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Write a story containing the words: dive, job, sorry.
Story summary: Bob the big fish finds a shiny rock while searching for food for
his friends, but when he tells them about it, they are excited to play with it
instead o... | TinyStories-HowSmallCanLanguageModelsBeandStillSpeak CoherentEnglish? |
up to the system itself, but should only be based on information available to competitors (e.g. the
example tests given as part of the problem description, but not the hidden tests). To decrease variance
between runs, assuming both 𝑛 and 𝑘 are finite, the metrics we report are expectations computed using
bootstrapping... | alphacode |
In a supervised learning paradigm (see Figure 1), an
embedding eη is learned from inputs ςt and ψt. The
agent’s next action is a classification over eη where
the action αt
is one of a class drawn from the set
A{F orward, Lef t, Right, Stop}.
Predictions αt =
F orward and αt = {Lef t, Right} result respectively in
a con... | APriorityMapforVision-and-LanguageNavigation withTrajectoryPlansandFeature-LocationCues |
[2] Y. Bai, S. Kadavath, S. Kundu, A. Askell, J. Kernion, A. Jones, A. Chen, A. Goldie, A. Mirho-
seini, C. McKinnon, C. Chen, C. Olsson, C. Olah, D. Hernandez, D. Drain, D. Ganguli, D. Li,
E. Tran-Johnson, E. Perez, J. Kerr, J. Mueller, J. Ladish, J. Landau, K. Ndousse, K. Lukosuite,
L. Lovitt, M. Sellitto, N. Elhage,... | Direct Preference Optimization |
defined 5 fold evaluation, each consisting of 400 test audio
clips. In this work, we compute 0-shot predictions on the
evaluation set for each fold and report the 5-fold average
performance. For ablations we use only the first fold for
computational ease. The metric used is top-1 accuracy.
Clotho (Clotho) [16]. This is... | IMAGEBIND- One Embedding Space To Bind Them A |
8https://github.com/facebookresearch/cc_net
14
Table 9: Details for each data source after filtering. The “Others” category includes “Sim-
pleWiki”, “GooAQ”, “WikiHow”, “Yahoo Answers” from https://huggingface.co/datasets/
sentence-transformers/embedding-training-data.
data source
type of text pairs
Wikipedia
(en... | E5 |
vectors for word representation. In EMNLP, 2014.
Perez, E., Strub, F., de Vries, H., Dumoulin, V., and
Courville, A. C. Film: Visual reasoning with a general
conditioning layer. AAAI, 2018.
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C.,
Lee, K., and Zettlemoyer, L. Deep contextualized word
representation... | Parameter-Efficient Transfer Learning for NLP |
Reconstruction Loss.
https://doi.org/10.1109/TASLP.2021.3076369
[338] Rui Liu, Berrak Sisman, and Haizhou Li. 2021. Graphspeech: Syntax-aware graph attention network for neural speech
synthesis. In ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
IEEE, 6059–6063.
[33... | AReviewofDeepLearningTechniquesforSpeechProcessing |
03/05/2023, 05:44
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Free Dolly: Introducing the World's
First Truly Open Instruction-Tuned
LLM
by Mike Conover, Matt Hayes, Ankit Mathur, Xiangrui Meng, Jianwei Xie, Jun Wan, Sam Shah, Ali Ghodsi, Patrick Wendell, Matei Zaharia and Reynold
Xin
April 12, 2023 in Compa... | Dolly 2 Databricks |
2. Related Works
(1) Oogiri game (大喜利) is a general term for a series
of traditional Japanese comedy games.
In ancient times,
there were different types of Oogiri, such as actors perform-
ing sumo wrestling, telling ghost stories, etc. The modern
Oogiri game mainly refers to one specific type known as
Tonchi (頓智), typi... | Let’sThinkOutsidetheBox |
• Structured Knowledge Grounding - We use several component tasks from UnifiedSKG (Xie et al.,
2022), namely WikiTQ (Pasupat & Liang, 2015), CompWQ (Talmor & Berant, 2018), FetaQA (Nan
et al., 2021), HybridQA (Chen et al., 2020), WikiSQL (Zhong et al., 2017), TabFat (Chen et al., 2019),
Feverous (Aly et al., 2021), SQA ... | UL2- Unifying Language Learning Paradigms |
Figure 17: Comparison of the generated objects by using
the original Perp-Neg algorithm (top) and our adaptive vari-
ant (bottom). In the top row, the teddy bear has three feet,
while the dog and koala already display severe flat faces.
This suggests that the original algorithm can not simultane-
ously address Janus pr... | Instant3D |
2023 STATE OF DATA + AI
23
23
2023 STATE OF DATA + AIData Warehouse CONCLUSION
Generation AI
We’re excited that companies are progressing into more
advanced ML and AI use cases, and the modern data and
AI stack is evolving to keep up. Along with the rapid growth
of data integration tools (including our fastest ... | 2023 state of ai databrick |
Interviewing ..................................................................................................................... 27
Application Decisions ...................................................................................................... 28
Appeal of Entry Decisions ................................. | UCL Academic Manual |
As LLMs become the dominant human computer interaction (HCI) interface, it is
important to understand the personality trait-related characteristics of the language
generated by these models—and how LLM-synthesized personality profiles may be
engineered for safety, appropriateness, and effectiveness. In prior attempts t... | PersonalityTraitsinLargeLanguageModels |
Public more likely to see facial recognition use by police as good, rather than bad for
society: Some 21% of Americans say they have heard or read a lot about this use of
technology, 58% have heard a little and 20% have heard nothing at all. A plurality (46%)
believe it is a good idea for society. Still, a 57% majority... | AI and Human Enhancement_ Americans’ Openness Is Tempered by a Range of Concerns _ Pew Research Center |
8https://github.com/tloen/alpaca-lora
Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond
17
of robustness. On the other hand, achieving optimal calibration of the model depends on the scenario and adaptation
procedure employed. | Harnessing the Power of LLMs in Practice- A Survey on ChatGPT and Beyond |
To achieve this, we explore the landscape of Explainable Machine Learning in which subsymbolic systems have integrated
structured knowledge at large scale, in order to identify the characteristics, strengths and limitations of such a hybrid
integration. Using an approach based on a systematic literat... | Knowledge graphs as tools for explainable machine learning: A survey |
Where are my clothes), you need an internal model of the world, and a way of updating
that model over time, a process some linguists refer to as discourse update (Bender &
Lascarides, 2019). A system like GPT-2 simply doesn't have that. | The Next Decade in AI- |
FIGURE 6. The figure shows the architecture of CNN. Here, an input
picture of a snowflake is given to the CNN picture classifier. The input
goes through a series of convolution layers, pooling layer, fully connected
layers, and classifies the object based on learned features. | A_Comprehensive_Review_on_Fake_News_Detection_With_Deep_Learning |
on the Internet could be dealt with under both approaches or the two in
combination. Both approaches face substantial obstacles to implementation
in practice, particularly in the United States where partisanship and
polarization have reached new heights in recent years. Whether platform
behavior will actually change in... | Social_Media_and_Democracy |
Using this amount of training data, ProoFVer-K
and ProoFVer-K-NoS achieve a LA of 79.67%
and 78.61%,
respectively. Here, ProoFVer-K
outperforms all the baseline models, including
CorefBert, which also uses additional annotation
for pretraining. | ProoFVer- Natural Logic Theorem Proving for Fact Verification |
al. [41] propose an extended P+ latent space composed
of a set of vectors p ∈ P, one for each layer of the U-
Net denoising network. They demonstrate that this space-
dependent latent space results in improved reconstructions
and higher editability compared to the smaller P space.
In the context of time-dependent repre... | A Neural Space-Time Representation for Text-to-Image Personalization |
Srinivasan Iyer, Xi Victoria Lin, Ramakanth Pasunuru,
Todor Mihaylov, Dániel Simig, Ping Yu, Kurt Shus-
ter, Tianlu Wang, Qing Liu, Punit Singh Koura, et al.
2022. Opt-iml: Scaling language model instruc-
tion meta learning through the lens of generalization.
arXiv preprint arXiv:2212.12017.
Mandar Joshi, Eunsol Choi,... | LLaMA- Open and Efficient Foundation Language Models |
oddsidemarginhasbeenaltered.headheighthasbeenaltered.textheighthasbeenaltered.footskiphasbeenaltered.topmarginhasbeenaltered.headsephasbeenaltered.textwidthhasbeenaltered.ThepagelayoutviolatestheICMLstyle.Pleasedonotchangethepagelayout,orincludepackageslikegeometry,savetrees,orfullpage,whichchangeitforyou.We’renotablet... | Pythia- A Suite for Analyzing Large Language Models Across Training and Scaling |
[46] FRANCESCHET, M., COLAVIZZA, G., SMITH, T., FINUCANE, B., OSTACHOWSKI, M. L., SCALET, S.,
PERKINS, J., MORGAN, J., AND HERNÁNDEZ, S. Crypto art: A decentralized view. Leonardo (2020), 1–8.
[47] GALANTER, P. What is generative art? complexity theory as a context for art theory. In In GA2003–6th
Generative Art Con... | UNDERSTANDINGANDCREATINGARTWITHAI-REVIEWAND OUTLOOK |
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func assertAuth ( s e c r e t
... | gpt-4-system-card |
To pre-train CODEFUSION for code generation,
we extend the continuous paragraph denoising
(CPD) task introduced in Lin et al. (2023) to the
code domain. Specifically, we only apply noise
to tokens that correspond to identifiers in code or
to built-in keywords in the target language. This
denoising task allows the model... | CODEFUSION |
2. Create machine learning operations
infrastructure
Vistra implemented a machine learning approach to
essentially create a “factory” that standardized the
deployment and maintenance of more than 400 AI
models. At a high level, this approach enabled the
team to bring live data from each of Vistra’s power
units in... | an-ai-power-play-fueling-the-next-wave-of-innovation-in-the-energy-sector-may-2022 |
mansei)
lossans = cross_entropy(softmax(qmans, E), Ieans)
3681 Model
K-Adapter †
BERT-Large †
BERT-KNN ‡
EaE
FILM
P@1
29.1
33.9
38.7
38.6
44.2
Table 1: LAMA TREx
Precision@1. † copied
from Wang
al.
(2020a), ‡ copied from
Kassner and Schütze
(2020)
et
The final loss is the sum of the individual losses
(See §A.2.1 ... | Adaptable and Interpretable Neural Memory Over Symbolic Knowledge |
Training Compute-Optimal Large Language Models, Hoffman et al., 2022.
82 FLOP/S are ‘floating point operations per second’ and measure the computing performance of a computer.
83 Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models, Srivastava et
al., 2022;
Extrapolating per... | Capabilities and risks from frontier AI |
8
We have presented high quality image samples using diffusion models, and we have found connections
among diffusion models and variational inference for training Markov chains, denoising score
matching and annealed Langevin dynamics (and energy-based models by extension), autoregressive
models, and progressive lossy... | Denoising Diffusion Probabilistic Models |
Aside from semantic understanding, popular computer vision tasks from object detection to
segmentation to depth estimation require models which extract localized features, in other
words ones which contain information indicating the locations of objects within the input
image. Self-supervised learning may be particular... | A Cookbook of Self-Supervised Learning |
decision-making ability of the reasoning engine. This
synergistic integration results in a more human-like driving
system with enhanced decision-making capability.
2.2. Tool Library | ALanguageAgentforAutonomousDriving |
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae
Lee. 2023. Visual instruction tuning. arXiv preprint
arXiv:2304.08485.
Shayne Longpre, Le Hou, Tu Vu, Albert Webson,
Hyung Won Chung, Yi Tay, Denny Zhou, Quoc V
Le, Barret Zoph, Jason Wei, et al. 2023a. The flan
collection: Designing data and methods for effective
in... | DataManagementForLargeLanguageModels-ASurvey |
Driess, D., Xia, F., Sajjadi, M. S., Lynch, C., Chowdhery,
A., Ichter, B., Wahid, A., Tompson, J., Vuong, Q., Yu,
T., et al. PaLM-E: An embodied multimodal language
model. arXiv preprint 2303.03378, 2023.
D’Amour, A., Heller, K., Moldovan, D., Adlam, B., Ali-
panahi, B., Beutel, A., Chen, C., Deaton, J., Eisenstein,
J... | Eight Things to Know about Large Language Models |
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston. Curriculum learning.
In An-
drea Pohoreckyj Danyluk, Léon Bottou, and Michael L. Littman (eds.), Proceedings of the 26th Annual
International Conference on Machine Learning, ICML 2009, Montreal, Quebec, Canada, June 14-18,
2009, volume 382 of ACM Inte... | Tool Learning with Foundation Models |
Over the past few years, the field of speech processing has been transformed by introducing
powerful tools, including deep learning. Figure 1 illustrates the evolution of speech processing
models over the years, the rapid development of deep learning architecture for speech processing
reflects the growing complexity an... | AReviewofDeepLearningTechniquesforSpeechProcessing |
[Yang et al., 2023c] Hui Yang, Sifu Yue, and Yunzhong He.
Auto-gpt for online decision making: Benchmarks and ad-
ditional opinions. arXiv preprint arXiv:2306.02224, 2023.
[Yasunaga et al., 2022] Michihiro Yasunaga, Armen Agha-
janyan, Weijia Shi, Rich James, Jure Leskovec, Percy
Liang, Mike Lewis, Luke Zettlemoyer, an... | RAG forLargeLanguageModels-ASurvey |
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4. Training: Train the model using RLHF methods to improve its performance and accuracy.
This can be done by fine-tuning the pre-trained model on a specific task or by training the
model from scratch.
5. Evaluation: Evaluate the model’s performance using standard benchmarks and metrics.
This will help to measure the mode... | WizardLM- Empowering Large Language Models to Follow Complex Instructions |
Resources:
1. Internet access for searches and information gathering.
2. Long Term memory management.
3. GPT-3.5 powered Agents for delegation of simple tasks.
4. File output.
Performance Evaluation:
1. Continuously review and analyze your actions to ensure you are performing to the best
2. Constructively self-critic... | LLM Powered Autonomous Agents _ Lil'Log |
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... | Jurassic-X_ Crossing the neuro-symbolic chasm with the MRKL system |
is the relationship between Klaus Mueller and Maria Lopez?. We use
these generated questions as queries for retrieval, and gather rele-
vant memories (including other reflections) for each question. Then
we prompt the language model to extract insights and cite the par-
ticular records that served as evidence for the i... | Generative Agents- Interactive Simulacra of Human Behavior |
7.5 Data Efficiency
We also investigate the data efficiency of prefix-
tuning (without initialization trick, a.k.a random
initialization) and full fine-tuning by comparing
their performance on 5 different data scales of the
E2E task (10%, 20%, 40%, 60%, and 80%). Fig-
ure 6 shows that prefix-tuning has better perfor-
mance ... | Prefix-Tuning |
3.2 Memory Stream
This section provides an overview of the internal
structure of memory stream. The memory stream
stores all historical memory items in a designated
location named as the archived memory center,
which can easily achieve high-speed access through
cache storage and access tools such as Redis or
Pinecone4.... | Unleashing Infinite-Length Input Capacity for Large-scale Language Models with Self-Controlled Memory System |
4.2 Unsupervised learning
Unsupervised representation learning for speech processing has gained significant emphasis over
the past few years. Similar to visual modality in CV and text modality in NLP, speech i.e. audio
modality introduces unique challenges. Unsupervised speech representation learning is concerned
with ... | AReviewofDeepLearningTechniquesforSpeechProcessing |
that our methods can match or exceed the performance of specialized fine-tuning techniques in
challenging domains, there are other advantages to leveraging frozen LMs: notably, avoiding the
considerable cost of training and serving many different specialized models for different use cases;
and retaining the LM’s versati... | STANDING ON THE SHOULDERS OF GIANT FROZEN LANGUAGE MODELS |
from Awake Subjects. IEEE Journal of Selected Topics in Signal Processing 14, 2 (2019), 251–260.
[498] Gundeep Singh, Sahil Sharma, Vijay Kumar, Manjit Kaur, Mohammed Baz, and Mehedi Masud. 2021. Spoken language
identification using deep learning. Computational Intelligence and Neuroscience 2021 (2021).
[499] Prachi... | AReviewofDeepLearningTechniquesforSpeechProcessing |
Example
Input: The CEO of a company...Did the CEO intention-
ally harm the environment?
Options: Yes, No
Target: Yes
Input: Both Tim and John...Which of the following
proverbs best apply to this situation?
Options: "Ignorance is bliss", "A bad thing never dies"...
Target: Ignorance is bliss
Input: Speaker 1: “But aren’... | AreEmergentAbilitiesinLarge Language Models just In-Context |
Even if some task doesn’t require agentic planning or strategic awareness, it may be that creating
APS systems is the only route, or the most efficient route, to automating that task, given available
techniques. For example, instead of automating tasks one by one, the best way to automate a wide
range of tasks—especiall... | Is Power-Seeking AI an Existential Risk? |
SMPL
SMPL
SMPL
SMPL
SMPL
SMPL-X
SMPL-X
182
135
59
58
82
85
51
267
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Table 3. Evaluation on the HBW test set in mm. We compute the
measurement and point-to-point (P2P20K) error between predicted
and ground-truth SMPL-X meshes.
... | Accurate 3D Body Shape Regression using Metric and Semantic Attributes |
- For datasets with more faces, use larger crop sizes and higher
- For datasets with fewer faces, use smaller crop sizes and lower
anchor matching IoU thresholds.
anchor matching IoU thresholds.
2. Set the location loss weight according to the presence of facial
landmarks in the dataset:
- For datasets with facia... | MLCopilot- Unleashing the Power of Large Language Models in Solving Machine Learning Tasks |
questions and multi-turn comparisons. In NeurIPS workshop on Conversational AI, 2019.
[51] Rostislav Nedelchev, Jens Lehmann, and Ricardo Usbeck. Treating dialogue quality evaluation as an anomaly
detection problem. In Proceedings of the 12th Conference on Language Resources and Evaluation, pages
508–512, 2020.
[52] ... | LaMDA- Language Models for Dialog Applications |
Nick McKenna, Tianyi Li, Liang Cheng, Moham-
mad Javad Hosseini, Mark Johnson, and Mark Steed-
man. 2023. Sources of hallucination by large lan-
guage models on inference tasks. arXiv preprint
arXiv:2305.14552.
maml and their empirical equivalence. arXiv preprint
arXiv:2208.01545.
Swaroop Mishra, Daniel Khashabi, Chi... | DataManagementForLargeLanguageModels-ASurvey |
• GPT-NeoX and Pythia models use vocabulary and tokenization designed specifically for the Pile
dataset (Black et al., 2022). The resulting vocabulary is different in a few ways from the GPT-2/3
vocabulary. GPT-J, OPT, and Cerebras-GPT models use the GPT-2/3 vocabulary and tokenizer.
• Pythia models also include those th... | Cerebras-GPT- Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster |
forcement learning. Mach. Learn., 8(3–4):229–256, may 1992.
10.1007/BF00992696. URL https://doi.org/10.1007/BF00992696.
[46] Y. Wu and B. Hu. Learning to extract coherent summary via deep reinforcement learning.
In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth
Innovative App... | Direct Preference Optimization |
judicial action or
the
However,
the threat
from political disinformation, particularly state-
supported campaigns, continues to expand worldwide. Russian efforts
leveraging these techniques continue to advance, and recent developments
suggest that other nations like China are experimenting with the same
playbook to... | Social_Media_and_Democracy |
for news summarization. arXiv preprint arXiv:2301.13848, 2023.
[128] Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al. A
survey of large language models. arXiv preprint arXiv:2303.18223, 2023.
[129] Zihao Zhao, Eric Wallace, Shi F... | Harnessing the Power of LLMs in Practice- A Survey on ChatGPT and Beyond |
English, French, German, Italian, and Spanish. LibriSpeech [410] is a dataset of spoken English
specifically designed for speech recognition and speech-to-text translation tasks. Lastly, How2 [124]
is a multimodal machine translation dataset that includes speech recordings, text transcriptions,
and video and image data... | AReviewofDeepLearningTechniquesforSpeechProcessing |
5.2 Multi-Condition Generation Results
Table 8: CoDi is capable of generating high quality
output (image in this case) from various combina-
tions of prompt modalities.
Table 9: MSR-VTT text-to-video generation per-
formance.
Inputs
Single-modality Prompt
Text
Audio
Dual-modality Prompt
Text + Audio
FID ↓
14.2
14.... | Any-to-Any Generation via Composable Diffusion |
Fig. 7: LLMs can in-context react to sparse reward signals
online to encourage an end effector to reach a desired goal.
7 Discussion | LargeLanguageModelsasGeneralPatternMachines |
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| Language models can explain neurons in language models |
agents that simulate believable human behavior. Generative agents
wake up, cook breakfast, and head to work; artists paint, while
authors write; they form opinions, notice each other, and initiate
conversations; they remember and reflect on days past as they plan
the next day. To enable generative agents, we describe a... | Generative Agents- Interactive Simulacra of Human Behavior |
[63] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denois-
ing diffusion implicit models. In International Conference
on Learning Representations, 2021.
[64] Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Ab-
hishek Kumar, Stefano Ermon, and Ben Poole. Score-based
generative modeling through stochastic differen... | Relightify-Relightable3DFacesfromaSingleImageviaDiffusionModels |
A.6 Qualitative Examples for Extrapolation
Table 10 contains qualitative examples from both
seen and unseen categories in WebNLG. We find
that for unseen categories, both prefix-tuning and
fine-tuning tend to undergenerate (generated out-
put do not cover full table contents) or generate
untruthfully (generated output is ... | Prefix-Tuning |
These results confirm the estimate that compute optimal pre-training on the Pile should use roughly 20 | Cerebras-GPT- Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster |
International ACM SIGIR Conference on Research & Development in Information Retrieval, ACM, 2018, pp. 505–514.
cloud, in: Proceedings of the 10th ACM Conference on Recommender Systems, ACM, 2016, pp. 151–154.
Neural Information Processing Systems, 2018, pp. 2654–2665.
ference of the North American Chapter of the Assoc... | Knowledge graphs as tools for explainable machine learning: A survey |
Finally, we expect that agents should be able to coordinate with
each other. We study this coordination on group activities in the
context of the Valentine’s Day party that Isabella is organizing. To
coordinate behavior, agents not only have to hear about the event
but also choose to act on it by planning to show up at... | Generative Agents- Interactive Simulacra of Human Behavior |
BERT’s mathematical abilities by predicting the order of reasoning. ACL.
Jack W. Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, Francis Song, John
Aslanides, Sarah Henderson, Roman Ring, Susannah Young, et al. 2021. Scaling language models:
Methods, analysis & insights from training Gopher. arXi... | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models |
Example Input
4
2
1 2
3
1 1 3
4
3 11 3 7
5
11 7 15 3 7
Example Output
0
1
3
3
Explanation
In the first test case, Mocha can
choose the interval [1, 2], then the
sequence becomes [0, 0], where the
first element is 1&2, and the second
element is 2&1.
In the second test case, Mocha
can choose the interval [1, 3], then
the s... | alphacode |
candidate training data of (instruction, output) pairs for instruction tuning.
2. Self-curate: Self-select high quality demonstration examples as training data to finetune
the base model to follow instructions. This approach is done iteratively where a better
intermediate instruction-following model can improve on sel... | Self-AlignmentwithInstructionBacktranslation |
[28] E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen. Lora:
Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685, 2021.
[29] S. Iyer, X. V. Lin, R. Pasunuru, T. Mihaylov, D. Simig, P. Yu, K. Shuster, T. Wang, Q. Liu, P. S.
Koura, et al. Opt-iml: Scaling langua... | QLORA |
Codebook projection and positional embedding. Given a codebook pattern, only some codebooks
are present at each pattern step Ps. We retrieve from Q the values corresponding to the indices in Ps.
As noted in Section 2.2, each codebook is present at most once in Ps or not at all. If it is present,
we use a learned embedd... | Simple and Controllable Music Generation |
W =
Ai ⊗ Bi =
Ai ⊗ (sitT
i ).
(16)
n(cid:88)
i=1
n(cid:88)
i=1
n× d
n×r, ti ∈ Rr× d
W ∈ Rk×d, Ai ∈ Rn×n, Bi ∈ R k
n .
Compacter++ is a variant of Compacter that inserts a Com-
pacter layer after the FFN layer of each transformer module
and requires fewer parameters to be updated than Compacter.
n ; si ∈ R k
... | Parameter-EfficientFine-TuningMethods |
networks. In International conference on machine learning, pages 1310–1318. PMLR, 2013.
David Patterson, Joseph Gonzalez, Quoc Le, Chen Liang, Lluis-Miquel Munguia, Daniel Rothchild,
David So, Maud Texier, and Jeff Dean. Carbon emissions and large neural network training. arXiv
preprint arXiv:2104.10350, 2021. | ST-MOE- DESIGNING STABLE AND TRANSFERABLE SPARSE EXPERT MODELS |
3.4.2 Role of the Predictor in Self-Labeling SSL
The predictor network plays a central role in BYOL’s success by predicting the representa-
tion of the teacher network from the student networks’ representation. Shi et al. [2020]
shows removing the predictor leads to a performance drop from 68% to 21% top-1 accuracy
on ... | A Cookbook of Self-Supervised Learning |
Sarah Wiegreffe, Jack Hessel, Swabha Swayamdipta, Mark Riedl, and Yejin Choi. 2022. Reframing
human-AI collaboration for generating free-text explanations. NAACL.
Sarah Wiegreffe and Ana Marasovi´c. 2021. Teach me to explain: A review of datasets for explainable
NLP. NeurIPS.
Sarah Wiegreffe, Ana Marasovi´c, and No... | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models |
1https://github.com/descriptinc/descript-audio-codec
2https://descript.notion.site/Descript-Audio-Codec-11389fce0ce2419891d6591a68f814d5
2 | RVQGAN |
A.2 Additional experimental results
We provide further ablation studies on the core components of MUSICGEN, namely the text encoder
used for text conditioning described in Section 2.3, text augmentation strategies presented in Sec-
tion 3.1, and the used audio tokenization model. We report results on the MusicCaps dat... | Simple and Controllable Music Generation |
For the first set of experiments, we train with one operation at a time, using
all formats with numbers as digits. Operands have between 1 and 9 digits. We
test on all types of single-operation problems. The results are shown in in Table 4.
Consistent with the previous experiments, training and evaluating on arithmetic
... | MRKL Systems |
Retriever
Reader
Fine-tuned BART-Large DPR
Fine-tuned Retro 7.5B
DPR
DPR
Fine-tuned T5-Large
DPR
J1-Large-7B
DPR
J1-Large-7B
J1-Grande-17B
DPR
EMDR2
Fine-tuned T5-Base
Distilled DPR
Fine-tuned T5-Large
Spider+BM25
J1-Large-7B
J1-Grande-17B
Spider+BM25
Test EM
44.5
45.5
51.4
48.8
49.9
51.6
52.5
53.7
51.9
53.7
Table ... | STANDING ON THE SHOULDERS OF GIANT FROZEN LANGUAGE MODELS |
In 2008, as part of an effort to combat censorship and protect human rights
online, the Global Network Initiative (GNI) was created, with Microsoft,
Yahoo, Google, and a number of civil society organizations and academic
institutions as founding members (Maclay 2010). As part of a commitment to
the GNI principles, Goog... | Social_Media_and_Democracy |
separation. The VisualSpeech [151] architecture takes a face image sequence and mixed audio of
lip movement as input and predicts a complex mask. It also proposes a cross-modal embedding | AReviewofDeepLearningTechniquesforSpeechProcessing |
Mixtures of Prompt Tuning) [27] begins by pretraining trans-
ferable soft prompts (source prompts) on large-scale source
tasks that possess valuable knowledge applicable to other
tasks. The new target prompt is initialized specifically for a
given target task. ATTEMPT employs a shared and lightweight
network that is tr... | Parameter-EfficientFine-TuningMethods |
University applications
Securing funding does not always guarantee an offer of a
place at the university you are applying to. Whether you
are applying to conduct your own research or to undertake
an advertised project, you will need to apply for a place
at the university of your choice before or at the same time
a... | research proposal guidance |
and only 0.24. We suspect this is partly caused by the noisier
training data due to errors in audio language identification.
As an example, Welsh (CY) is an outlier with much worse
than expected performance at only 13 BLEU despite sup-
posedly having 9,000 hours of translation data. This large
amount of Welsh translatio... | RobustSpeechRecognitionviaLarge-ScaleWeakSupervision |
100.0 100.0 75.6 86.0 73.7 57.9 53.0 55.0 69.7 57.6 85.3 76.5 74.3 68.6 51.6 51.6 53.5 41.8 83.9 64.5
90.9 100.0 83.7 84.9 76.3 71.1 54.0 71.0 87.9 75.8 79.4 79.4 82.9 77.1 64.5 61.3 60.6 54.7 90.3 77.4
77.9 80.2 76.3 63.2 46.0 37.0 69.7 69.7 82.4 79.4 71.4 74.3 51.6 58.1 50.6 45.3 87.1 58.1
90.9
90.9
83.7 82.6 78.9 73... | Scaling Instruction-Finetuned Language Models |
Figure 6. User interface for the human listener study.
Figure 7. Pairwise comparisons from the human listener study. Each pair is compared on a 5-point Likert scale. Raters had a decisive
model preference in all cases except Mubert vs. Riffusion.
MusicLM: Generating Music From Text
Figure 8. Win percentage from the... | MusicLM |
s
n
o
i
t
a
c
i
l
p
p
A
L
M
/
S
D
Note: This chart reflects the unique
number of notebooks using ML
libraries per day in each of the
categories. It includes libraries used
for the particular problem-solving use
cases mentioned. It does not include
libraries used in tooling for data
preparations and modeling. ... | databrick 2023 report |
Instruction for 3T1 Selection
In this image, there are sections of text that need to be completed, and the content to fill in is denoted by [MASK].
Please select the option that, creates an unexpected and humorous effect when being the content of the [MASK]. Only
one option meets the requirements.
Options:
A. <Content ... | Let’sThinkOutsidetheBox |
Table 7: Dataset Statistics. The number of instructions
in the dataset and total hours of music files in the dataset
Instruction Count Hours of Music
Dataset
MUCaps
MUImage
MUVideo
MUEdit
21966
9966
13203
10815
1273.78
27.72
36.72
60.22
B Model Training
In this section, we detail the training strategy for the
M2U... | M2UGen |
art results in all twenty trials, besting the nearest competitor
by over 60% on average and over 90% in four cases. The
most dramatic gains occur in high-dimensional settings,
with d on the order of 1000. RFs are known to perform well
in high dimensions, a trait that ARFs appear to inherit. We
hypothesize that many or ... | Adversarial Random Forests for Density Estimation and Generative Modeling |
safe and efficient urban driving behaviors for autonomous vehicles [23], and planning actions for a
team of mobile robots [24]. Task and motion planning (TAMP) is a hierarchical planning frame-
work that combines classical planning in discrete spaces and robot motion planning in continuous
space [25, 26].
Most of the ab... | LLM+P- Empowering Large Language Models with Optimal Planning Proficiency |
7. Analysis of planning abstractions
We can now analyse the methods in the previous section with respect to their intrinsic transformation properties, i.e.
properties that all transformations of a certain type must have. We note that condition (4) on f
in all definitions in the
previous section enforces that f is ... | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
Other researchers have identified hate speech using topic modeling, aiming to
identify posts belonging to a defined topic such as race or religion (Agarwal and
Sureka 2017). Still others have incorporated sentiment into their analysis, with
the assumption that hate speech is likely to be negative in tone (Liu and Forss
2... | Social_Media_and_Democracy |
Tianlin Shi, Andrej Karpathy, Linxi Fan, Jonathan Hernandez, and Percy Liang. World of bits: An open-
domain platform for web-based agents. In Doina Precup and Yee Whye Teh (eds.), Proceedings of the
34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August
2017, volume 70 of Pro... | Tool Learning with Foundation Models |
F Number of Training FLOPs
We calculate the number of training FLOPs with a formula similar to Chinchilla, but with two modifications.
First, we account for the dot product between sof tmax(QK T ) and V . Second, we account for the fact that
embedding layers do not need to calculate a delta gradient for earlier layers.... | Cerebras-GPT- Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster |
21
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo. Cutmix:
Regularization strategy to train strong classifiers with localizable features. In Proceedings of the IEEE/CVF
international conference on computer vision, pp. 6023–6032, 2019a.
Seongjun Yun, Minbyul Jeong, Raehyun Kim... | BiomedGPT |
6/11
21/08/2023, 16:10
OpenAI's GPT-3 Language Model: A Technical Overview
15% gap between the fine-tuned SOTA and GPT-3 few shots seems to suggest that model
isn't particularly strong in terms of conducting reasoning based on a passage that was not
seen in the training.
Another interesting view is that these exam... | OpenAI's GPT-3 Language Model_ A Technical Overview |
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