimization.
LEGO:Language Enhanced Multi-modal Grounding Model
Authors: name: Zhaowei Li, name: Qi Xu, name: Dong Zhang, name: Hang Song, name: Yiqing Cai, name: Qi Qi, name: Ran Zhou, name: Junting Pan, name: Zefeng Li, name: Van Tu Vu, name: Zhida Huang, name: Tao Wang
Published: 2024-01-11 17:41:57

Multi-modal large language models have demonstrated impressive performance
across various tasks in different modalities. However, existing multi-modal
models primarily emphasize capturing global information within each modality
while neglecting the importance of perceiving local information across
modalities. Consequently, these models lack the ability to effectively
understand the fine-grained details of input data, limiting their performance
in tasks that require a more nuanced understanding. To address this limitation,
there is a compelling need to develop models that enable fine-grained
understanding across multiple modalities, thereby enhancing their applicability
to a wide range of tasks. In this paper, we propose LEGO, a language enhanced
multi-modal grounding model. Beyond capturing global information like other
multi-modal models, our proposed model excels at tasks demanding a detailed
understanding of local information within the input. It demonstrates precise
identification and localization of specific regions in images or moments in
videos. To achieve this objective, we design a diversified dataset construction
pipeline, resulting in a multi-modal, multi-granularity dataset for model
training. The code, dataset, and demo of our model can be found at https:
//github.com/lzw-lzw/LEGO.
DeepSeekMoE: Towards Ultimate Expert Specialization in
  Mixture-of-Experts Language Models
Authors: name: Damai Dai, name: Chengqi Deng, name: Chenggang Zhao, name: R. X. Xu, name: Huazuo Gao, name: Deli Chen, name: Jiashi Li, name: Wangding Zeng, name: Xingkai Yu, name: Y. Wu, name: Zhenda Xie, name: Y. K. Li, name: Panpan Huang, name: Fuli Luo, name: Chong Ruan, name: Zhifang Sui, name: Wenfeng Liang
Published: 2024-01-11 17:31:42

In the era of large language models, Mixture-of-Experts (MoE) is a promising
architecture for managing computational costs when scaling up model parameters.
However, conventional MoE architectures like GShard, which activate the top-K
out of N experts, face challenges in ensuring expert specialization, i.e.
each expert acquires non-overlapping and focused knowledge. In response, we
propose the DeepSeekMoE architecture towards ultimate expert specialization. It
involves two principal strategies: (1) finely segmenting the experts into mN
ones and activating mK from them, allowing for a more flexible combination of
activated experts; (2) isolating K_s experts as shared ones, aiming at
capturing common knowledge and mitigating redundancy in routed experts.
Starting from a modest scale with 2B parameters, we demonstrate that
DeepSeekMoE 2B achieves comparable performance with GShard 2.9B, which has 1.5
times the expert parameters and computation. In addition, DeepSeekMoE 2B nearly
approaches the performance of its dense counterpart with the same number of
total parameters, which set the upper bound of MoE models. Subsequently, we
scale up DeepSeekMoE to 16B parameters and show that it achieves comparable
performance with LLaMA2 7B, with only about 40% of computations. Further, our
preliminary efforts to scale up DeepSeekMoE to 145B parameters consistently
validate its substantial advantages over the GShard architecture, and show its
performance comparable with DeepSeek 67B, using only 28.5% (maybe even 18.2%)
of computations.
Tuning LLMs with Contrastive Alignment Instructions for Machine
  Translation in Unseen, Low-resource Languages
Authors: name: Zhuoyuan Mao, name: Yen Yu
Published: 2024-01-11 10:28:17

This article introduces contrastive alignment instructions (AlignInstruct) to
address two challenges in machine translation (MT) on large language models
(LLMs). One is the expansion of supported languages to previously unseen ones.
The second relates to the lack of data in low-resource languages. Model
fine-tuning through MT instructions (MTInstruct) is a straightforward approach
to the first challenge. However, MTInstruct is limited by weak cross-lingual
signals inherent in the second challenge. AlignInstruct emphasizes
cross-lingual supervision via a cross-lingual discriminator built using
statistical word alignments. Our results based on fine-tuning the BLOOMZ models
(1b1, 3b, and 7b1) in up to 24 unseen languages showed that: (1) LLMs can
effectively translate unseen languages using MTInstruct; (2) AlignInstruct led
to consistent improvements in translation quality across 48 translation
directions involving English; (3) Discriminator-based instructions outperformed
their generative counterparts as cross-lingual instructions; (4) AlignInstruct
improved performance in 30 zero-shot directions.
A Shocking Amount of the Web is Machine Translated: Insights from
  Multi-Way Parallelism
Authors: name: Brian Thompson, name: Mehak Preet Dhaliwal, name: Peter Frisch, name: Tobias Domhan, name: Marcello Federico
Published: 2024-01-11 08:56:13

We show that content on the web is often translated into many languages, and
the low quality of these multi-way translations indicates they were likely
created using Machine Translation (MT). Multi-way parallel, machine generated
content not only dominates the translations in lower resource languages; it
also constitutes a large fraction of the total web content in those languages.
We also find evidence of a selection bias in the type of content which is
translated into many languages, consistent with low quality English content
being translated en masse into many lower resource languages, via MT. Our work
raises serious concerns about training models such as multilingual large
language models on both monolingual and bilingual data scraped from the web.
Towards Conversational Diagnostic AI
Authors: name: Tao Tu, name: Anil Palepu, name: Mike Schaekermann, name: Khaled Saab, name: Jan Freyberg, name: Ryutaro Tanno, name: Amy Wang, name: Brenna Li, name: Mohamed Amin, name: Nenad Tomasev, name: Shekoofeh Azizi, name: Karan Singhal, name: Yong Cheng, name: Le Hou, name: Albert Webson, name: Kavita Kulkarni, name: S Sara Mahdavi, name: Christopher Semturs, name: Juraj Gottweis, name: Joelle Barral, name: Katherine Chou, name: Greg S Corrado, name: Yossi Matias, name: Alan Karthikesalingam, name: Vivek Natarajan
Published: 2024-01-11 04:25:06

At the heart of medicine lies the physician-patient dialogue, where skillful
history-taking paves the way for accurate diagnosis, effective management, and
enduring trust. Artificial Intelligence (AI) systems capable of diagnostic
dialogue could increase accessibility, consistency, and quality of care.
However, approximating clinicians' expertise is an outstanding grand challenge.
Here, we introduce AMIE (Articulate Medical Intelligence Explorer), a Large
Language Model (LLM) based AI system optimized for diagnostic dialogue.
  AMIE uses a novel self-play based simulated environment with automated
feedback mechanisms for scaling learning across diverse disease conditions,
specialties, and contexts. We designed a framework for evaluating
clinically-meaningful axes of performance including history-taking, diagnostic
accuracy, management reasoning, communication skills, and empathy. We compared
AMIE's performance to that of primary care physicians (PCPs) in a randomized,
double-blind crossover study of text-based consultations with validated patient
actors in the style of an Objective Structured Clinical Examination (OSCE). The
study included 149 case scenarios from clinical providers in Canada, the UK,
and India, 20 PCPs for comparison with AMIE, and evaluations by specialist
physicians and patient actors. AMIE demonstrated greater diagnostic accuracy
and superior performance on 28 of 32 axes according to specialist physicians
and 24 of 26 axes according to patient actors. Our research has several
limitations and should be interpreted with appropriate caution. Clinicians were
limited to unfamiliar synchronous text-chat which permits large-scale
LLM-patient interactions but is not representative of usual clinical practice.
While further research is required before AMIE could be translated to
real-world settings, the results represent a milestone towards conversational
diagnostic AI.
Transformers are Multi-State RNNs
Authors: name: Matanel Oren, name: Michael Hassid, name: Yossi Adi, name: Roy Schwartz
Published: 2024-01-11 18:35:26

Transformers are considered conceptually different compared to the previous
generation of state-of-the-art NLP models - recurrent neural networks (RNNs).
In this work, we demonstrate that decoder-only transformers can in fact be
conceptualized as infinite multi-state RNNs - an RNN variant with unlimited
hidden state size. We further show that pretrained transformers can be
converted into finite multi-state RNNs by fixing the size of their
hidden state. We observe that several existing transformers cache compression
techniques can be framed as such conversion policies, and introduce a novel
policy, TOVA, which is simpler compared to these policies. Our experiments with
several long range tasks indicate that TOVA outperforms all other baseline
policies, while being nearly on par with the full (infinite) model, and using
in some cases only 1{8} of the original cache size. Our results
indicate that transformer decoder LLMs often behave in practice as RNNs. They
also lay out the option of mitigating one of their most painful computational
bottlenecks - the size of their cache memory. We publicly release our code at
https://github.com/schwartz-lab-NLP/TOVA.
Sleeper Agents: Training Deceptive LLMs that Persist Through Safety
  Training
Authors: name: Evan Hubinger, name: Carson Denison, name: Jesse Mu, name: Mike Lambert, name: Meg Tong, name: Monte MacDiarmid, name: Tamera Lanham, name: Daniel M. Ziegler, name: Tim Maxwell, name: Newton Cheng, name: Adam Jermyn, name: Amanda Askell, name: Ansh Radhakrishnan, name: Cem Anil, name: David Duvenaud, name: Deep Ganguli, name: Fazl Barez, name: Jack Clark, name: Kamal Ndousse, name: Kshitij Sachan, name: Michael Sellitto, name: Mrinank Sharma, name: Nova DasSarma, name: Roger Grosse, name: Shauna Kravec, name: Yuntao Bai, name: Zachary Witten, name: Marina Favaro, name: Jan Brauner, name: Holden Karnofsky, name: Paul Christiano, name: Samuel R. Bowman, name: Logan Graham, name: Jared Kaplan, name: Sören Mindermann, name: Ryan Greenblatt, name: Buck Shlegeris, name: Nicholas Schiefer, name: Ethan Perez
Published: 2024-01-10 22:14:35

Humans are capable of strategically deceptive behavior: behaving helpfully in
most situations, but then behaving very differently in order to pursue
alternative objectives when given the opportunity. If an AI system learned such
a deceptive strategy, could we detect it and remove it using current
state-of-the-art safety training techniques? To study this question, we
construct proof-of-concept examples of deceptive behavior in large language
models (LLMs). For example, we train models that write secure code when the
prompt states that the year is 2023, but insert exploitable code when the
stated year is 2024. We find that such backdoored behavior can be made
persistent, so that it is not removed by standard safety training techniques,
including supervised fine-tuning, reinforcement learning, and adversarial
training (eliciting unsafe behavior and then training to remove it). The
backdoored behavior is most persistent in the largest models and in models
trained to produce chain-of-thought reasoning about deceiving the training
process, with the persistence remaining even when the chain-of-thought is
distilled away. Furthermore, rather than removing backdoors, we find that
adversarial training can teach models to better recognize their backdoor
triggers, effectively hiding the unsafe behavior. Our results suggest that,
once a model exhibits deceptive behavior, standard techniques could fail to
remove such deception and create a false impression of safety.
Distilling Vision-Language Models on Millions of Videos
Authors: name: Yue Zhao, name: Long Zhao, name: Xingyi Zhou, name: Jialin Wu, name: Chun-Te Chu, name: Hui Miao, name: Florian Schroff, name: Hartwig Adam, name: Ting Liu, name: Boqing Gong, name: Philipp Krähenbühl, name: Liangzhe Yuan
Published: 2024-01-11 18:59:53

The recent advance in vision-language models is largely attributed to the
abundance of image-text data. We aim to replicate this success for
video-language models, but there simply is not enough human-curated video-text
data available. We thus resort to fine-tuning a video-language model from a
strong image-language baseline with synthesized instructional data. The
resulting video-language model is then used to auto-label millions of videos to
generate high-quality captions. We show the adapted video-language model
performs well on a wide range of video-language benchmarks. For instance, it
surpasses the best prior result on open-ended NExT-QA by 2.8%. Besides, our
model generates detailed descriptions for previously unseen videos, which
provide better textual supervision than existing methods. Experiments show that
a video-language dual-encoder model contrastively trained on these
auto-generated captions is 3.8% better than the strongest baseline that also
leverages vision-language models. Our best model outperforms state-of-the-art
methods on MSR-VTT zero-shot text-to-video retrieval by 6%.
Efficient LLM inference solution on Intel GPU
Authors: name: Hui Wu, name: Yi Gan, name: Feng Yuan, name: Jing Ma, name: Wei Zhu, name: Yutao Xu, name: Hong Zhu, name: Yuhua Zhu, name: Xiaoli Liu, name: Jinghui Gu
Published: 2023-12-19 05:40:43

Transformer based Large Language Models (LLMs) have been widely used in many
fields, and the efficiency of LLM inference becomes hot topic in real
applications. However, LLMs are usually complicatedly designed in model
structure with massive operations and perform inference in the auto-regressive
mode, making it a challenging task to design a system with high efficiency.
  In this paper, we propose an efficient LLM inference solution with low
latency and high throughput. Firstly, we simplify the LLM decoder layer by
fusing data movement and element-wise operations to reduce the memory access
frequency and lower system latency. We also propose a segment KV cache policy
to keep key/value of the request and response tokens in separate physical
memory for effective device memory management, helping enlarge the runtime
batch size and improve system throughput. A customized
Scaled-Dot-Product-Attention kernel is designed to match our fusion policy
based on the segment KV cache solution. We implement our LLM inference solution
on Intel GPU and publish it publicly. Compared with the standard HuggingFace
implementation, the proposed solution achieves up to 7x lower token latency and
27x higher throughput for some popular LLMs on Intel GPU.
ANIM-400K: A Large-Scale Dataset for Automated End-To-End Dubbing of
  Video
Authors: name: Kevin Cai, name: Chonghua Liu, name: David M. Chan
Published: 2024-01-10 18:32:38

The Internet's wealth of content, with up to 60% published in English,
starkly contrasts the global population, where only 18.8% are English speakers,
and just 5.1% consider it their native language, leading to disparities in
online information access. Unfortunately, automated processes for dubbing of
video - replacing the audio track of a video with a translated alternative -
remains a complex and challenging task due to pipelines, necessitating precise
timing, facial movement synchronization, and prosody matching. While end-to-end
dubbing offers a solution, data scarcity continues to impede the progress of
both end-to-end and pipeline-based methods. In this work, we introduce
Anim-400K, a comprehensive dataset of over 425K aligned animated video segments
in Japanese and English supporting various video-related tasks, including
automated dubbing, simultaneous translation, guided video summarization, and
genre/theme/style classification. Our dataset is made publicly available for
research purposes at https://github.com/davidmchan/Anim400K.
Score Distillation Sampling with Learned Manifold Corrective
Authors: name: Thiemo Alldieck, name: Nikos Kolotouros, name: Cristian Sminchisescu
Published: 2024-01-10 17:51:46

Score Distillation Sampling (SDS) is a recent but already widely popular
method that relies on an image diffusion model to control optimization problems
using text prompts. In this paper, we conduct an in-depth analysis of the SDS
loss function, identify an inherent problem with its formulation, and propose a
surprisingly easy but effective fix. Specifically, we decompose the loss into
different factors and isolate the component responsible for noisy gradients. In
the original formulation, high text guidance is used to account for the noise,
leading to unwanted side effects. Instead, we train a shallow network mimicking
the timestep-dependent denoising deficiency of the image diffusion model in
order to effectively factor it out. We demonstrate the versatility and the
effectiveness of our novel loss formulation through several qualitative and
quantitative experiments, including optimization-based image synthesis and
editing, zero-shot image translation network training, and text-to-3D
synthesis.
InseRF: Text-Driven Generative Object Insertion in Neural 3D Scenes
Authors: name: Mohamad Shahbazi, name: Liesbeth Claessens, name: Michael Niemeyer, name: Edo Collins, name: Alessio Tonioni, name: Luc Van Gool, name: Federico Tombari
Published: 2024-01-10 18:59:53

We introduce InseRF, a novel method for generative object insertion in the
NeRF reconstructions of 3D scenes. Based on a user-provided textual description
and a 2D bounding box in a reference viewpoint, InseRF generates new objects in
3D scenes. Recently, methods for 3D scene editing have been profoundly
transformed, owing to the use of strong priors of text-to-image diffusion
models in 3D generative modeling. Existing methods are mostly effective in
editing 3D scenes via style and appearance changes or removing existing
objects. Generating new objects, however, remains a challenge for such methods,
which we address in this study. Specifically, we propose grounding the 3D
object insertion to a 2D object insertion in a reference view of the scene. The
2D edit is then lifted to 3D using a single-view object reconstruction method.
The reconstructed object is then inserted into the scene, guided by the priors
of monocular depth estimation methods. We evaluate our method on various 3D
scenes and provide an in-depth analysis of the proposed components. Our
experiments with generative insertion of objects in several 3D scenes indicate
the effectiveness of our method compared to the existing methods. InseRF is
capable of controllable and 3D-consistent object insertion without requiring
explicit 3D information as input. Please visit our project page at
https://mohamad-shahbazi.github.io/inserf.