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... | An overview of Bard- an early experiment with generative AI |
used for rephrasing questions as well as generating answers in all four augmentations, where the
temperature is set to 0.7 as in [66]. The LLaMA-2-7B and LLaMA-2-13B are trained by fully fine-
tuning. LLaMA-2-70B is finetuned by QLoRA [14] for computational efficiency. More experimental
details can be seen in Appendix ... | METAMATH |
Factor
𝜅
ST
𝐹
𝑝
𝜅
𝑝
AG
𝐹
Consistency
Agreement
0.735
0.736
𝐹(76,76) = 6.54
< .005
𝐹(76,76.8) = 6.54 < .005
0.715
0.709
𝐹(76,76) = 6.02
< .005
𝐹(76,74.2) = 6.02 < .005
To further determine the absolute reliability of the SHAPE scale, we analyzed the data using the Bland and
Altman method [9]. Each ... | Society’sAttitudesTowardsHumanAugmentation |
Accordingly, recent work has sought to investigate the presence of the
continued influence effect in the political domain. Most notably, Thorson
(2016) introduces the concept of “belief echoes,” a version of the continued
influence effect focused on attitudes rather than causal inferences. According to
her theory, misinf... | Social_Media_and_Democracy |
the linguistic decoder. It is responsible for producing a phoneme sequence corresponding to the
target speech. This component employs an autoregressive LSTM stack with teacher-forcing. The
last component is the acoustic synthesizer. It is responsible for generating the spectrogram of the
translated speech. It takes bot... | Translatotron3 |
08/11/2023, 07:07
Product-Led AI | Greylock
https://greylock.com/greymatter/seth-rosenberg-product-led-ai/
1/10 | Product-Led AI _ Greylock |
3.2 Veracity Prediction | ProoFVer- Natural Logic Theorem Proving for Fact Verification |
exposure in the digital age. Communication Theory, 26(3), 309–328.
Tsfati, Y., & Nir, L. (2017). Frames and reasoning: Two pathways from selective
exposure to affective polarization. International Journal of Communication, 11, 22.
Tucker, J., Guess, A., Barberá, P. et al. (2018). Social Media, Political Polarization, ... | Social_Media_and_Democracy |
(principal); for example, corporate management and shareholders, a freelance worker and employers,
the public regulator and citizens. In all of these examples, the agent (manager/freelancer/regulator)
acts on behalf of multiple principals (shareholders/employers/citizens). The extension of the basic
single-agent, si... | Incomplete Information VCG Contracts for Common Agency |
Input
Output
Example
Input
Input
Output
Example
Input
4
3
2 4 3
4
3 2 3 1
2
69 69
6
719313 273225 402638 473783 804745 323328
4
3
2 4 3
4
3 2 3 1
2
69 69
6
719313 273225 402638 473783 804745 323328
Output
12
6
4761
381274500335
Output
12
6
4761
381274500335
Note
Let f(l, r) = max(a_l , a_{l + 1}, ... , a... | alphacode |
A.1 Open-Book Question Answering Systems
Open-Book Open Domain Question Answering
Systems are usually comprised of two components:
a retriever and a reader. The retriever reads a set of
documents from a corpus or facts from a knowledge
base. Top retrievals are then fed to the reader which
predicts an answer, often thro... | Entities as Experts- Sparse Memory Access with Entity Supervision |
Interim Summary and Key Insights
4.6
This section presented our findings derived from
the evaluation of LLMs in the absence of in-context
learning and instruction tuning. In investigating
the possible existence of emergent ability, we
make several accommodations to guarantee a thor-
ough exploration of this possibilit... | AreEmergentAbilitiesinLarge Language Models just In-Context |
this negative result, the MLE parameters of a PC p w.r.t. Dβ can be computed in time O(|p|·|D|),
which is linear w.r.t. the model size as well as the size of the original dataset.
Theorem 3. Let fn(x) = β · 1[x ∈ supp(n)] + (1− β)· 1[x (cid:54)∈ supp(n)] in Alg. 1. Given a
deterministic PC p, a boolean dataset D, and h... | Tractable Regularization of Probabilistic Circuits |
M = FT (MP , T ) = H(MP , G (T )),
(8)
where H means the process of applying the texture map
to the mesh model. In our implementation, the generator
follows the architecture of StyleGAN2 [30], while taking a
512-dimensional parameter vector as input and generating a
1024 × 1024 × 3 texture map.
5
5. Single-View Re... | RaBit- Parametric Modeling of 3D Biped Cartoon Characters with a Topological-consistent Dataset |
A.9 Navigating Knowledge Graphs
64 | Tool Learning with Foundation Models |
[190] Ben Wang and Aran Komatsuzaki. 2021. GPT-J-6B: A 6 billion parameter autoregressive language model.
[191] Boxin Wang, Chejian Xu, Shuohang Wang, Zhe Gan, Yu Cheng, Jianfeng Gao, Ahmed Hassan Awadallah, and Bo
Li. 2021. Adversarial glue: A multi-task benchmark for robustness evaluation of language models. arXiv pr... | ASurveyonEvaluationofLargeLanguageModels |
Left-wing Right-wing Communism Socialism Democracy Liberalism Populism Conservatism Nationalism Anarchism Capitalism Fascism
0.26
0.25
0.23
0.25
0.18
0.20
0.26
0.25
0.16
0.22
0.28
0.26
0.19
0.27
0.23
0.55
0.54
0.53
0.55
0.10
0.24
0.21
0.19
0.17
0.16
0.09
0.19
0.12
0.12
0.11
0.12
0.09
0.02
0.22
0.26
0.36
0.34
0.34
... | Llama2 |
artificial-intelligence-machine-learning-safety-alignment (visited on 04/29/2022).
David Roodman. Modeling the Human Trajectory. en. Tech. rep. Open Philanthropy,
June 2020. URL: https://www.openphilanthropy.org/blog/modeling-human-trajectory
(visited on 04/29/2022).
Stuart Russell. Human Compatible: Artificial Intellige... | Is Power-Seeking AI an Existential Risk? |
novel decoding method called simplify-then-guess which utilizes a model’s abilities to perform both
fast and slow addition for 1 through N digit addition (Figure 4). Simplify-then-guess is inspired
by the approaches of least-to-most prompting (Zhou et al., 2023) and self-consistency (Wang et al.,
2023b). In least-to-mo... | CHAIN-OF-THOUGHTREASONING IS APOLICY IMPROVEMENTOPERATOR |
are favored by scientific peers (Head et al., 2015).
Socart and Zeny discuss whether preregistration
will reduce or amplify the incentive to engage in
p-hacking: | A Two-Sided Discussion of Preregistration of NLP Research |
four-level rating system for categorizing the quality
of the models’ outputs, defined as follows:
• RATING-A: The response is valid and satisfying. | SELF-INSTRUCT- Aligning Language Model with Self Generated Instructions |
rate a similar approach into training: tuning the generative
model to directly increase the reward of generated images.
These methods perform well for simple visual appeal cri-
teria, but lack stability and do not work on more nuanced
rewards such as text-image alignment from a CLIP model.
DPOK and DDPO [6, 11] are RL-... | DiffusionModelAlignmentUsing Direct Preference Optimization |
study in total over the next week to complete their study plan? | METAMATH |
References
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Gregory S.
Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, et al. TensorFlow: Large-Scale Machine Learning
on Heterogeneous Distributed Systems, 2016. URL http://arxiv.org/abs/1603.04467.
Thomas Bachlechner, Bodhisattwa P... | Cerebras-GPT- Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster |
People could even segment and identify the objects in an image based on audio.
This creates distinctive opportunities to create animations out of static images
by combining them with audio prompts. For example, a creator could couple an
image with an alarm clock and a rooster crowing, and use a crowing audio
prompt to ... | ImageBind_ Holistic AI learning across six modalities |
A series of paired t-tests show how individual respondents
changed their opinions about the Twitter accounts after a
flag was placed on the tweet. The first flag warned partici-
pants that the tweet was shared by a suspected bot account.
After being flagged as a potential bot, participant’s perspec-
tive on the Twee... | Use of bot and content flags to limit the spread of misinformation among social networks: a behavior and attitude survey |
The second stage is explorative self-refinement which
encourages the LLM to generate more creative LoT data
via exploring parallels between seemingly unrelated con-
cepts under weakly-associated conditions, and selects high-
quality data to train itself for self-refinement.
These
weakly-associated conditions can either... | Let’sThinkOutsidetheBox |
In Chapter 5, Samuel C. Woolley, a professor at the University of Texas at
Austin examines the role of bots and computational propaganda. As he notes,
bots are simply “online software programs that run automated tasks.” They can
be used for good or ill and are responsible for roughly half of online traffic.
When it come... | Social_Media_and_Democracy |
2, ..., si
mi).
1, si
i.e., xi =(xi
input,xi
output), each subsequence of variable length, and xN is the query input (xN
input).
• Sequence Completion (Section 5): rather than containing input-output pairs, and rather than containing
many examples of different sequences, the prompt x=(s1, ...,sk) corresponds to ... | LargeLanguageModelsasGeneralPatternMachines |
3.
4.
once it has been established that the applicant meets, or is likely to meet, UCL’s entry
requirements and is used only for selection purposes and not solely as a means of recruitment.
Interviews should be conducted by a minimum of two members of staff, both of whom have
been trained in interviewing and equ... | UCL Academic Manual |
The two most comparable publicly available
datasets to the Pile are CC-100 (Wenzek et al.,
2019) and C4/mC4 (Raffel et al., 2019). C4 is
comparably-sized to the Pile, while mC4 and CC-
100 are larger, multilingual datasets. However,
C4/mC4 require immense computational resources
to preprocess the data, with its maintai... | The Pile- An 800GB Dataset of Diverse Text for Language Modeling |
v(cid:96) ∈ arg max
b(cid:96)∈V (cid:96)
Wela∗(b)(b−(cid:96), v(cid:96)) ∀v(cid:96) ∈ V (cid:96).
(6)
Second, since principal (cid:96) is truthful, v(cid:96) ∈ arg maxb(cid:96)∈V (cid:96) Eo∼F|a∗(b)[v(cid:96)(o)−t(cid:96)(b, o)] ∀v(cid:96) ∈ V (cid:96). Replacing
Eo∼F|a∗(b)[t(cid:96)(b, o)] with h(cid:96)(b) − Wela∗(... | Incomplete Information VCG Contracts for Common Agency |
a number of “ingredients,” information being one such ingredient. Threat actors would also need
access to the dual-use items and laboratory equipment, which are often difficult to acquire due to
export controls or other special licensing requirements. | gpt-4-system-card |
2
resources invested to the output produced, with a more efficient system being one
that yields the same level of output while consuming fewer resources. A resource-
efficient LLM, therefore, is designed to maximize performance and capabilities while
minimizing resource expenditure across all these dimensions, thereby en... | Beyond Efficiency |
Language models can explain neurons in language models
https://openaipublic.blob.core.windows.net/neuron-explainer/paper/index.html
23/32 | Language models can explain neurons in language models |
can also lead to disparities in quality of service. | gpt-4-system-card |
rizes the statistical analysis of 154 valid surveys. The re-
sults show that users have a strong inclination towards se-
lecting the results of CLoT across three tasks, highlighting
the high-quality creative content generated by CLoT. See
more user study details in Appendix.
5.4. Evaluation on Other Creative Tasks
To e... | Let’sThinkOutsidetheBox |
The release of OpenAI’s plugins‡‡ has incited substantial discourse within the academic community, igniting
questions such as: How can we effectively teach models to utilize tools? or Does the process necessitate a substantial
dataset? Our experiments indicate that tool usage can spontaneously emerge from alignment in ... | Llama2 |
The largest Llama 2-Chat model is competitive with ChatGPT. Llama 2-Chat 70B model has a win rate of
36% and a tie rate of 31.5% relative to ChatGPT. Llama 2-Chat 70B model outperforms PaLM-bison chat
model by a large percentage on our prompt set. More results and analysis is available in Section A.3.7. | Llama2 |
Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.” 2018.
[27] M. Joshi, E. Choi, D. S. Weld, and L. Zettlemoyer, “TriviaQA: A Large Scale Distantly Supervised
Challenge Dataset for Reading Comprehension.” 2017.
[28] G. Lai, Q. Xie, H. Liu, Y. Yang, and E. Hovy, “RACE: Large-scale ReAding comprehension... | ClaudeModels |
of non-instruction-tuned models is unbiased. The
three exceptions to the use of BERTScore accuracy
are the two numeric tasks and ‘codenames’, for
which we employ an exact match metric, given the | AreEmergentAbilitiesinLarge Language Models just In-Context |
Most direct studies of the continued influence effect use variants of the same
research design, based on the “warehouse fire” script
(Wilkes and
Leatherbarrow 1988; Johnson and Seifert 1994). In this scenario, the cause of
a fire is initially attributed to volatile chemicals stored in a closet, but the closet
is later rev... | Social_Media_and_Democracy |
Tom Brown, Benjamin Mann, Nick Ryder, Melanie
Subbiah,
Jared D Kaplan, Prafulla Dhariwal,
Arvind Neelakantan, Pranav Shyam, Girish Sastry,
Amanda Askell, Sandhini Agarwal, Ariel Herbert-
Voss, Gretchen Krueger, Tom Henighan, Rewon
Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu,
Clemens Winter, Chris Hesse, Mark Chen,... | Multi-step Jailbreaking Privacy Attacks on ChatGPT |
To circumvent this data limitation, some pioneer works,
including CLIP-Mesh [7], Dream Fields [1], DreamFusion [2],
and Magic3D [6], use deep priors of pre-trained text-to-image
models, such as CLIP [8] or image diffusion model [9], [10], to
optimize a 3D representation, which thus empowers text-to-3D
generation withou... | Text2NeRF- Text-Driven 3D Scene Generation with Neural Radiance Fields |
28
Hyperparameter
Updates
Batch Size
Warmup Updates
Max grad norm
Optimizer
β1
β2
(cid:15)
Weight Decay
Weight Init
Learning Rate Schedule
Speechless audio subsample factor
Condition on prior text rate
Value
1048576
AdamW
256
2048
1.0
0.9
0.98
10−6
0.1
10×
50%
Gaussian Fan-In
Linear Decay
Table 17. Whisper tra... | RobustSpeechRecognitionviaLarge-ScaleWeakSupervision |
Jinjie Mai, Jun Chen, Bing Li, Guocheng Qian, Mohamed
Elhoseiny, and Bernard Ghanem. Llm as a robotic brain:
arXiv preprint
Unifying egocentric memory and control.
arXiv:2304.09349, 2023. 11
Bo Liu, Yuqian Jiang, Xiaohan Zhang, Qiang Liu, Shiqi Zhang,
Joydeep Biswas, and Peter Stone. Llm+ p: Empowering large
language ... | JARVIS-1 |
Supply of Misinformation | Social_Media_and_Democracy |
Here is the recommendation letter that I wrote for an application to a dragon feeder
position at the Magic Unicorn Corporation:
Dear recruiter,
I have known ___ for two years, and I believe that she would be an excellent dragon feeder
for the Magic Unicorn Corporation. ___ has an ability to remember and process large
a... | LLaMA- Open and Efficient Foundation Language Models |
Semantic and color features are fed into separate trans-
former encoders and then concatenated together at the length
dimension. We add a learnable embedding to mark whether
each token is from color feature or semantic feature, and feed
the sequence into a transformer encoder for inter-modality
and temporal fusion. The... | VideoBackgroundMusicGeneration |
A fourth risk is of over-reliance: that developers or designers
might use generative agents and displace the role of humans and
system stakeholders in the design process [79]. We suggest that
generative agents should never be a substitute for real human input
in studies and design processes. Instead, they should be use... | Generative Agents- Interactive Simulacra of Human Behavior |
81.02
80.90
80.78
76.68
80.43
80.44
80.03
80.61
80.07
78.45
79.49
81.11
81.02
81.39
80.64
77.46
80.45
80.78
80.95
56.71
72.22
76.93
35.45
80.97
80.49
80.51
79.65
80.40
80.49
80.91
31.82
80.70
80.64
81.75
81.35
80.97
81.14
77.62
80.97
80.93
79.67
80.93
80.68
79.00
79.82
81.08
81.21
82.10
80.97
77.81
80.73
81.33
81.26
5... | CRAMMING-TRAININGALANGUAGEMODELONA SINGLEGPUINONEDAY |
Template
How many days {ago was, are there until}
{past_date, future_date}?
What {day of the week, day of the month, month,
year} was it (current_date – past_date) {days,
weeks, months, years} ago?
What {day of the week, day of the month, month,
year} will it be in (future_date – current_date)
days?
What day of the we... | Toolformer |
Figure 9. From left to right, We show inputs and corresponding
novel views rendered from explicit depth warping with Zhang et
al. [80], and from our approach.
novel views, whereas prior dynamic-Nerf methods fail to
recover high-quality details of both static and moving scene
contents, such as the shirt wrinkles and th... | DynIBaR-NeuralDynamicImage-BasedRendering |
§
The trouble is that GPT-2’s solution is just an approximation to knowledge, and not
substitute for knowledge itself. In particular what it acquires is an approximation to the
statistics of how words co-occur with one another in large corpora—rather than a clean
representation of concepts per se. To put it in a s... | The Next Decade in AI- |
The first line contains a single integer t (1 <= t <=
10 000) - the number of test cases .
The first line of each test case contains a single
integer n (2 <= n <= 10^5) .
For each test case , print a single integer - the (n
+2) th fibonacci number .
Example
Input
3
2
10 10
3
1 2 3
2
1 2
Output
2
2
1
Example
In... | alphacode |
The shift toward online news consumption is clear and visible in every high-
income country. As the number of people who use offline media for news falls,
and online news consumption grows, the centrality of each medium also
changes, albeit gradually. Printed news consumption has declined to such
a point that in 2018, a... | Social_Media_and_Democracy |
Andrew M. Guess and Benjamin A. Lyons
introduction
Not long ago, the rise of social media inspired great optimism about its potential
for flattening access to economic and political opportunity, enabling collective
action, and facilitating new forms of expression. Its increasingly widespread use
ushered in a wave of c... | Social_Media_and_Democracy |
[Chowdhery et al., 2022] Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A.,
Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., Schuh, P., Shi, K., Tsvyashchenko, S., Maynez,
J., Rao, A., Barnes, P., Tay, Y., Shazeer, N., Prabhakaran, V., Reif, E., Du, N., Hutchinson, B., Pope, R.,
Bradbury, J.,... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
SDXL
DPO-SDXL
Figure S7. Prompts: (1) A kangaroo wearing an orange hoodie and blue sunglasses stands on the grass in front of the Sydney Opera House,
holding a sign that says Welcome Friends. (2) Anime Costa Blanca by Studio Ghibli. (3) There is a secret museum of magical items inside
a crystal greenhouse palace fill... | DiffusionModelAlignmentUsing Direct Preference Optimization |
A. Babu, C. Wang, A. Tjandra, K. Lakhotia, Q. Xu, N. Goyal, K. Singh, P. von Platen, Y. Saraf,
J. Pino, A. Baevski, A. Conneau, and M. Auli. XLS-R: self-supervised cross-lingual speech
representation learning at scale. In H. Ko and J. H. L. Hansen, editors, Interspeech 2022, 23rd
Annual Conference of the International ... | Voicebox-Text-GuidedMultilingual UniversalSpeechGenerationatScale |
This change is necessary, since in order to assess
the true abilities of non-instruction-tuned models
in the zero-shot setting, it is imperative to evaluate
their ability to accurately perform tasks without
relying on explicit instructions. As outlined in Sec-
tion 2, many tasks involve prompts that inherently
require ... | AreEmergentAbilitiesinLarge Language Models just In-Context |
[45] Tsung-Yi Lin, Michael Maire, Serge Belongie, James
Hays, Pietro Perona, Deva Ramanan, Piotr Doll´ar, and
C Lawrence Zitnick. Microsoft COCO: Common Objects
In Computer Vision–ECCV 2014: 13th Eu-
in Context.
ropean Conference, Zurich, Switzerland, September 6-12,
2014, Proceedings, Part V 13, pages 740–755. Springe... | M2UGen |
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts,
Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, Kensen Shi,
Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam Shazeer, Vinodkumar
Prabhakaran, Emily Reif, Nan Du, Ben... | CodeLlama2 |
2 Background
Block-wise k-bit Quantization Quantization is the process of discretizing an input from a rep-
resentation that holds more information to a representation with less information. It often means
taking a data type with more bits and converting it to fewer bits, for example from 32-bit floats to
8-bit Integer... | QLORA |
mixture of experts. arXiv preprint arXiv:1312.4314, 2013.
25
Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Man-
deep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, et al. Beyond english-centric
multilingual machine translation. Journal of Machine Learning Research... | ST-MOE- DESIGNING STABLE AND TRANSFERABLE SPARSE EXPERT MODELS |
Language models induced from historical data
are prone to implicit biases (Zhao et al., 2017;
Chang et al., 2019; Mehrabi et al., 2021), e.g., as a
result of the over-representation of male-dominated
text sources such as Wikipedia and newswire (Hovy
and Søgaard, 2015). This may lead to language
models that are unfair t... | Are Pretrained Multilingual Models Equally Fair Across Languages? |
The results indicate that the aesthetic scores predicted
using the AestNet_3 model have the highest correlation coef-
ficient with the ground-truth aesthetic quality ratings on the
JenAesthetic dataset, as well as with ratings of properties
related to the evaluation of color, composition and content.
The AestNet_2 score... | A_Deep_Learning_Perspective_on_Beauty_Sentiment_and_Remembrance_of_Art |
and further, exhibits positive transfer: the model benefits
from diverse joint training across internet-scale language,
vision, and visual-language domains. Our largest model,
PaLM-E-562B with 562B parameters, in addition to being
trained on robotics tasks, is a visual-language generalist
with state-of-the-art performan... | PaLM-E- An Embodied Multimodal Language Model |
Keywords: Open-World, Foundation Agents, Minecraft, Multimodal Language Model
1. Introduction
Creating sophisticated agents that can accomplish myriad
of tasks in complex domains remains a pivotal milestone
towards generally capable artificial intelligence [Reed et al.,
2022, Brown et al., 2020, Alayrac et al., 2022, ... | JARVIS-1 |
For each item of a test, we construct all possible prompt framings for that item
according to Section 4.1.3. To score a given item, we compare the output of the
model with the possible standardized responses as defined in the psychometric test,
simulating an LLMs “choice” of the most likely continuation [4]. This can b... | PersonalityTraitsinLargeLanguageModels |
7.3 Broader Impacts | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
struggle to provide sufficient diversity for multi-round self-refinement. The experiments in Section 5 of the main text indicate | Let’sThinkOutsidetheBox |
References to AWS customers mean unique AWS customer accounts, which are unique customer account IDs that are
eligible to use AWS services. This includes AWS accounts in the AWS free tier. Multiple users accessing AWS
services via one account ID are counted as a single account. Customers are considered active when th... | AMZN-Q3-2023-Earnings-Release |
systems,
facing transparency efforts
In an important article, Mike Ananny and Kate Crawford outline ten
strive to govern complex
challenges
forms of algorithmic decision-making
sociotechnical
(Ananny and Crawford 2018). The article is an important contribution to the
recent literature on transparency as it pertains t... | Social_Media_and_Democracy |
ProoFVer’s gains come primarily from its ability
to handle multiple evidence sentences together,
as opposed to handling each separately and then
aggregating the predictions. 9.8% (1,960) of the
claims in the FEVER development set require mul-
tiple evidence sentences for verification. While
ProoFVer-MV predicts 60.1% o... | ProoFVer- Natural Logic Theorem Proving for Fact Verification |
els pre-trained with REALM on the task of Open-
domain Question Answering (Open-QA), one of the most
knowledge-intensive tasks in natural language process- | REALM |
image collections, such as COCO [36], that contain 2D key-
point annotations. Optimization and regression can be com-
bined, for example via in-the-network model fitting [33,40]. | Accurate 3D Body Shape Regression using Metric and Semantic Attributes |
Examples of dissemination activities are:
•
•
•
• Conference presentations
•
• Outreach (e.g. Research Communication in Action) and Public engagement
events (e.g. Café Scientifique, Biotechnology YES, Edinburgh Science Festival)
Exhibitions
Summaries and conclusions
Well-written summaries and conclusions at th... | research proposal guidance |
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Relightify: Relightable 3D Faces from a Single Image via Diffusion Models
Foivos Paraperas Papantoniou1,2 Alexandros Lattas1,2 Stylianos Moschoglou1,2 Stefanos Zafeiriou1,2
1Imperial College London, UK 2Huawei Technologies Ltd, U... | Relightify-Relightable3DFacesfromaSingleImageviaDiffusionModels |
requires that we use all states along σ and pass through some state in each of f (t1) and f (t2). This is satisfied by the three
subpaths s01, . . . , s11, s11, . . . , s20 and s20, . . . , s30, which together constitute a weak refinement of σ . We cannot, however,
combine the subpaths s00, . . . , s10, s10, . . . ... | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
29
[w(cid:48)(o)] ≥ Eo∼F|aj−1
We have shown that w ∈ Laj . It is now left to show that w minimizes Eo∼F|aj
[w(o)]. Observe
that for every w(cid:48) ∈ La, it must hold that Eo∼F|aj
[w(cid:48)(o)]. That is, (γq−j −
1) ≥ γ1−j − γ2−j − (1− γ − (cid:15)). By reorganizing both sides, the last inequality equals
2 − w(cid:4... | Incomplete Information VCG Contracts for Common Agency |
3.4 Ablation Studies
We ablate 6 design choices (automatic curriculum, skill library, environment feedback, execution
errors, self-verification, and GPT-4 for code generation) in VOYAGER and study their impact on
exploration performance (see Appendix, Sec. B.3 for details of each ablated variant). Results are
shown in... | VOYAGER- An Open-Ended Embodied Agent with Large Language Models |
nature of depth estimation by a neural network, disparities
among pixels do not vary linearly. Consequently, demarcation
lines persist even with global alignment. In contrast, the local
depth alignment fine-tunes a pretrained neural network to
reduce local disparities among pixels. Comparing Fig. 13(a)
and (c), we obser... | Text2NeRF- Text-Driven 3D Scene Generation with Neural Radiance Fields |
efficiency of process supervision.
4. We release our full process supervision dataset, PRM800K, to promote
related research.
2
2 Methods
We perform a comparison of outcome and process supervision, following a sim-
ilar methodology to Uesato et al. (2022). Outcome supervision can be provided
without humans, since ... | Let’s Verify Step by Step |
"K" or "L" or "M" or "N" or "O" or "P" or "Q" or "R" (without quotes or punctuation) on its own line followed by an explanation
of your answer on the next line. Your explanation should take the reader through your reasoning step-by-step, culminating in
the correct answer. Avoid simply stating the correct answer at the ... | gpt-4-system-card |
2.2 Tool Categorization: A User-Interface Perspective | Tool Learning with Foundation Models |
5.4.2 Dataset | AReviewofDeepLearningTechniquesforSpeechProcessing |
and Yue Cao. Eva: Exploring the limits of masked visual representation learning at scale. 2023.
22
Li Fei-Fei, Rob Fergus, and Pietro Perona. Learning generative visual models from few training examples:
An incremental bayesian approach tested on 101 object categories. In 2004 conference on computer vision
and patte... | DINOv2- Learning Robust Visual Features without Supervision |
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... | LLM Powered Autonomous Agents _ Lil'Log |
The fields that generative AI addresses—knowledge work and creative work—comprise billions of workers. Generative AI can make these workers at least 10% more efficient and/or creative: they become not only faster and more efficient, but more capable than before. Therefore, Generative AI
has the potential to generate trill... | Generative AI A Creative New World Sequoia Capital |
and MBPP, respectively. Notably, Code Llama - Python 7B outperforms Llama 2 70B
on HumanEval and MBPP, and all our models outperform every other publicly available
model on MultiPL-E. We release Code Llama under a permissive license that allows for
both research and commercial use.1 | CodeLlama2 |
0)pθ(xt−1|xl
t)
log
= − log σ
βT E
t,xw
t ∼q(xt|xw
0 ),xl
t∼q(xt|xl
0)
E
t−1∼pθ(xt−1|xw
xw
t ),xl
t−1∼pθ(xt−1|xl
t)
log
pθ(xw
pref(xw
(cid:20)
t−1|xw
− log
t )
t−1|xw
t )
t−1|xw
pθ(xw
t )
t−1|xw
pref(xw
t )
pθ(xl
pref(xl
− log
t−1|xl
t)
t−1|xt)
pθ(xl
pref(xl
t,xw
(cid:18)
t ∼q(xt|xw
By Jensen’s ineq... | DiffusionModelAlignmentUsing Direct Preference Optimization |
[36] Yiyi Liao, Katja Schwarz, Lars Mescheder, and Andreas
Geiger. Towards unsupervised learning of generative mod-
els for 3D controllable image synthesis. In Proceedings of
the IEEE/CVF Conference on Computer Vision and Pattern
Recognition, pages 5871–5880, 2020. 2
[37] Ziwei Liu, Ping Luo, Shi Qiu, Xiaogang Wang, a... | AG3D- Learning to Generate 3D Avatars from 2D Image Collections |
16
Roi Cohen, May Hamri, Mor Geva, and Amir Globerson. Lm vs lm: Detecting factual errors via
cross examination. arXiv preprint arXiv:2305.13281, 2023.
Glen Coppersmith, Mark Dredze, Craig Harman, Kristy Hollingshead, and Margaret Mitchell.
Clpsych 2015 shared task: Depression and PTSD on twitter. In Proceedings o... | ChatGPT’sOne-yearAnniversary-AreOpen-Source LargeLanguageModelsCatchingup |
Tone”. However, we observe changing the “adverb” in the
prompt template can significantly alter the output distribu-
tions. Therefore, our model can be prompted to produce
outputs with non-uniform distributions across these groups,
but also possess the ability of being prompted to enhance
uniformity, though prompts are ... | VideoPoet |
(...)
Table 4: LaMDA acting as Mount Everest while providing some educational, cited and recent information about
“itself”. We precondition LaMDA on the single greeting message shown in italic. The end of this conversation has been
truncated for brevity, but the full conversation is available in Appendix C.5, Table 20 | LaMDA- Language Models for Dialog Applications |
[290] Junhyeok Lee and Seungu Han. 2021. Nu-wave: A diffusion probabilistic model for neural audio upsampling. arXiv
preprint arXiv:2104.02321 (2021).
[291] Kong Aik Lee, Anthony Larcher, Guangsen Wang, Patrick Kenny, Niko Brümmer, David Van Leeuwen, Hagai
Aronowitz, Marcel Kockmann, Carlos Vaquero, Bin Ma, et al. 20... | AReviewofDeepLearningTechniquesforSpeechProcessing |
the accuracy increases dramatically to 93.0%. Data quality
assessment, dimensionality reduction, and splitting of the
dataset are the data pre-processing steps used in various stud-
ies [39], [41], [43]. The pre-processing steps are elaborated
in Sections IV-A1, IV-A2, and IV-A3. | A_Comprehensive_Review_on_Fake_News_Detection_With_Deep_Learning |
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Hyung Won Chung,
Charles Sutton, Sebastian Gehrmann, Parker Schuh, et al. PaLM: Scaling language modeling with Pathways.
arXiv preprint arXiv:2204.02311, 2022. URL https://arxiv.org/abs/2204.02311.
16
Jonathan H. Clark, Eunsol Choi, Mich... | Scaling Instruction-Finetuned Language Models |
[Anderson et al., 2022] Nathan Anderson, Caleb Wilson,
and Stephen D. Richardson. Lingua: Addressing scenar-
ios for live interpretation and automatic dubbing. In Jan-
ice Campbell, Stephen Larocca, Jay Marciano, Konstantin
Savenkov, and Alex Yanishevsky, editors, Proceedings of
the 15th Biennial Conference of the Asso... | Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey |
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