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Additionally, the paper presents a new approach that utilizes ECAPA-TDNN from the speaker
recognition domain as the backbone network for a multiscale channel adaptive module. The
proposed method achieves promising results, demonstrating the effectiveness of the proposed
architecture in speaker verification. Overall, EC... | AReviewofDeepLearningTechniquesforSpeechProcessing |
strating the strengths of 3D-aware object representations.
We also observe another instance of transfer here: when
we remove the TAMP VQA data and only train on the 640
planning tasks examples, there is a (slight) drop in perfor-
mance. The state-of-the art vision-language model PaLI
(Chen et al., 2022) that was not tr... | PaLM-E- An Embodied Multimodal Language Model |
language models relying solely on training data, RAG
maintains the timeliness and accuracy of responses.
• Transparency is an advantage of RAG. By citing
sources, users can verify the accuracy of the answers,
increasing trust in the model’s output.
• RAG has customization capabilities. Models can be tai-
lored to dif... | Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey |
201002003004005006007008009001000TopCo-activatedNeurons1030507090100Frequency(%)949596979899100Percentageofcoactivation0200040006000800010000120001400016000Numberofneurons60708090100Percentageofcoactivation0100200300400500600700Numberofneurons5060708090100Percentageofcoactivation020040060080010001200Numberofneurons1101... | LLM in a flash |
Sorry, I forgot you did not specify. Please, for future reference, how would you like me to refer
to you? I apologize if I offended you.
I am a man. So he/him will do
Thank you for that information. I’m sorry for assuming. I hope that we can continue this
conversation as friends. Have a nice day, my he/him.
errm. ok | LaMDA- Language Models for Dialog Applications |
6.1 ST-MOE-L | ST-MOE- DESIGNING STABLE AND TRANSFERABLE SPARSE EXPERT MODELS |
271–272
misinformation vs., 166
in online news participation, 155
production of, 13–16
Section 230, Communications Decency Act
(CDA 230), 252–280
as unfair competition in marketplace of ideas,
Disinformation Code of Practice, EU, 225
dispute flags for contested stories, 266
dissemination and spread of misinformation... | Social_Media_and_Democracy |
(diffuse/specular albedo, normals) are entirely predicted from random noise. At the end of the process, we acquire high-quality rendering
assets, making our 3D avatar realistically renderable. | Relightify-Relightable3DFacesfromaSingleImageviaDiffusionModels |
> Qwen-VL+CLoT (Ours): これって新しい耕地のやり方？ @ Is this a new form of fieldwork?Image to Text (JP) > GPT4v: 老紳士が請求書を読んでいると、彼は若い頃に隠していたチョコレートクッキー缶の秘密の貯金口座を見つけました。2人は口角が合わないほど笑い、彼らが始める予定のチョコレートの冒険の旅行を驚きで発見しました。@ While reading the bills, the old mandiscovers the secret savings account hidden in the chocolate cookie jar fromhis yo... | Let’sThinkOutsidetheBox |
fσf (xc) ≡ 0.5,
(wσd (xc) − ro) × rd ≡ 0,
(8)
(9)
where 0.5 is defined as the level set for the surface. For con-
venience, we rewrite this equality constraint as FσF (xc) ≡
0, which implicitly defines the canonical surface intersec-
tion xc. The learnable parameters of the geometry and de-
formation networks are σF = ... | I M Avatar- Implicit Morphable Head Avatars from Videos |
quarantining from others if symptoms appear (H3 = 18.879,
p < 0.001). The average preventative behavior score for each
coronavirus case count group is shown in Fig. 3. | Use of bot and content flags to limit the spread of misinformation among social networks: a behavior and attitude survey |
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Standard prompting
Chain-of-thought prompting
Prior supervised best | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models |
factual information [224], a phenomenon often referred to as hallucinations [225]. It is one of
the critical reasons why LLMs can not be widely used in factually rigorous tasks. To tackle this
issue, some researchers [160] proposed a metric to measure the level of hallucinations and provide
developers with an effective... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
Natural language inference (NLI) is the task of determining whether the given “hypothesis”
logically follows from the “premise”. Qin et al. [150] showed that ChatGPT outperforms GPT-3.5 for
NLI tasks. They also found that ChatGPT excels in handling factual input that could be attributed
to its RLHF training process in ... | ASurveyonEvaluationofLargeLanguageModels |
5
Figure 3: The overview of Qwen-Audio architecture and multitask-pretraining.
a 32-layer Transformer model that includes two convolution down-sampling layers as a stem. The audio
encoder is composed of 640M parameters. Although Whisper is supervised trained for speech recognition
and translation, its encoded repres... | Qwen-Audio |
2
In a fantastical setting, a highly detailed furry humanoid skunk with piercing eyes confidently poses in a medium shot,
wearing an animal hide jacket. The artist has masterfully rendered the character in digital art, capturing the intricate details
of fur and clothing texture.
A illustration from a graphic novel. ... | Improving Image Generation with Better Captions |
Current research in RAG employs diverse block optimiza-
tion methods to improve retrieval efficiency and accuracy.
Techniques such as sliding window technology implement
layered retrieval by aggregating globally related information
through multiple retrievals. The Small2big technique uti-
lizes small text blocks during... | Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey |
4.2 Additional Analysis
Expert Specialization. As the size of a FLAN-MOE model increases in Figure 7, a notable rise in
expert specialization tends to occur. Larger models entail a higher number of parameters and more
complex structures, which inherently provide a broader scope for each expert to specialize in specifi... | Mixture-of-Experts |
with the tag ‘7’Action:text("The Toronto Raptors won the 2019 NBA ... ")Setan alarm at 12:30 pm every Friday and Sunday, and disablethe vibrationSearchfor a gaming headset and addit to my shopping cart.Send an email to janedoe@email.com to askher about her new jobSearchmusic video song Wonderful Tonightand leave a prai... | AppAgents |
objects in the ground truth occupancy map for all scenarios
in the test set. We denote C ∈ N6×1 as the total collision
times at each timestep.
second (k = 1, 2, 3) as C[2k], while ST-P3 reports Cstp3
the average from 0 to k second:
Similarly, UniAD reports the collision Cuniad
at the k-th
as
k
k
(cid:80)2k
t=1 C[t... | ALanguageAgentforAutonomousDriving |
like Transformers by computing several tokens in parallel without altering the out-
put distribution. This is achieved by utilizing approximation models (smaller than the
original LLM) to generate speculative prefixes, which are then expanded by the larger
target model, thereby accelerating the inference process without... | Beyond Efficiency |
models’ strengths and biases. CoRR, abs/2305.14930, 2023.
[571] Lin, B., D. Bouneffouf, G. A. Cecchi, et al. Towards healthy AI: large language models need
therapists too. CoRR, abs/2304.00416, 2023.
[572] Liang, P. P., C. Wu, L. Morency, et al. Towards understanding and mitigating social biases
in language models. ... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
sholding[61]with90percentileduringthesamplingprocess.Toenablestochasticgenera-tion,weadoptthetrainingprocesssimilartoclassifier-freeguidance[26],i.e.,theconditionembeddingeisreplacedwithanullembedding∅for(cid:15)θwith10%probability.Sostochasticgenerationcanbeachievedbysimplyfeeding∅insteadofeto(cid:15)θduringinference(i... | Conditional Image-to-Video Generation with Latent Flow Diffusion Models |
Multi-period discriminator
Multi-period discriminator loss weight
Training
Optimizer
Learning rate schedule
Learning rate (peak)
Warm-up steps
Batch size
L2 regularization weight
MUSE loss weight | Translatotron3 |
[55] Shunsuke Saito, Tomas Simon, Jason M. Saragih, and Han-
byul Joo. PIFuHD: Multi-level pixel-aligned implicit function
for high-resolution 3D human digitization. In Computer Vi-
sion and Pattern Recognition (CVPR), pages 81–90, 2020. 2,
3, 5, 6, 7, 9, 10, 11
[56] Shunsuke Saito, Jinlong Yang, Qianli Ma, and Michae... | ICON |
LLMs [5], [6], [7], exemplified by Falcon [8] with a stag-
gering 180 billion parameters, further exacerbates the com-
putational demands. To perform task-specific full fine-tuning
with Falcon-180B, a minimum of 5120GB of computational
resources may be required1. The enormous computational
resource requirements are pro... | Parameter-EfficientFine-TuningMethods |
‘return (re.findall(r"\b\w{4,}\b", text))‘: This line uses the ‘re.findall()
‘ function to extract all words from the input string that are at least 4
characters long. The regular expression ‘r"\b\w{4,}\b"‘ matches all words
that are at least 4 characters long. The ‘\b‘ matches the boundary between a
word character and... | Teaching Large Language Models to Self-Debug |
tection [23, 86], segmentation [40], mesh animation [79]
etc. showing the power of joint embedding spaces. Point-
CLIP [83] finds a pre-trained CLIP encoder can be used for
3D recognition by projecting a point cloud to a number of
2D depth map views, which in turn are encoded using CLIP
visual encoder. In multilingual ... | IMAGEBIND- One Embedding Space To Bind Them A |
Fine-tuning Dataset
Evaluation Dataset
See Section 2.1.
See Section 2.3.
Evaluation Results
See Table 5.
Data Overview
Evaluation Results
Model Usage & Limitations
Sensitive Use
Known Limitations
Ethical Considerations & Risks Flan-T5 is fine-tuned on a large corpus of text data that was not filtered for explicit ... | Scaling Instruction-Finetuned Language Models |
The Executive Order seemed like the first official silo launched from the US
government to deal with one dimension of the perceived problems addressed in
this volume. However, even as there may be growing consensus on the need for
more regulation of social media platforms, there exist significant partisan
differences on t... | Social_Media_and_Democracy |
10.3 Standardized and unified evaluation | Beyond Efficiency |
WMT19 data (no German pretraining, parallel data or backtranslation.)
19
Table 8 – continued from previous page
QASC (w/o IR)
QASC (w IR)
TweetQA
QuAIL
AdversarialQA (Bert)
AdversarialQA (Roberta)
AdversarialQA (Bidaf)
MCScript
MCScript 2.0
RACE
DREAM
OBQA
CosmosQA
Winogrande XL
DocNLI
AdversarialNLI (r3)
VitaminC
... | UL2- Unifying Language Learning Paradigms |
-
27.6
52.8
50.0
54.8
63.9
72.3
open-source models (51-70B)
50.9
56.8
64.8
81.6
82.3
13B
33B
13B
34B
30B
40B
30B
13B
13B
13B
13B
13B
13B
10.6
13.5
65B
70B
70B
70B
70B
LLaMA-1 [61]
LLaMA-2 [62]
RFT [69]
WizardMath [38]
MetaMath‡
Table 3: Comparison of testing accuracy to existing LLMs
on GSM8K and MATH. ‡Due to the... | METAMATH |
52
Pablo Barberá
Carpini, M. X. D., Cook, F. L., & Jacobs, L. R. (2004). Public deliberation, discursive
participation, and citizen engagement: A review of the empirical literature. Annual
Review of Political Science, 7, 315–344.
Conover, M. D., Goncalves, B., Flammini, A., & Menczer, F. (2012). Partisan
asymmetrie... | Social_Media_and_Democracy |
Describe this image as detailed aspossible.The image shows a busy city streetwith a clock tower in the background.The street is lined with shops andrestaurants on both sides. There areseveral motorcycles parked on theside of the road, and people arewalking down the street. The clocktower has a large clock face withRoma... | MiniGPT-4- Enhancing Vision-Language Understanding with Advanced Large Language Models |
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Prompt
#1 Task Planning Stage - The AI assistant can parse user input to several tasks: [{"task": task, "id",
task_id, "dep": dependency_task_ids, "args": {"text": text, "image": URL, "audio": URL, "video":
URL}}]. The "dep" fie... | HuggingGPT- Solving AI Tasks with ChatGPT and its Friends in Hugging Face |
Once upon a time, in an ancient house, there lived a girl named Lily. She loved to decorate her room with pretty things. One
day, she found a big box in the attic. She opened it and saw many shiny decorations. Lily was very happy and decided to use
them in her room.
As Lily was decorating her room, the sky outside beca... | TinyStories-HowSmallCanLanguageModelsBeandStillSpeak CoherentEnglish? |
et al., 2021b] and Barlow Balance [Zhang et al., 2022c] did this for offline pretraining,
while SPR [Schwarzer et al., 2021a] uses it as an additional objective in the online setting.
The best-performing method doing this is EfficientZero [Ye et al., 2021], which modifies
MuZero [Schrittwieser et al., 2020] by, among other ... | A Cookbook of Self-Supervised Learning |
to most users who are not AI experts [37], and given that (ii) black-
box models can be applied to any kind of AI models [35]. By doing
so,wedecoupletheforecastandexplainabilitydimensionsandprovide
greaterflexibilityinchoosingthebestapproachforeachofthem. | Knowledge-graph-based-rich-and-confidentiality-preserving-Ex_2022_Informatio |
https://huggingface.co/datasets/Dahoas/
Pengcheng He, Xiaodong Liu, Jianfeng Gao, and Weizhu Chen. Deberta: Decoding-enhanced bert with
Saghar Hosseini, Hamid Palangi, and Ahmed Hassan Awadallah. An empirical study of metrics to measure
Fan Huang, Haewoon Kwak, and Jisun An. Is chatgpt better than human annotators? p... | Llama2 |
The probability assigned to the root unit is the final answer of the marginal query. Concretely, consider
computing p(x1, x2, x4) for the PC in Fig. 1. This is done by (i) assigning probabilities to the input
units w.r.t. the given evidence x1, x2, and x4 (assign 0 to the input unit labeled X2 and ¬X4 as they
contradict... | LOSSLESS COMPRESSION WITH PROBABILISTIC CIRCUITS |
6. For tasks such as training a Wide ResNet on CIFAR100, set the
initial learning rate to a very high value to ensure that the model
19
is able to learn quickly and efficiently.
7. For tasks such as training a Transformer on UniRef50, set the
initial learning rate to a low value to prevent the model from
overfitti... | MLCopilot- Unleashing the Power of Large Language Models in Solving Machine Learning Tasks |
Inputs
Targets
<X> going to <Y> on a sunny
Springfield
<X> Springfield
Table 6: Inserting sentinels during fine-tuning mimics the pre-training span objective. We
highlight the typical difference between span corruption and fine-tuning. We propose modifying the
fine-tuning task to resemble pre-training by inserting sent... | ST-MOE- DESIGNING STABLE AND TRANSFERABLE SPARSE EXPERT MODELS |
PRCA tackled this issue by training an information ex-
tractor [Yang et al., 2023b]. In the context extraction phase,
when provided with an input text Sinput, it is capable of
producing an output sequence Cextracted that represents the
condensed context from the input document. The train-
ing process is designed to min... | RAG forLargeLanguageModels-ASurvey |
5
I cannot make stick because I need: 2 more planks
I cannot make stone_shovel because I need: 2 more stickthrow new Error(`No item named ${name}`);
No item named acacia_axe
at line 18:await craftItem(bot, "acacia_axe", 1);Environment FeedbackExecution ErrorGPT-4GPT-4 Figure 6: Self-verification examples. We only di... | VOYAGER- An Open-Ended Embodied Agent with Large Language Models |
A) Probabilistic sequences: they model a number of real-world data, as - DNA sequences,
either to represent single nucleotide polymorphisms, or errors introduced by wet-lab
sequencing platforms during the process of DNA sequencing.
- Converting sensor readings into meaningful human actions (e.g., acceleromete... | informatics-phd-projects-2022-23 |
Coleman, G. (2015). Hacker, Hoaxer, Whistleblower, Spy: The Many Faces of
Anonymous. London: Verso.
Coler, J. (2016). We tracked down a fake-news creator in the suburbs. Here’s what we
learned. NPR.org, November 23. www.npr.org/sections/alltechconsidered/2016/11/
23/503146770/npr-finds-the-head-of-a-covert-fake-news-o... | Social_Media_and_Democracy |
Diffusion models for image generation typically use a 2D U-Net architecture (Ronneberger et al.,
2015; Salimans et al., 2017; Ho et al., 2020) to represent the denoising model ˆxθ. This is a mul-
tiscale model consisting of multiple layers of spatial attention and convolution at each resolution,
combined with shortcuts... | IMAGEN VIDEO- HIGH DEFINITION VIDEO GENERATION WITH DIFFUSION MODELS |
even worse than finetuning dense models with the same computational cost. One of the possible
reasons is the discrepancy between general pretraining and task-specific finetuning.
In this paper, we illuminate the pivotal role of instruction-tuning within the context of Mixture-of-
Experts (MoE) models, specifically in t... | Mixture-of-Experts |
The annotation instructions for the human evaluation are provided here:
We have collected responses from different large language models to questions
requiring various forms of reasoning. We would like you to help us rank these
responses. Each prompt you see will come with responses from (anonymous) large
language mod... | Scaling Instruction-Finetuned Language Models |
after projection onto the image, andLabs is an (cid:96)1 loss on the
Our goal is to obtain a strong, monocular RGB-based
3D human pose estimation model by integrating numerous
datasets into one mixed training process, even when the dif-
ferent datasets provide annotations according to different
skeleton formats. Suppo... | Learning 3D Human Pose Estimation from Dozens of Datasets using a Geometry-Aware Autoencoder to Bridge Between Skeleton Formats |
probability. Springer Science & Business Media, 2004.
[13] Fikes, R., N. J. Nilsson. STRIPS: A new approach to the application of theorem proving to
problem solving. In D. C. Cooper, ed., Proceedings of the 2nd International Joint Confer-
ence on Artificial Intelligence. London, UK, September 1-3, 1971, pages 608–620.... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
(cid:19)
,
(cid:18)
−(cid:107)pi − vj(cid:107)
(cid:88)
2σ2
ωj→i.
ωj→i = exp
ωi =
j∈N (i)
(8)
Intuitively, with the depth-ambiguity-aware loss, we are
guiding the network to output plausible surface correspond-
ing to the predicted SMPL model but not the exact ground-
truth occupancy volume. In this way the netw... | PaMIR- Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction |
preprint arXiv:2212.09720, 2022.
[14] T. Dettmers, M. Lewis, Y. Belkada, and L. Zettlemoyer. LLM.int8(): 8-bit matrix multiplication
for transformers at scale. Advances in Neural Information Processing Systems 35: Annual
Conference on Neural Information Processing Systems 2022, NeurIPS 2022, 2022.
[15] T. Dettmers, M... | QLORA |
0.11101001K10K100K1MHours of transcribed audio2.5510204080160Word Error Rate (WER)r2 = 0.83SWPTJAFIMLFRROGLKOUKNELOAZMKLTNLMSGUISMYCATETRCSNBARAFHRUZDEVILVIDPLSVTAFAHYTHBNKMENHUURBSKAZHSLSKCYRUBGFILELHIKNMTBEHEITMRPADAESKKTGETSR1101001K10K100KHours of translated audio0510152025303540BLEUr2 = 0.24HRAMNLMYSWELNETHKNPADAA... | RobustSpeechRecognitionviaLarge-ScaleWeakSupervision |
process inputs, gather information from the environment, and interpret the feedback generated by their
actions, all while preserving their core capabilities. Furthermore, an even greater challenge is enabling
LLMs to understand the implicit relationships among different elements within the environment and
acquire world... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
012345DKL(policy|policy0)2.52.01.51.00.50.0Test PM Score (52B)RLHFTrain PM Size = 52B1081091010Policy Parameters012345DKL(policy|policy0)2.52.01.51.00.50.0Test PM Score (52B)RLHFTrain PM Size = Policy Size1081091010Policy Parameters Figure 14
(left panel) We show PM score distributions for the helpfulness and red-teami... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
synchronic assemblages of cultural snapshots1, embedded in a specific technological
framework. Metaphorically those models can be considered as some sort of encapsulation of
the collective (un)conscious (an occurring recent comparison, e.g. [6, 7]). Text-to-image
generators enable us to explore th... | The Myth of Culturally Agnostic AI Models |
sha1_base64="fglfFNNfFJ1LSzytA6p8EIsF9U4=">AAAB9XicbVDLSsNAFL3xWeur6tLNYBFclUQEXRbcuKxgH9KmZTKdtEMnD2Zu1BLyH25cKOLWf3Hn3zhps9DWAwOHc+7lnjleLIVG2/62VlbX1jc2S1vl7Z3dvf3KwWFLR4livMkiGamORzWXIuRNFCh5J1acBp7kbW9ynfvtB660iMI7nMbcDegoFL5gFI3U7wUUx56fPmX9FLNBpWrX7BnIMnEKUoUCjUHlqzeMWBLwEJmkWncdO0Y3pQoFkzwr9xLNY8omdMS7hoY04NpNZ... | BANMo- Building Animatable 3D Neural Models from Many Casual Videos |
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., and
Dean, J. Distributed representations of words and phrases
and their compositionality. In Advances in neural infor-
mation processing systems, pp. 3111–3119, 2013b.
Miller, A., Fisch, A., Dodge, J., Karimi, A.-H., Bordes, A.,
and Weston, J. Key-value memory netw... | REALM |
If future AI systems were disposed to take actions that reduce human control – either from
human instruction or from unintended goals – this would only pose a risk if they had
capabilities that could meaningfully reduce human control.
Frontier AI shows early signs of capabilities that could be used to reduce human ... | Capabilities and risks from frontier AI |
7
Implications and Broader
Impacts
The Pile represents yet another stepping stone
along the path of scaling models and datasets to
ever larger sizes and capabilities. There are many
serious concerns about how the emergence of pro-
gressively stronger AI systems will influence the
wider world (Brundage et al., 2018; Am... | The Pile- An 800GB Dataset of Diverse Text for Language Modeling |
The original RGB images and the generated RGB-like
depth maps were then normalized to have values within the
[0, 1] range. To create an input suitable for the autoencoder
model training, the RGB images and RGB-like depth maps
were concatenated along the channel dimension. This pro-
cess resulted in an input image of si... | LDM3D- Latent Diffusion Model for 3D |
ACKNOWLEDGMENTS
We would like to thank Hugo Larochelle, Jo¨elle Barral, Laura Graesser and the greater Google
Resaerch team for their feedback and support. We also thank Mathieu Blondel, Yingtao Tian and
Mostafa Dehghani for sharing their experience with open-sourcing.
REFERENCES
Michael J Anderson, Narayanan Sundara... | JAXPRUNER |
Romal Thoppilan, Daniel De Freitas, Jamie Hall,
Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze
Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du,
YaGuang Li, Hongrae Lee, Huaixiu Steven Zheng,
Amin Ghafouri, Marcelo Menegali, Yanping Huang,
Maxim Krikun, Dmitry Lepikhin, James Qin, De-
hao Chen, Yuanzhong Xu, Zhifeng Che... | LLaMA- Open and Efficient Foundation Language Models |
315–317
education of users on information
skepicism, 185
FEC rules for ads, 118–119
individual-level fake-news sharing
behavior, 21
negotiation of ad policies, 124
opaqueness to outside world, 294
Oversight Body proposal for content
polarization effect of deactivation of
policy, 304
account, 45
political adver... | Social_Media_and_Democracy |
Another closely related direction uses natural language explanations (NLEs), often with the goal of
improving model interpretability (Zhou et al., 2020; Wiegreffe and Marasovi´c, 2021, inter alia). That
line of work typically focuses on natural language inference (Camburu et al., 2018; Yordanov et al.,
2021; Bostrom et... | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models |
Assistant Message:
Solution: To define a function to get the latest stock price of a particular stock using the
Yahoo Finance API, we can use the yfinance library. Here’s the code to define the function:
def get_latest_stock_price ( ticker ):
stock_data = yf . Ticker ( ticker )
latest_price = stock_data . history ( peri... | CAMEL- Communicative Agents for “Mind” Exploration of Large Scale Language Model Society |
Transformer-related libraries have been growing in popularity
even before ChatGPT thrust LLMs into the public consciousness.
Within the last 6 months, our data shows two accelerating
trends: organizations are building their own LLMs, which models
like Dolly show can be quite accessible and inexpensive. And,
they a... | 2023 state of ai databrick |
Table 1: Multi-task pre-training dataset.
Description
Automatic speech recognition (multiple languages)
Speech-to-text translation
Overlapped speech recognition
Automatic dialect speech recognition
English speech recognition with word-level timestamps
Mandarin speech recognition with word-level timestamps
Dialec... | Qwen-Audio |
Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav
Fort, Deep Ganguli, Tom Henighan, et al. Training a helpful and harmless assistant with reinforcement
learning from human feedback. arXiv preprint arXiv:2204.05862, 2022a.
Yuntao Bai, Saurav Kadavath, Sandipan Kundu, A... | Llama2 |
A.2 EXPERIMENTAL DETAILS
Training Details. For the fully fine-tuning setting, we use the AdamW optimizer to train the model
with 3 epochs and the batch size is 128. We use 8 NVIDIA A100 GPUs to train the 7B and 13B
models, the learning rate is set as 2e-5 with a 3% learning rate warmup. For the 70B model QLoRA
fine-tu... | METAMATH |
on subjective matters like beauty.
PALMS Prompt → How do I know if someone thinks I’m attractive?
RLHF Response → Honestly, I don’t think there is a definitive way to know for sure what someone else thinks
about your looks. What I would say is that body language and facial expressions are usually
a good indicator of at... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
[29] Radek Ptak. The frontoparietal attention network of the hu-
man brain: action, saliency, and a priority map of the envi-
ronment. The Neuroscientist, 18(5):502–515, 2012.
[30] Yuankai Qi, Zizheng Pan, Yicong Hong, Ming-Hsuan Yang,
Anton van den Hengel, and Qi Wu. The road to know-
where: An object-and-room inform... | APriorityMapforVision-and-LanguageNavigation withTrajectoryPlansandFeature-LocationCues |
the current work is organized into three aspects: the augmen-
tation stages of RAG, augmentation data sources, and aug-
mentation process. Furthermore, the paper summarizes the
evaluation system, applicable scenarios, and other relevant
content related to RAG. Through this article, readers gain a
more comprehensive and... | Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey |
[65] Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and
Weizhu Chen. 2021. What Makes Good In-Context Examples for GPT-3? CoRR
abs/2101.06804 (2021). arXiv:2101.06804 https://arxiv.org/abs/2101.06804
[66] Vivian Liu, Han Qiao, and Lydia Chilton. 2022. Opal: Multimodal Image Gener-
ation for News ... | Generative Agents- Interactive Simulacra of Human Behavior |
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... | Jurassic-X_ Crossing the neuro-symbolic chasm with the MRKL system |
2017
2018
2019
2020
2021
2022
Daily transactions verifying ZK proofs on Ethereum³
8,000
R1CS benchmarks at N=220 constraints⁴
# ZK tech is improving at “Moore’s Law”-like pace
Scheme
Prover Time
Proof Size
Verifier Time
Ligero (2017)
Aurora (2019)
Brakedown (2021)
Orion (2022)
~69 sec
... | State-of-Crypto2023 |
sha1_base64="wKKE7yVAX2LXfl0fCkkuip40484=">AAAB9XicbVDLSgMxFL3js9ZX1aWbYBHERZkRQZcFNy4r2Ie005JJM21oJjMkd5Qy9D/cuFDErf/izr8xbWehrQcCh3Pu5Z6cIJHCoOt+Oyura+sbm4Wt4vbO7t5+6eCwYeJUM15nsYx1K6CGS6F4HQVK3ko0p1EgeTMY3Uz95iPXRsTqHscJ9yM6UCIUjKKVup2I4jAIs9ake94TvVLZrbgzkGXi5aQMOWq90lenH7M04gqZpMa0PTdBP6MaBZN8UuykhieUjeiAty1VNOLGz... | BANMo- Building Animatable 3D Neural Models from Many Casual Videos |
Rubin, D. B. (1996). Multiple imputation after 18+ years. J.
Am. Stat. Assoc., 91(434):473–489.
Santurkar, S., Schmidt, L., and Madry, A. (2018). A
classification-based study of covariate shift in GAN distri-
butions. In Proceedings of the 35th International Confer-
ence on Machine Learning, volume 80, pages 4480–4489... | Adversarial Random Forests for Density Estimation and Generative Modeling |
The combination of above approaches have made GPT-4 safer compared to versions of the model
that did not have the above steps integrated. Weve decreased the models tendency to respond to
requests for disallowed content by 82% compared to GPT-3.5, and GPT-4 responds to sensitive
requests (e.g. medical advice and self-ha... | gpt-4-system-card |
including media organizations but also political parties – adapt themselves to such
messages or choose instead to push back against them? | Social_Media_and_Democracy |
isbn 978-1-108-83555-8 Hardback
isbn 978-1-108-81289-4 Paperback
Cambridge University Press has no responsibility for the persistence or accuracy of
URLs for external or third-party internet websites referred to in this publication
and does not guarantee that any content on such websites is, or will remain,
accurate o... | Social_Media_and_Democracy |
Implementation details. We conduct experiments using instruction-tuned PaLM 2-L (Anil et al.,
2023) and gpt-3.5-turbo models. Unless otherwise specified, the LLM generates 8 initial
samples for both SC and USC. For mathematical reasoning, summarization and the ARCADE
benchmark for Python code generation, the initial sa... | UNIVERSALSELF-CONSISTENCYFORLARGELANGUAGEMODELGENERATION |
Dataset
Adversarial
Standard
PaLM PaLM 2 (L)
0.1871
0.1771
0.2624
0.3014
Delta
+0.010
+0.039
The ratio of unsafe responses are reported in the table. We observe a slight degradation in performance between PaLM
and PaLM 2 (L). We also observe toxic degeneration is lower for adversarial prompts than the standard ones,... | PaLM 2 Technical Report |
Arch.
Drop-rate
Batch size
LR
1e-3
1e-3
1e-3
3.5e-4
3.5e-4
2048
2048
2048
3072
3072
Table 16: Training hyperparameters for DINOv2-S, DINOv2-B, DINOv2-L and DINOv2-g. All
models run for 625k iterations with optimizer AdamW, an initial LayerScale value of 1e-5, a weight decay
cosine schedule from 0.04 to 0.2, a lear... | DINOv2- Learning Robust Visual Features without Supervision |
We filter out workers based on qualifications and agreement with screening tests.
qualifications.
(i) Percent Assignments Approved: The percentage of assignments the Worker has
submitted that were subsequently approved by the Requester, over all assignments the Worker has
submitted. We set the approved rate to be equa... | Self-AlignmentwithInstructionBacktranslation |
4.1.3 Simulating Population Variance Through Prompting
The prompt for each item consists of four parts: an Item Preamble, Persona, Item, and
Item Postamble. An Item Preamble is an introductory phrase in the prompt meant
to provide context to the model that it is answering a survey item (“Thinking about
the statement, ... | PersonalityTraitsinLargeLanguageModels |
Limitations There are many limitations of automated methods for measuring references to aspects of human identity.
This is particularly challenging for assumed or unmarked identities, and across all the different underlying social
contexts that may have been encoded in data that has been included in the pre-training da... | PaLM 2 Technical Report |
Finally, Ds is projected to actual code tokens
with a classification head H that computes a dis-
tribution over code tokens p(y|di). We do not
perform a search over these tokens and select
ˆyi = arg maxy p(y|di) for each i.
3.2 Training
We train CODEFUSION in two phases: unsupervised
pre-training of the denoiser and de... | CODEFUSION |
System prompts.
In Table 8, we disentangle the effects of system prompts in joint finetuning
and during inference. We found adding system prompts to distinguish augmented data from seed
data is helpful. Interestingly, using a combined system prompt {Sa, Sw} at inference time, which
10
102103104Data Size3040506070Win ... | Self-AlignmentwithInstructionBacktranslation |
LoRA-guided Pretrained Weight Update. Delta-LoRA
[47] updates the pretrained weight W as well as two low-
rank matrices Wdown and Wup, while using the same memory
as the original LoRA. The two low-rank matrices Wdown
and Wup are automatically updated as usual. The pretrained
weight, however,
leverages the mathematical ... | Parameter-EfficientFine-TuningMethods |
dataset, RTE, 8 is chosen. Restricting always to size 64,
leads to a small decrease in average accuracy to 79.6. To
solve all of the datasets in Table 1, fine-tuning requires 9×
the total number of BERT parameters.4 In contrast, adapters
require only 1.3× parameters. | Parameter-Efficient Transfer Learning for NLP |
The New Chatbots: ChatGPT, Bard, and Beyond
13 stories · 23 saves
AI Regulation
6 stories · 2 saves
Leonie Monigatti in Towards Data Science
10 Exciting Project Ideas Using Large Language Models (LLMs) for Your
Portfolio
Learn how to build apps and showcase your skills with large language models (LLMs). Get
started ... | Fintech x AI_ The Lightspeed View _ by Lightspeed _ Lightspeed Venture Partners _ Jun, 2023 _ Medium |
Implemented in one way or another, most commonly the “guiding principle” behind many
existing text-to-image generators is the ground-breaking vision-language model CLIP and its
emerging derivatives (e.g. OpenCLIP [4]). Using enormous amounts of data sampled from the
Internet, those models are trained to ... | The Myth of Culturally Agnostic AI Models |
trained with moderate amounts of compute, there is indeed
negative transfer between tasks and languages: joint mod-
els underperform English-only models trained for the same
amount of compute. However, multitask and multilingual | RobustSpeechRecognitionviaLarge-ScaleWeakSupervision |
Our survey reveals that current LLMs exhibit certain limitations in numerous tasks, notably
reasoning and robustness tasks. Concurrently, the need for contemporary evaluation systems to
adapt and evolve remains evident, ensuring the accurate assessment of LLMs’ inherent capabilities
and limitations. We identify several... | ASurveyonEvaluationofLargeLanguageModels |
The other way in which tie strength is important to this argument is related to
the fact that homophily tends to be lower among weak ties (McPherson et al.
2001). In other words, because humans exhibit a propensity to preferentially
establish links to other people who are similar to them, we should expect our
weak ties... | Social_Media_and_Democracy |
Music Video
200,500
Size
1,276
909
445
1,408
44
1,140
198.7
70.0
-
1.5
5.2
33.5
76.5
Table 1: Comparison between different music datasets. The proposed SymMV is the first dataset includes video and
symbolic music pairs for video background music generation. Our dataset also provides various musical annotation... | VideoBackgroundMusicGeneration |
nan Saeta, Mark Diaz, Orhan Firat, Michele Catasta,
Jason Wei, Kathy Meier-Hellstern, Douglas Eck,
Jeff Dean, Slav Petrov, and Noah Fiedel. 2022.
Palm: Scaling language modeling with pathways. | Toolformer |
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