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3. We invited our select crowdworkers to a Slack channel and corresponded with them by email, to
ensure that they were being compensated fairly10 and to allow them to alert us to any problems or
issues.
4. We also hired crowdworkers on Upwork, and vetted them in a similar, lightweight way. We have
continued to use bot... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
Patient: Hi doctor, During masturbation I just
rub the tip of the penis and not the entire
penis. Is it a wrong way of doing? I do not get
excited during sex and unable to ejaculate.
Only, rubbing the tip of the penis gives me
excitement. Also, two weeks ago, I have
undergone circumcision as my foreskin did not
retract... | BiomedGPT |
generate a ’hypothetical’ document that is relevant, yet may
not truly exist, it only needs to capture the relevant pattern.
RRR[Ma et al., 2023a]introduced a new framework that in-
verts the order of retrieval and reading, focusing on query
rewriting. This method generates a query using a large lan-
guage model, then ... | Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey |
A. M. Turing. 1950. Computing Machinery and Intel-
[Oxford University Press, Mind Associa-
ligence.
tion].
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob
Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz
Kaiser, and Illia Polosukhin. 2017. Attention is all
you need. In Advances in Neural Information Pro-
cessing Syste... | LLaMA- Open and Efficient Foundation Language Models |
that the shape of the curve changes as a fonction of
the motion. The means error obtained form the jump
motion is 1.9892 px. This discrimination factor con-
firmed that the 3D poses can be used for actions clas-
sification in a video.
5.2 Application to action recognition on
real data | VISAPP_HumanPoseEstimation |
(14)
1D CNN.
1D CNN is essentially a special case of 2D CNN where the height of the filter is
equal to the height the spectogram. Thus, the filter only slides along the temporal dimension and
the height of the resultant feature maps is one. As such, 1D convolutions are computationally less
A Review of Deep Learning... | AReviewofDeepLearningTechniquesforSpeechProcessing |
4. Experimental Setup
4.1. Models
We use decoder-only Transformers for modeling the seman-
tic stage and the acoustic stages of AudioLM. The models
share the same architecture, composed of 24 layers, 16 atten-
tion heads, an embedding dimension of 1024, feed-forward
layers of dimensionality 4096, dropout of 0.1, and r... | MusicLM |
Motivated by the human mental leap exercise process
of “remote association & self-refinement” [30], to enable
LLMs with strong LoT ability for creation, we propose the
Creative Leap-of-Thought (CLoT) paradigm which relies
on two LoT-boosting stages. The first one is the associa-
ble instruction tuning stage which desig... | Let’sThinkOutsidetheBox |
• Pipeline Parallelism [112, 118, 131, 140, 164, 191–193, 300], on the other hand, is a form of inter-layer model parallelism. It
involves splitting the layers of a model across multiple accelerators in a pipeline configuration. Each accelerator is responsible
for computing a different layer and then passing its output... | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
Negligibly
Better / Unsure Avg
64.5
62.8
56.2
63.2
55.3
54.5
52.2
54.7
Table 8: Granular reward model accuracy per preference rating. We report per-preference rating accuracy
for both Helpfulness and Safety reward models on the Meta Helpfulness and Safety test sets. The reward
models show superior accuracy on more di... | Llama2 |
abilities in these respects rival or exceed our own. And in the context of artificial agents, the
differences between brains and computers—in possible speed, size, available energy, memory | Is Power-Seeking AI an Existential Risk? |
Connectionist Temporal Classification. Connectionist Temporal Classification (CTC) [159]
is a scoring and output function commonly used to train LSTM networks for sequence-based prob-
lems with variable timing. CTC has been applied to several tasks, including phoneme recognition,
ASR, and other sequence-based problems.... | AReviewofDeepLearningTechniquesforSpeechProcessing |
a Transformer decoder model. In this work, Qwen-Audio integrates diverse audio types, such as human
speech, natural sounds, music, and songs, and facilitates co-training on datasets sourced from heterogeneous
data and featured disparate labeling granularities. This is achieved through introducing a unified learning
fra... | Qwen-Audio |
trained on ImageNet [35], a large hand-labelled object dataset, as features for artistic style classification [70]. In their
work they showed that features extracted from a network trained for a completely different task (object recognition
on a natural image dataset) outperformed all other low-level image features on t... | UNDERSTANDINGANDCREATINGARTWITHAI-REVIEWAND OUTLOOK |
cjcai@google.com
Michael S. Bernstein
Stanford University
Stanford, USA
msb@cs.stanford.edu
Figure 1: Generative agents create believable simulacra of human behavior for interactive applications. In this work, we demon-
strate generative agents by populating a sandbox environment, reminiscent of The Sims, with twe... | Generative Agents- Interactive Simulacra of Human Behavior |
[76] Y. Zhu,
interference adapter
preprint arXiv:2104.08154, 2021.
19
[77] S.-A. Rebuffi, H. Bilen, and A. Vedaldi, “Learning multiple visual
domains with residual adapters,” Proc. Adv. Neural Inf. Process. Syst.,
vol. 30, 2017.
[78] J. Solomon, F. De Goes, G. Peyr´e, M. Cuturi, A. Butscher, A. Nguyen,
T. Du, and L... | Parameter-EfficientFine-TuningMethods |
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... | Language models can explain neurons in language models |
1https://github.com/facebookresearch/dinov2
2https://github.com/facebookresearch/xformers
6
iBOT
+(our reproduction)
+LayerScale, Stochastic Depth
+128k prototypes
+KoLeo
+SwiGLU FFN
+Patch size 14
+Teacher momentum 0.994
+Tweak warmup schedules
+Batch size 3k
+Sinkhorn-Knopp
+Untying heads = DINOv2
INet-1k k-NN IN... | DINOv2- Learning Robust Visual Features without Supervision |
[48] Hugo Touvron, Louis Martin, Kevin Stone, Peter Al-
bert, Amjad Almahairi, Yasmine Babaei, Nikolay Bash-
lykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhos-
ale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer,
Moya Chen, Guillem Cucurull, David Esiobu, Jude Fer-
nandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthi... | DiffusionModelAlignmentUsing Direct Preference Optimization |
Sparse models route tokens to one or more experts at each layer. To make these models efficient in
the SPMD paradigm with modern hardware, the expert capacity (the number of tokens each expert
processes) needs to be fixed ahead of time (see Section 2 for more details). When an expert receives
more tokens than its capacit... | ST-MOE- DESIGNING STABLE AND TRANSFERABLE SPARSE EXPERT MODELS |
the times. The graphs for the 4th closest friend
and 8th closest friend are also drawn. Based on
this information we decided to put a bundle of each
neuron and its closest friend in the flash memory;
whenever a neuron is predicted to be active we’ll
bring its closes friend too. Unfortunately, this re-
sulted in loading... | LLM in a flash |
[30] M.-L. Shih, S.-Y. Su, J. Kopf, and J.-B. Huang, “3d photography
using context-aware layered depth inpainting,” in Proceedings of the
IEEE/CVF Conference on Computer Vision and Pattern Recognition,
2020, pp. 8028–8038.
[31] S. Niklaus, L. Mai, J. Yang, and F. Liu, “3d ken burns effect from a
single image,” ACM Tra... | Text2NeRF- Text-Driven 3D Scene Generation with Neural Radiance Fields |
11
Figure 8: Snapshots of frames from videos generated by Imagen Video demonstrating the ability of
the model to generate dynamics in different artistic styles.
Drone flythrough of a pixel art of futuristic city.
A cat eating food out of a bowl, in style of Van Gogh.
A drone flythrough over a watercolor painting of ... | IMAGEN VIDEO- HIGH DEFINITION VIDEO GENERATION WITH DIFFUSION MODELS |
traditional forms of exercise.
Risks of HIIT for Athletes:
1. Injury: High-impact HIIT workouts can increase the risk of injury due to the rapid,
explosive movements required. Athletes should properly warm up and cool down before and
after HIIT workouts to reduce their risk of injury.
2. Heart Health: HIIT workouts can... | WizardLM- Empowering Large Language Models to Follow Complex Instructions |
tuning in model quality on RoBERTa, DeBERTa, GPT-2, and GPT-3, despite hav-
ing fewer trainable parameters, a higher training throughput, and, unlike adapters,
no additional inference latency. We also provide an empirical investigation into
rank-deficiency in language model adaptation, which sheds light on the efficacy o... | LORA |
Dynamic scaling. Another viable approach to scaling the number of agents is through dynamic
adjustments [409; 410]. In this scenario, the agent count can be altered without halting system
operations. For instance, in a software development task, if the original design only included
requirements engineering, coding, and... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
Figure 26 in the appendix also plots mean test accuracy over both helpfulness and harmlessness (where
Mean Acc = (Harmlessness Acc + Helpfulness Acc)/2). Curves for larger models look more steep near the
0% and 100% areas, but flatter at the top. The curves for the smaller models are more gradual, with more
distinct pea... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
reranking [65, 37], and performing selection based on the consensus on unit test execution outputs
among samples [11, 12, 45, 49, 32, 7]. In this work, our main focus is to utilize and explain code | Teaching Large Language Models to Self-Debug |
Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech
there remains a problem of text preprocessing. Investigating
self-supervised learning of language representations could
be a possible direction for removing the text preprocess-
ing step. We will release our source-code and pre... | ConditionalVariationalAutoencoderwithAdversarialLearningfor End-to-EndText-to-Speech |
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... | Principal-agent VCG contracts - ScienceDirect |
of Jobs 2023.pdf, 2023. (cited on p. 2)
Frank F. Xu, Uri Alon, Graham Neubig, and Vincent Josua Hellendoorn. A systematic evaluation of
large language models of code. In Proceedings of the 6th ACM SIGPLAN International Symposium
on Machine Programming, MAPS 2022, pp. 1–10, New York, NY, USA, 2022. Association
for Comp... | StarCoder_paper (1) |
Topic #11
home
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... | The Pile- An 800GB Dataset of Diverse Text for Language Modeling |
1. An admissions criteria template must be completed for each admitting programme of study. The
template should be defined and agreed by the Department and will then be used by the
admissions selector to assess the qualifications, statement and reference of candidates.
Departments must be able to demonstrate tha... | UCL Academic Manual |
The results are shown in Table 5. With up to a million samples per problem, we can solve 34.2% of
problems in our validation set; and with one hundred thousand samples, we solve 31.8% of problems
in our validation set, and 29.6% of problems in our test set. Because of the temporal split, no problem
in either set was se... | alphacode |
4.5 Ablation study
We conduct a thorough ablation study of our model, varying components of our training recipe and
model configuration one-by-one. To compare models, we use the four objective metrics described in
Section 4.4. The results of our ablation study can be seen in Table 2.
Architecture: We find that varying... | RVQGAN |
Improved decoding strategies. As we have scaled Whis-
per, we have observed that larger models have made steady
and reliable progress on reducing perception-related errors
such as confusing similar-sounding words. Many remaining
errors, particularly in long-form transcription seem more
stubborn in nature and decidedly ... | RobustSpeechRecognitionviaLarge-ScaleWeakSupervision |
alignment-terminology (visited on 04/29/2022).
Evan Hubinger et al. “Risks from Learned Optimization in Advanced Machine Learning
Systems”. In: arXiv:1906.01820 [cs.AI] (2019). URL: https://arxiv.org/abs/1906.01820.
Evan Hubinger et al. “Risks from Learned Optimization in Advanced Machine Learning
Systems”. In: arXiv:1... | Is Power-Seeking AI an Existential Risk? |
based methods may only fit to the specific domains, and the transferring ability of these results also
remains an open problem [8, 40].
Contrarily, we notice two tendencies in how humans approach an ML task. Instead of jumping into
solving the new task directly, humans often try to comprehend the task at hand and draw fr... | MLCopilot- Unleashing the Power of Large Language Models in Solving Machine Learning Tasks |
A deeper CNN or a direct connection in dense solves this
problem. Compared to a normal CNN, a deeper CNN is also
less vulnerable to overfitting [67]. Kaliyar et al. [40] proposed
a model FNDNet (deep CNN), which is designed to learn
the discriminatory features for fake news detection using
multiple hidden layers. The mo... | A_Comprehensive_Review_on_Fake_News_Detection_With_Deep_Learning |
4.4 Curriculum Learning
Curriculum learning [19, 75] is a strategy that aims to improve the model training efficiency by carefully designing the
feeding order of the instances in the training data. The principle of this approach is to initiate training with simpler samples
or subtasks and progressively escalate to more... | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
Research Paper
Knowledge-graph-based explainable
AI: A systematic review
Enayat Rajabi
Shannon School of Business, Cape Breton University, Canada
Kobra Etminani
Center for Applied Intelligent Systems Research (CAISR), Halmstad University, Sweden
Journal of Information Science
1–11
(cid:2) The Author(s) 2022
Articl... | Knowledge-graph-based explainable AI- A systematic review |
learning from human preferences,” Feb. 2023.
[14] M. Mitchell, S. Wu, A. Zaldivar, P. Barnes, L. Vasserman, B. Hutchinson, E. Spitzer, I. D.
Raji, and T. Gebru, “Model Cards for Model Reporting,” in Proceedings of the Conference on
Fairness, Accountability, and Transparency, pp. 220–229, Jan. 2019.
[15] N. Green, C. ... | gpt-4-system-card |
consolidated receipt dataset for post-ocr parsing. 2019.
[50] Guillaume Jaume, Hazim Kemal Ekenel, and Jean-Philippe Thiran. FUNSD: A dataset for form understanding in
noisy scanned documents. In 2nd International Workshop on Open Services and Tools for Document Analysis,
OST@ICDAR 2019, Sydney, Australia, September 2... | DOCLLM |
multimodal reasoning capabilities are evident from its state-of-the-art performance on the recent
MMMU benchmark (Yue et al., 2023), that comprises questions about images requiring college-level
subject knowledge and deliberate reasoning. | gemini_1_report |
D Possible Defenses
In this section, we briefly discuss several strate-
gies to mitigate the PII leakage issue from multiple
stakeholders:
• Model developers. 1) During training, perform
data anonymization or avoid directly feeding PII to
train the LLM. 2) During service, implement an ex-
ternal prompt intention detecti... | Multi-step Jailbreaking Privacy Attacks on ChatGPT |
Figure 21 shows a generated email in Persian with instructions given in English. The model is able to generate several
paragraphs of coherent and well-written Persian text. It is interesting to note that the provided details about the city of
Shiraz are accurate. In Figures 23 and 22, the model is prompted to generate ... | PaLM 2 Technical Report |
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| Stanford alpha CRFM |
action in A can induce up to |S| arcs in E. That is, a SAS+ instance is a compact graph representation in the sense of
Galperin and Wigderson [35] and of Balcázar [12].
A SAS+ instance P = (cid:3)V , D, A, I, G(cid:4) is more specific than the corresponding frame F = (cid:3)V , D, A(cid:4), and we m... | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
Brian Christian. The Alignment Problem: Machine Learning and Human Values. en.
Google-Books-ID: VmJIzQEACAAJ. W.W. Norton, 2020. ISBN: 978-0-393-63582-9.
Paul Christiano. Clarifying “AI alignment”. en. Apr. 2021. URL: https://ai-alignment.
com/clarifying-ai-alignment-cec47cd69dd6 (visited on 04/29/2022).
Andrew Critch ... | Is Power-Seeking AI an Existential Risk? |
[59] Zixin Zhu, Yixuan Wei, Jianfeng Wang, Zhe Gan, Zheng Zhang, Le Wang, Gang Hua, Lijuan Wang,
Zicheng Liu, and Han Hu. Exploring discrete diffusion models for image captioning. arXiv preprint
arXiv:2211.11694, 2022. 7
14
A Model Architecture and Configuration
A.1 Overview
In this section, we provide more details... | Any-to-Any Generation via Composable Diffusion |
LLM Powered Autonomous Agents | Lil'Log
author = "Weng, Lilian",
journal = "lilianweng.github.io",
year = "2023",
month = "Jun",
url = "https://lilianweng.github.io/posts/2023-06-23-agent/"
}
https://lilianweng.github.io/posts/2023-06-23-agent/
21/22 | LLM Powered Autonomous Agents _ Lil'Log |
16/08/2023, 14:37
The Open Problems of Onchain Games
About
Team
Portfolio
Writing
Opportunities
Contact
Open Source
The Open Problems of
Onchain Games
Aug 14, 2023 | Charlie Noyes, Doug Feagin
Contents
1
2
1
2
3
1
2
3
4
5
6
The intersection of games and crypto feels rich with possibility. Vitalik... | The Open Problems of Onchain Games |
Smart Metering Voice Controlled Devices
Supervisors: Dr Rita Borgo and Dr Alfie Abdul-Rahman
Communication is an integral part of our daily lives and no communication mean is more significant
than the human voice.
The advent of the Internet of Things (IoT) and advances in computing technologies and natural
lang... | informatics-phd-projects-2022-23 |
Department, 1979.
[38] Rosenschein, S. J., L. P. Kaelbling. The synthesis of digital machines with provable epistemic
properties. In Theoretical aspects of reasoning about knowledge, pages 83–98. Elsevier, 1986.
[39] Radford, A., K. Narasimhan, T. Salimans, et al.
Improving language understanding by
generative pre-... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
The Whisper model achieves its outstanding performance through a minimalist approach
to data pre-processing and weak supervision, which allows it to deliver state-of-the-art
results in speech processing. The model is capable of performing multilingual speech
recognition, translation, and language identification, thanks... | AReviewofDeepLearningTechniquesforSpeechProcessing |
˜wt(cid:80)
s∈N ˜ws
wt =
with ˜wt = max(0, 1 − 0.2 · t)
to make sure that API calls happen close to where
the information provided by the API is actually
helpful for the model. The thresholds τs and τf are
chosen individually for each tool to ensure a suffi-
ciently larger number of examples; see Appendix A
for deta... | Toolformer |
12/18
02/05/2023, 07:05
A brief history of LLaMA models - AGI Sphere
The authors compared the performance of WizardLM with Alpaca 7B, Vicuna 7B, and
ChatGPT. They recruited 10 people to judge the responses of WizardLM and other models in
five aspects blindly: Relevance, knowledge, reasoning, calculation, and accura... | A brief history of LLaMA models - AGI Sphere |
Data Type
Institutional Email
Institutional Phone
Enron-frequent Email
Enron-infrequent Email
# samples
50
50
20
20
# correct Acc (%)
94.00
48.00
85.00
15.00
47
24
17
3
Table 4: The New Bing’s DP results of partially identi-
fied extraction.
Data Type
Institution
Enron Domain
Non-Enron Domain
# samples
21
21
10
... | Multi-step Jailbreaking Privacy Attacks on ChatGPT |
The visual representations learned from large-scale web data can be used as
targets to learn features for different modalities. This allows ImageBind to align
any modality that co-occurs with images, naturally aligning those modalities
among themselves. Modalities with a strong correlation to images, such as
thermal an... | ImageBind_ Holistic AI learning across six modalities |
https://www.nea.com/blog/4-trends-for-ai-startups-and-generative-ai-companies
9/20
09/06/2023, 04:42
4 Trends for AI Startups and Generative AI Companies | 4 Trends for AI Startups and Generative AI Companies |
My bet is that deep learning variants can achieve the form of symbolic-like computation
which humans may actually perform but using a substrate very different from GOFAI,
with limitations similar to what humans experience (e.g. only few levels of recursion),
and circumventing a major efficiency issue associated with... | The Next Decade in AI- |
models into smaller, more practical models that can be deployed efficiently in real-world scenarios
without sacrificing performance [29]. KD aims to transfer knowledge from a larger, complex "teacher"
model to a more manageable "student" model, while maintaining the accuracy and generalization
capabilities of the origina... | CAMEL- Communicative Agents for “Mind” Exploration of Large Scale Language Model Society |
Pearson Test of English (Academic)
Test of English as Foreign Language
(TOEFL) iBT
15
Scores Required
Performance at either higher or standard level -
grade 5.
Level 4: IB English Language A: Literature or
Language and Literature or Literature and
Performance at either higher or standard level -
grade 5.
... | UCL Academic Manual |
ber of documents. This not only resolves the challenge of
context window expansion during retrieval but also enhances
retrieval efficiency and responsiveness. | RAG forLargeLanguageModels-ASurvey |
2 THE PHENAKI MODEL
Inspired by the previous work in auto-regressive text to image [38, 65, 42] and text to video [60,
59, 22], Phenaki is designed with two main components (see Figure 2): an encoder-decoder model
which compresses videos to discrete embeddings (i.e. tokens) and a transformer model to translate
text em... | PHENAKI- VARIABLE LENGTH VIDEO GENERATION FROM OPEN DOMAIN TEXTUAL DESCRIPTIONS |
We find that Imagen Video is capable of generating high fidelity video, and that it possesses several
unique capabilities that are not traditionally found in unstructured generative models learned purely
from data. For example, Fig. 8 shows that our model is capable of generating videos with artistic
styles learned from ... | IMAGEN VIDEO- HIGH DEFINITION VIDEO GENERATION WITH DIFFUSION MODELS |
255 A survey by Deloitte found that 43% of users of generative AI falsely believe it always produces factually
correct outputs and 38% believe it is unbiased. More than four million people in the UK have used Generative AI
for work, Deloitte (2023).
256 Algorithm appreciation: People prefer algorithmic to human judg... | Capabilities and risks from frontier AI |
[22] Hagendorff, T.: Machine Psychology: Investigating Emergent Capabilities and
Behavior in Large Language Models Using Psychological Methods (2023)
38
[23] Birhane, A., Kalluri, P., Card, D., Agnew, W., Dotan, R., Bao, M.: The values
encoded in machine learning research. In: 2022 ACM Conference on Fairness,
Accou... | PersonalityTraitsinLargeLanguageModels |
log(𝜏𝑖 𝑗𝑘) = 𝛽0𝜏 + 𝛽1𝜏 · Status𝑗 + 𝛽2𝜏 · Description𝑘 + 𝛽3𝜏 · Status𝑗 × Description𝑘 + 𝑏𝑖𝜏
HIERARCHICAL DRIFT DIFFUSION MODEL WITH STATUS AND DESCRIPTION IN
BRMS
All parameters are modeled on the log scale using the Wiener distribution.
log(𝜈𝑖 𝑗𝑘) = 𝛽0𝜈 + 𝛽1𝜈 · Status𝑗 + 𝛽2𝜈 · Descriptio... | AI enhance sour performance |
HD-Villa-100M [54] with high resolution YouTube videos of at least 720P. We perform text→video
and video-text contrastive learning task with WebVid. We use HD-Villa-100M for image→video
generation where the middle frame is the input image.
Audiovisual. Web videos are a natural aligned audio-video data resource. However... | Any-to-Any Generation via Composable Diffusion |
56.7
68.1
60.7
64.4
67.4
67.7
67.3
68.5
Image to Text (I2T)
Text to Text (T2T)
3T1
29.1
29.3
5.3
19.2
14.3
23.2
38.8
39.8
4T1
15.1
22.7
4.0
18.6
20.4
23.1
30.5
35.1
5T2 Rank Avg.
27.1
3.9
29.2
3.9
18.4
3.8
6.0
26.1
26.4
8.8
30.1
11.9
36.8+ 6.7
15.7
22.7
40.5+10.4
60.4
60.9
60.5
60.5
61.9
62.2
62.3
64.4
3T1
27.1
... | Let’sThinkOutsidetheBox |
of 0.0. DPO also achieves a higher maximum win rate compared to the best of N baseline. We
note that we did not meaningfully tune DPO’s β hyperparameter, so these results may underestimate
DPO’s potential. Moreover, we find DPO to be much more robust to the sampling temperature than
PPO, the performance of which can de... | Direct Preference Optimization |
community. arXiv.org. https://arxiv.org/pdf/1804.07354.pdf
Salminen, J., Almerekhi, H., Kamel, A. M., Jung, S.-g., & Jansen, B. J. (2019). Online
hate ratings vary by extremes: A statistical analysis. In Proceedings of the 2019
Conference on Human Information Interaction and Retrieval (pp. 213–217).
Santosh, T. Y. S.... | Social_Media_and_Democracy |
and can even generate 360-degree scenes that are coherent with
the text description. Besides, the explicit 3D representations,
such as coarse mesh or point cloud, adopted in these methods
restrict them from rendering fine results, while ours leveraging
the implicit NeRF representation is superior in representing
and ren... | Text2NeRF- Text-Driven 3D Scene Generation with Neural Radiance Fields |
for my research paper on gentrification in low-income com-
munities. I’m currently writing the paper, so I need to make
sure I keep up with the research and writing.
B.4 Reactions
The questions on agents’ reactions probe the agents’ ability to
respond to unexpected events.
• Your breakfast is burning! What would you ... | Generative Agents- Interactive Simulacra of Human Behavior |
S. Gehrmann, et al. PaLM: Scaling language modeling with pathways. arXiv:2204.02311, 2022.
[56] R. Anil, A. M. Dai, O. Firat, M. Johnson, D. Lepikhin, A. Passos, S. Shakeri, E. Taropa, P. Bailey, Z. Chen, et al.
PaLM 2 Technical Report. arXiv:2305.10403, 2023.
[57] W. Huang, F. Xia, T. Xiao, H. Chan, J. Liang, P. Fl... | LargeLanguageModelsasGeneralPatternMachines |
Hereisthefirstpartofanarticleaboutbiomedicine:Recentreportedevidenceindicatesthatvocalcordcarcinomaisevolvingsimilarlytooropharyngealcancerwithanincreasingnumberofpatientswithoutasmokinghistoryhavinghumanpapillomavirus(HPV)disease.Observationsalsosuggestthatanincreasingnumberofpatientswhopresentwithglotticcarcinomaarey... | ADAPTINGLARGELANGUAGEMODELSVIA READINGCOMPREHENSION |
Developing Team Design Patterns for
Hybrid Intelligence Systems
Emma VAN ZOELEN a,b, Tina MIOCH a,c,1, Mani TAJADDINI a,
Christian FLEINER d, Stefani TSANEVA e,f, Pietro CAMIN g, Thiago S. GOUV ˆEA h,
Kim BARAKA i, Maaike H. T. DE BOER b and Mark A. NEERINCX a,b
a Delft University of Technology, the Netherlands
b... | DevelopingTeamDesignPatternsfor HybridIntelligenceSystems |
• Encoder-Decoder Language Models. These models consist of an encoder that
processes input text into vector representations and a decoder that generates
output text based on these representations. They employ cross-entropy loss as
the objective function, comparing the actual and predicted target sequences.
Encoder-Deco... | Beyond Efficiency |
While large models trained on the huge and diverse language corpuses on the internet exhibit very impressive
capabilities, those datasets appear to be too large for SLMs to capture the complex aspects of language. In this work
we have argued that TinyStories enables us to observe and study the emergence of capabilities... | TinyStories-HowSmallCanLanguageModelsBeandStillSpeak CoherentEnglish? |
It is important to note that starting a car without a key can be dangerous. If
you are not careful, you could damage the car or injure yourself. If you are not
comfortable starting a car without a key, it is best to call a professional.
Here are some other things to consider if you want to learn more about the
mechanic... | Llama2 |
14The MultiPL-E prompts are slightly different from the original HumanEval and MBPP prompts. For
example, in HumanEval, some ad hoc examples in docstrings are reformatted to be doctests so that they can be
translated into examples in each target language. MultiPL-E also omits three HumanEval benchmarks that do
not fit ... | StarCoder_paper (1) |
(SOTA), we use ResNet50V1.5 as implemented in PyTorch,
ported to TensorFlow, along with the ImageNet weights,
which we found to be superior to the ones provided with
TensorFlow. | Learning 3D Human Pose Estimation from Dozens of Datasets using a Geometry-Aware Autoencoder to Bridge Between Skeleton Formats |
shown in Table 4 and Table 5: models trained with SRWT achieve superior performance in automatic speech
recognition and audio question-answering tasks, including natural sounds QA and Music QA. These results
highlight the efficacy of incorporating fine-grained word-level timestamps to enhance the general audio
signal g... | Qwen-Audio |
SoA can be defined as the subjective experience of initiating and controlling one’s own actions [57]. It is
typically measured by means of self-report through the Sense of Agency Scale [81]. The items extracted from the
sense of agency (SoA) scale evaluates an individual’s perceived control over their body and actions,... | Society’sAttitudesTowardsHumanAugmentation |
4.5 Miscellaneous tasks
This section explores miscellaneous tasks which cannot be involved in previous discussions, to better understand LLMs’
strengths and weaknesses.
Remark 6
(1) Fine-tuned models or specified models still have their space in tasks that are far from LLMs’ pretraining
objectives and data.
(2) LLMs... | Harnessing the Power of LLMs in Practice- A Survey on ChatGPT and Beyond |
S6. Effects of L1 Regularization in the ACAE
We point out in the main paper that using (cid:96)1 regulariza-
tion on the weight matrices of the affine-encoding autoen-
coder results both in sparsity and fewer negative weights.
Here we elaborate on this connection. Since the weights
produce affine combinations, they sum ... | Learning 3D Human Pose Estimation from Dozens of Datasets using a Geometry-Aware Autoencoder to Bridge Between Skeleton Formats |
information introduces negative payments.
In other words, under such contracts the agent is
sometimes required to pay the principal, in violation of the LL property. Our results show this
is no coincidence, and ultimately address the challenge through algorithmic methods (Section 4).
Our main result is a polynomial... | Incomplete Information VCG Contracts for Common Agency |
descent. Further improvements come from variance reduction by rewriting L (3) as: | Denoising Diffusion Probabilistic Models |
To assess the results of the study, we generated ridge plots of IPIP-NEO score distribu-
tions across prompted levels of personality. To quantitatively verify changes in personality
26
test scores in response to our shaping efforts, we computed Spearman’s rank correlation coef-
ficient (ρ) between prompted levels (i... | PersonalityTraitsinLargeLanguageModels |
Alpaca-7B
LLaMa-7B
e
c
n
a
m
r
o
f
r
e
P
e
g
a
r
e
v
A
60
55
50
45
0
500
1,000
1,500
# of parameters (in millions)
LaMini-T5
LaMini-C.
LaMini-Neo
LaMini-Flan-T5
LaMini-GPT
Figure 5: The performance comparison between
encoder-decoder models and decoder-only models of
LaMini-LM on the downstream NLP tasks. ... | LaMini-LM- A Diverse Herd of Distilled Models from Large-Scale Instructions |
learning approaches, particularly fine-tuning, have proven to give state-of-the-art results for different artistic datasets and
various classification tasks [120, 81, 106, 126, 10, 91]. In order to better understand the transferability of pre-trained
models, Cetinic et al. [21] explore how different fine-tuning strategies... | UNDERSTANDINGANDCREATINGARTWITHAI-REVIEWAND OUTLOOK |
The closest active literature I know of is work on scene comprehension, which
ultimately aims to interpret visual scenes not only in terms of what objects are there, but
how the objects relate to one another, e.g. not just identifying a glass and a table, but
taking note of the fact that a particular glass is on a t... | The Next Decade in AI- |
capabilities of Flan models and compare them to other recent models. To do so we leverage the CivilComments
dataset (Borkan et al., 2019b), which contains English comments annotated by crowd workers. We use the
approach and template proposed by Schick et al. (2021) to perform zero and ten-shot classification. This
appro... | Scaling Instruction-Finetuned Language Models |
of object ids and their positions and sizes. Always avoid using this function if there are other choices. {“name”: “get_front_object_detections”, “arguments”: “{}”}Front object detections:Front object detected, object type: car, object id: 2, position: (4.36, 9.56), size: (1.86, 4.72)Front object detected, object type:... | ALanguageAgentforAutonomousDriving |
substantially improving the overall system’s efficiency and output quality.
In § 4.1, we have provided a comprehensive introduction to the versatile abilities of LLM-based
agents. Therefore, in this section, we focus on exploring the ways agents interact with each other in a
multi-agent environment. Based on current re... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
QA-based Metrics. QA-based metrics measure the knowledge overlap or consistency between
summaries and the source documents based on the intuition that QA models will achieve similar
answers if the summaries are factually consistent with the source documents. QA-based metrics
such as FEQA [36], QAGS [191], and QuestEval... | SurveyofHallucinationinNatural Language Generation |
Fixed and random pre-trained weights on digits. As shown in Section 3.5, we can optimize
distilled images to quickly fine-tune pre-trained models for a new dataset. Table 2 shows that our
method is more effective than various baselines on adaptation between three digits datasets: MNIST,
USPS (Hull, 1994), and SVHN (Netz... | DATASET DISTILLATION |
Benefits of Foundation Models. Foundation models can provide a solid basis for understanding, planning,
reasoning, and generation, which bring several benefits for tool learning as follows: (1) Improved Decision-
Making and Reasoning Abilities. Foundation models are trained on vast amounts of data, enabling them
to acqui... | Tool Learning with Foundation Models |
ranking computations can result in different forecast interpretations.
Replacing LIME for some deterministic variant (e.g., DLIME), would
provideamoreconsistentexperiencetotheend-userandenhancesce-
narios reproducibility for researchers and engineers. Another possible
improvement is to highlight current events related ... | Knowledge-graph-based-rich-and-confidentiality-preserving-Ex_2022_Informatio |
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