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3.5 Multimodal Feedback from Humans
VOYAGER does not currently support visual perception, because the available version of GPT-4 is
text-only at the time of this writing. However, VOYAGER has the potential to be augmented by
multimodal perception models [59, 60] to achieve more impressive tasks. We demonstrate that gi... | VOYAGER- An Open-Ended Embodied Agent with Large Language Models |
I’ll
think of “deployment” as the point where an AI system moves out of a develop-
ment/laboratory/testing environment and into a position of real-world influence (even if this influence
is mediated via e.g. humans following its instructions).125 This isn’t always a discrete point; some-
times, for example, it’s an ongoi... | Is Power-Seeking AI an Existential Risk? |
(*)An augmented human is in full control of what they do. S13
5 TEST-RETEST RELIABILITY AND CONSTRUCT VALIDITY
In this step, we evaluated the construct validity of the SHAPE scale through three methods: (1) Reliability:
conducting a test-retest reliability study. (2) Content validity: analyzing the correlation between... | Society’sAttitudesTowardsHumanAugmentation |
load to provide resources for higher thinking, allowing learners to engage in activities out of their reach, and
allowing learners to generate and test hypotheses (e.g., simulated diagnosis for medical students). | Tool Learning with Foundation Models |
(640, 360, 3) Ego-centric RGB frames.
The coordinates of (x,y,z), pitch, and yaw of the agent.
The environmental information of the agent’s current position,
including biome_id, sea_level, can_see_sky, is_raining etc.
The items in the current inventory of the agent, including
the type and corresponding quantity of e... | JARVIS-1 |
sha1_base64="J0bUOso/uejqONF+gr9Z1NwYuBo=">AAAB9XicbVC7TsMwFL3hWcqrwMhiUSExVQkLjJVYGItEH6hNK8d1WquOE9k3oCrqf7AwgBAr/8LG3+C0GaDlSJaOzrlX9/gEiRQGXffbWVvf2NzaLu2Ud/f2Dw4rR8ctE6ea8SaLZaw7ATVcCsWbKFDyTqI5jQLJ28HkJvfbj1wbEat7nCbcj+hIiVAwilbq9yKK4yDMOrM+DsSgUnVr7hxklXgFqUKBxqDy1RvGLI24QiapMV3PTdDPqEbBJJ+Ve6nhCWUTOuJdSxWNuPGze... | BANMo- Building Animatable 3D Neural Models from Many Casual Videos |
13
Real Sample
NeTI w/o PE
NeTI w/o Nested Dropout
NeTI
NeTI w/ Textual Bypass
“A photo
of S∗ in
the jungle”
“A photo
of S∗”
“An oil
painting of
S∗”
“A photo of
S∗ on
the beach”
Figure 14. Ablation study. We compare our NeTI models trained without our positional encoding function, without Nested Dropout ... | A Neural Space-Time Representation for Text-to-Image Personalization |
5.1. Evaluations and Comparisons
Evaluation Setup. We evaluate NeTI with respect
to state-of-the-art
inversion methods (Textual Inversion
(TI) [9], Extended Textual Inversion (XTI) [41]) and
fine-tuning approaches (DreamBooth [32], CustomDiffu-
sion [14]). We consider 10 concepts taken from TI and 6
concepts from Custo... | A Neural Space-Time Representation for Text-to-Image Personalization |
Sharan Narang, Logan Feistel, Erich Elsen Undersander,
Cindy Song, and Gregory Diamos. 2022. Firefly:
A lightweight system for running multi-billion pa-
rameter models on commodity hardware. In 2022
ACM/IEEE 49th Annual International Symposium
on Computer Architecture (ISCA), pages 757–771.
IEEE.
Duc Nien Hoang, Minsi... | LLM in a flash |
17
we have that the optimal welfare Wela∗(b−(cid:96),˜v(cid:96))(b−(cid:96), ˜v(cid:96)) ≤ h(cid:96) at the feasible region. Minimizing
h(cid:96) over all ˜v(cid:96) ∈ V (cid:96) gives the first term in m(cid:96)(b). To complete m(cid:96)(b)’s computation, subtract
Wela∗(b)(b−(cid:96), 0) = F|a∗(b) · b−(cid:96) − ψ(a∗... | Incomplete Information VCG Contracts for Common Agency |
Rafal Jozefowicz, Oriol Vinyals, Mike Schuster, Noam Shazeer, and Yonghui Wu. Exploring the
limits of language modeling. arXiv preprint arXiv:1602.02410, 2016. (cited on p. 4)
Eirini Kalliamvakou. Research: quantifying GitHub Copilot’s impact on developer productivity and
happiness. https://github.blog/2022-09-07-res... | StarCoder_paper (1) |
are the emotions in audio 1 and audio 2, respectively?The tone of the woman in audio 1 is cheerful, possibly talking about something pleasant. The tone of the woman in audio 2 is fearful, possibly discussing something terrifying.How to respond to the questions in audio 1 and audio 2?In audio 1, when the woman asks if y... | Qwen-Audio |
The obtained experience pool and knowledge pool will be further utilized by MLCopilot in the online
stage, to conduct reasonable, promising, and competitive ML solutions for novel tasks. | MLCopilot- Unleashing the Power of Large Language Models in Solving Machine Learning Tasks |
One is to use campaign committee filings with the FEC. In reporting their
spending, campaign committees must identify a purpose for each expenditure.
Unfortunately, campaigns are not required to use consistent categories, and so it
can be a herculean (and potentially error-prone) task to sort through and
identify whethe... | Social_Media_and_Democracy |
Where a flow can be seen as either a temporal instance or a thematic instance.
Starting from these core elements we will aim to define a novel framework capable of supporting
human understanding and agency within the human-AI dialogue dynamic and its characteristics, with
special focus on the specific context of ho... | informatics-phd-projects-2022-23 |
[119] LMSYS. 2023. Chatbot Arena: Benchmarking LLMs in the Wild with Elo Ratings. https://lmsys.org.
[120] Alejandro Lopez-Lira and Yuehua Tang. 2023. Can chatgpt forecast stock price movements? Return predictability
and large language models. arXiv preprint arXiv:2304.07619 (2023).
[121] Chenyang Lyu, Jitao Xu, and ... | ASurveyonEvaluationofLargeLanguageModels |
Video Generation
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classification problems (Vaswani et al., 2017; Radford et al.,
2018; Devlin et al., 2018). We consider the standard Trans-
former architecture, as proposed in Vaswani et al. (2017).
Adapter modules present many architectural choices. We
provide a simple design that attains good performance. We
experimented with a number... | Parameter-Efficient Transfer Learning for NLP |
models trained on labeled data, even on highly competitive benchmarks like ImageNet
[Tomasev et al., 2022, He et al., 2020a, Deng et al., 2009]. SSL has also been successfully
applied across other modalities such as video, audio, and time series [Wickstrøm et al.,
2022, Liu et al., 2022a, Schiappa et al., 2022a]. | A Cookbook of Self-Supervised Learning |
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8
Fig. 8. Our results on natural images. From left to right: the 1st is the input images, the 2nd and 3rd column show the SMPL models estimated by our
method (network inference + optimization), the 4th to 6th demonstrates our geometry reconstruction results, and the last three column demonstrates
the texture inference... | PaMIR- Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction |
to the exploration progress and the agent’s current state (Fig. 3). As VOYAGER progresses to harder
self-driven goals, it naturally learns a variety of skills, such as “mining a diamond”.
The input prompt to GPT-4 consists of several components:
(1) Directives encouraging diverse behaviors and imposing constraints,
s... | VOYAGER- An Open-Ended Embodied Agent with Large Language Models |
4 Experiment
In this section, we introduce the datasets used in this paper and present the experimental results that
demonstrate the advantages of our methods from a comparative perspective.
4.1 Datasets and Evaluation Metrics | Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models |
black boxes . The back normal maps are for reference.
(b) Examples of perceptual preference on back normal maps. Unanimously preferred results are in
black boxes . The front normal maps are for reference.
Figure 13. Qualitative results to evaluate the effect of body prior for normal prediction on in-the-wild images.... | ICON |
On the individual level, qualitative research suggests that Muslims living in
the West who are targeted by online hate speech fear that online threats may
materialize offline (Awan and Zempi 2015). Furthermore, surveys of adolescent
internet users have found that large numbers of African American respondents
have experi... | Social_Media_and_Democracy |
5.2. Primary Results: Aligning Diffusion Models
First, we show that the outputs of the Diffusion-DPO-
finetuned SDXL model are significantly preferred over the
baseline SDXL-base model. In the Partiprompt evaluation
(Fig. 3-top left), DPO-SDXL is preferred 70.0% of the time
for General Preference (Q1), and obtains a si... | DiffusionModelAlignmentUsing Direct Preference Optimization |
• Auto-encoding Models: Auto-encoding Models have garnered significant attention in the
domain of self-supervised learning, particularly Autoencoders (AEs) and Variational Autoen-
coders (VAEs). AEs consist of an encoder and a decoder that work together to reconstruct
input while disregarding less important details, pr... | AReviewofDeepLearningTechniquesforSpeechProcessing |
2 Dataset
We introduce MozArt, a four-way multilingual
cloze test dataset with annotator demographics.
We sampled 100 sentence quadruples from each
of the four languages (English, French, German,
Spanish) in the corpus provided for the WMT 2006
Shared Task.4 The data was extracted from the
publicly available Europarl ... | Are Pretrained Multilingual Models Equally Fair Across Languages? |
We conclude that MultiHashEmbed performs head to head with MultiEmbed, highlighting
the validity of the use of hash embeddings. We also found that having more or fewer rows as | MULTI HASH EMBEDDINGS IN SPACY |
biases in sentence encoders. arXiv preprint arXiv:1903.10561, 2019.
[67] Maarten Sap, Dallas Card, Saadia Gabriel, Yejin Choi, and Noah A. Smith. The risk of racial bias in hate speech
detection. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019.
[68] Shikha Bordia and Sa... | LaMDA- Language Models for Dialog Applications |
Subjective Evaluation. Following Liu et al. [17] and Kreuk et al. [16], we ask six human evalua-
tors to assess two aspects –– overall audio quality (OVL) and relevance to the input text (REL) – of
30 randomly-selected baseline- and TANGO-generated audio samples on a scale from 1 to 100. The
evaluators were proficient i... | Text-to-Audio Generation using Instruction-Tuned LLM and Latent Diffusion Model |
44.0*
67.0*
53.0
61.1
81.8
75.1
5We denote the initial round as round 0, whereas Du et al. (2023) refers to it as round 1. The standard
deviation for Standard Prompting over 9 runs is 0.91.
7
Large Language Models Cannot Self-Correct Reasoning Yet
the model
to include all
instead of asking the model
the concep... | LARGELANGUAGEMODELSCANNOTSELF-CORRECT REASONINGYET |
https://doi.org/10.1017/9781108890960 Published online by Cambridge University Press
Democratic Creative Destruction?
153
more politically balanced news repertoires than people who do not use search
engines for news (Fletcher and Nielsen 2018b).
This does not mean that echo chambers and filter bubbles do not exist ... | Social_Media_and_Democracy |
Generative Models Generative models aim to
learn a lower-dimension representation space, and
then reconstruct to the high-dimension space con-
ditioning on the given information (Rombach et al.,
2022; Yang et al., 2022; Kreuk et al., 2022; Ho
et al., 2022). Some effective methods earlier in-
clude auto-encoding (Hinton... | Moûsai |
of Yang et al. (2021) that unequal load balance may not significantly impact model quality. | ST-MOE- DESIGNING STABLE AND TRANSFERABLE SPARSE EXPERT MODELS |
Ferrara, E., Varol, O., Davis, C., Menczer, F., & Flammini, A. (2016). The rise of social
the ACM, 59(7), 96–104. https://doi.org/10.1145
bots. Communications of
/2818717
Følstad, A., Brandtzaeg, P. B., Feltwell, T., Law, E. L. C., Tscheligi, M., & Luger, E.
(2018). Chatbots for social good. Extended Abstracts of the... | Social_Media_and_Democracy |
layer’s execution, tokens meant to be processed by a specific expert are routed to the corresponding
GPU for processing, and the expert’s output is returned to the original token location. Note that EP
introduces challenges in load balancing, as it is essential to distribute the workload evenly across the
GPUs to preve... | Mixtral of Experts paper |
(a) MNIST. These distilled images unknown random initializations to 79.50% ± 8.08% test accuracy.
(b) CIFAR10. These distilled images unknown random initializations to 36.79% ± 1.18% test accuracy.
Figure 3: Distilled images trained for random initialization with ten GD steps and three epochs (100
images in total). W... | DATASET DISTILLATION |
agents are more likely to “give up” a given type of power once they are “done with it.”
106Thanks to Rohin Shah, Paul Christiano, and Carl Shulman for discussion. And note that a given operational-
ization of “time” can itself be vulnerable to various forms of manipulation (an AI could, for example, find ways
to stop a... | Is Power-Seeking AI an Existential Risk? |
Core Contributors
Ajay Kannan
Ming-Wei Chang
Axel Stjerngren
Josip Djolonga
Yuting Sun
Ankur Bapna
Matthew Aitchison
Pedram Pejman
Henryk Michalewski
Tianhe Yu
Cindy Wang
Juliette Love
Junwhan Ahn
Dawn Bloxwich
Kehang Han
Peter Humphreys
Thibault Sellam
James Bradbury
Varun Godbole
Sina Samangooei
Bogdan Damoc
Alex Kas... | gemini_1_report |
from a source the believers
In Chapter 9, the first of the “policy” or “reform” chapters, Stanford
professor Francis Fukuyama and Andrew Grotto, director of the Stanford
Program on Geopolitics, Technology, and Governance, focus on how
different countries regulate legacy media, with an eye to how they might
regulate the... | Social_Media_and_Democracy |
6.3 Scalable Tuning
Large Language Models trained on massive and varied datasets have demonstrated remarkable general problem-solving
capabilities. However, their performance can be significantly enhanced for specific domains or tasks through targeted
19
Efficient LLM Algorithmic Survey, Nov, 2023, USA.
Ding, Chen,... | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
The main aim of this PhD project will be to develop model-based AI techniques for representing,
analysing and reasoning about the security and safety of both the technical components of a CPS
(control, computation, communication) and its social components (e.g., user interaction processes and
user behavior) together... | informatics-phd-projects-2022-23 |
3.2 Fact Detection & Memorization
Fact detection increases the task difficulty by moving the fact to a random position in the input
(Figure 4, middle). This requires the model to first distinguish the fact from irrelevant text, write it to
memory, and later use it to answer the question located at the end.
3.3 Reasonin... | Scaling Transformer to 1M tokens and beyond with RMT |
CoT
41.2
78.4
86.8
91.6
11.2
10.8
12.4
26.8
14.8
36.4
14.8
49.6
22.8
50.8
18.8
34.0
48.0
46.0
57.2
56.8
74.4
82.4
74.8
78.0
Direct CoT
18.4 33.2
65.6 62.8
82.0 58.8
75.2 68.4
1.6
22.4
0.8
13.2
28.0
2.4
13.2 10.4
27.6
0.4
21.2 21.6
26.0
0.8
33.6 26.0
20.8
0.0
53.6 35.6
21.2 24.4
36.8 32.0
50.4 54.0
63.6 54.8
51.6 60.4
... | Scaling Instruction-Finetuned Language Models |
with UCL regulations, a qualifying examination need not necessarily be restricted to a formal
written examination. The structure of a qualifying year is determined by the admitting
Department having regard to the candidate's academic background and subject to the approval
of the Director of Access and Admissions. Al... | UCL Academic Manual |
Our PM-VLN module modulates transformer-based en-
coder embeddings in the main task ϕV LN using a hierarchi-
cal process of operations and leveraging prior learning on
auxiliary tasks (ϕ1, ϕ2) (see Figure 3). In order to priori-
tise relevant information, a training strategy for PM-VLN
components is designed where trai... | APriorityMapforVision-and-LanguageNavigation withTrajectoryPlansandFeature-LocationCues |
static test set. The PMs are trained to predict crowdworker behavior, so PM-Crowdworker agreement is best.
However, the largest PM actually agrees with the authors (i.e. Anthropic researchers) slightly more than the
authors agree with crowdworkers on labels. We also suspect this is a poor subsample of the data, since P... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
Generative Agents
arXiv, April, 2023, | Generative Agents- Interactive Simulacra of Human Behavior |
1. LLMs predictably get more capable with
increasing investment, even without
targeted innovation | Eight Things to Know about Large Language Models |
Parametric knowledge bias. Pre-training of models on a large corpus is known to result in the
model memorizing knowledge in its parameters [121, 142, 158]. This so-called parametric knowledge
helps improve the performance of downstream tasks, but also serves as another contributor to
hallucinatory generation. Large pre... | SurveyofHallucinationinNatural Language Generation |
4.2. Where to use KGs
KG explainability can be leveraged at different stages in the AI development pipeline [4]. KG explainability is usually
performed before (pre-modelling explainability), during (explainable modelling), or after (post-modelling explainability)
the AI modelling stage [14]. | Knowledge-graph-based explainable AI- A systematic review |
in natural language format, appending historical records to each subsequent input. As these records
expand, they might surpass the constraints of the Transformer architecture that most LLM-based
agents rely on. When this occurs, the system might truncate some content. The second challenge is
the difficulty in extractin... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
Rubinstein (2014).
68 See Rubinstein (2014), pp. 919–921; see also Executive Office of the President (2014), which
advocates for approaches that give individuals the ability to “participate in the use and distribu-
tion of his or her information after it is collected.”
69 See Rubinstein (2014), p. 913.
https://doi.or... | Social_Media_and_Democracy |
that at t = 0, we generate synthetic data ˜X(0) ∼(cid:81)d
to ˜X(t+1) ∼(cid:81)d
j=1 P (Xj), which becomes input to the discriminator f (0)
j=1 P (Xj|θ(t)
proceed to train a new discriminator and repeat the process.
Let P ∗ be the target distribution and P (t) the synthetic distribution at round t. For all t ≥ 1, th... | Adversarial Random Forests for Density Estimation and Generative Modeling |
understanding of medical background knowledge. In contrast, the original LLaMA tends to
introduce irrelevant content in its output. As GPT-4 notes, the rest of the text is less clear
and informative compared to the PMC-LLaMA output ”and the response goes on to discuss
PCT concentrations, which are not directly related ... | PMC-LLaMA- Further Finetuning LLaMA on Medical Papers |
he travels 3 hours. 3 hours at 10 mph means he travels 3 * 10 = 30 miles. He then travels back at 6 mph. This means he travels 6 miles per hour. He has to travel 30 miles, so it takes him 30 / 6 = 5 hours. The answer is 5. (Correct)Stephen placed an online order for groceries. His final bill came to $40.00. Because thi... | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models |
Democratic Transparency in the Platform Society
305
the difficult politics of contemporary content moderation,
it may not
significantly increase the transparency of Facebook’s actual practices. The
Oversight Board could even become a “transparency proxy” of sorts,
attracting public attention while remaining little more... | Social_Media_and_Democracy |
model robustness. Once trained, retrieval-enhanced models
based on pure pre-training eliminate the need for external li- | Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey |
where N is the number of samples and τi is the free-flight
probability that a photon travels between the camera center
j=1 pj(1−pi). Here
pi = exp (−σiδi) is the probability that the photon is trans-
mitted through the interval δi between the i-th sample and
the next. Color ci and density σi are computed by Eq. 1-2.
We... | BANMo- Building Animatable 3D Neural Models from Many Casual Videos |
Cash Flows and Shares
Operating cash flow -- trailing twelve months (TTM)
Operating cash flow -- TTM Y/Y growth (decline)
Purchases of property and equipment, net of proceeds from sales and
incentives -- TTM
Principal repayments of finance leases -- TTM
Principal repayments of financing obligations -- TTM
Equipment ac... | AMZN-Q3-2023-Earnings-Release |
ALFWorld | Tool Learning with Foundation Models |
Then, we also set up three human evaluations, all
on a scale of 1 (the worst) to 5 (the best). First,
we let human annotators to assess the authentic-
ity/fidelity of the generated music via a music Tur-
ing test (Goel et al., 2022; Hawthorne et al., 2019b;
Hyun et al., 2022). Specifically, we ask the an-
notators to l... | Moûsai |
question as to research access and privacy is not whether user data should be
analyzed for insights, but whether the platforms should have a monopoly on
such access or inquiry. | Social_Media_and_Democracy |
Ilya Loshchilov and Frank Hutter. 2019. Decoupled
In International Con-
weight decay regularization.
ference on Learning Representations.
H. Brendan McMahan, Eider Moore, Daniel Ramage,
and Blaise Ag¨uera y Arcas. 2016. Federated learn-
ing of deep networks using model averaging. Pro-
ceedings of the 20 th Internatio... | Prefix-Tuning |
uncertainty for each response (Wang et al., 2022). However, universal self-consistency has not
yet been developed to include the confidence estimation. We consider developing a calibration
mechanism for USC as future work, where we can leverage the LLM to perform output clustering
and pairwise self-consistency.
Also, U... | UNIVERSALSELF-CONSISTENCYFORLARGELANGUAGEMODELGENERATION |
However, since ChatGPT is not open-sourced and its access is controlled by a private company, most
of its technical details remain unknown. Despite the claim that it follows the procedure introduced in
InstructGPT (also called GPT-3.5) (Ouyang et al., 2022b), its exact architecture, pre-training data
and fine-tuning da... | ChatGPT’sOne-yearAnniversary-AreOpen-Source LargeLanguageModelsCatchingup |
Large language models (LLMs) (Brown et al., 2020; OpenAI, 2022, 2023; Chowdhery et al., 2022; Anil et al.,
2023; Touvron et al., 2023a,c; Qwen, 2023) have greatly propelled advancements in the field of general
artificial intelligence (AGI) due to their strong knowledge retention, complex reasoning and problem-solving
c... | Qwen-Audio |
Programming is a powerful and ubiquitous problem-solving tool. Developing systems that can assist pro-
grammers or even generate programs independently could make programming more productive and
accessible, yet so far incorporating innovations in AI has proven challenging. Recent large-scale lan-
guage models have demo... | alphacode |
We found that GPT-4-early and GPT-4-launch exhibit many of the same limitations as earlier
language models, such as producing societal biased and unreliable content. Prior to our mitigations
being put in place, we also found that GPT-4-early presented increased risks in areas such as finding
websites selling illegal goo... | gpt-4-system-card |
Table 3: Unlikelihood samples from TL;DR prompts sampled at temperature 1.0. In general, we find unlikelihood
fails to generate meaningful responses for more complex problems such as summarization and dialogue.
or dialogue experiment because it produces generally meaningless responses, which we believe is a
result of ... | Direct Preference Optimization |
6
User:Mycurrenttaskis, but I have never accomplished this task before. What relatedtasks might be helpful for me to complete ?Assistant:reasoning stopswoodenpickaxe312141stonepickaxe…1131Multi-ModalMemoryinitialquery (text)EnchantingTableObsidianDiamondBookDiamondPickaxeLeatherPaperDiamondIronPickaxenot inmemoryinmem... | JARVIS-1 |
25
Preprint
Name
Modified Transformer
DeepNarrow (12 Layers)
DeepNarrow (24 Layers)
E = 128
FFN every 2 blocks
FFN every 3 blocks
FFN every 4 blocks
H = 512
H = 1024
4 Layers
6 Layers
8 Layers
10 Layers
12 Layers
18 Layers
24 Layers
Recurrent (1-12)
Recurrent (2-6)
Recurrent (3-4)
Recurrent (4-3)
BERT-Tiny Variant
BE... | CRAMMING-TRAININGALANGUAGEMODELONA SINGLEGPUINONEDAY |
CREATE TABLE host (
host_id number ,
name text ,
nationality text ,
age number ,
primary key ( host_id )
)
insert into host (host_id, name, nationality, age) values (1,"Austin Daye","
United States",43);
Translate the following question into SQL.
Question: Show the name and the nationality of the oldest host.
SQL: S... | Teaching Large Language Models to Self-Debug |
Adversarial Random Forests
in tabular settings, and performs well on small and large
datasets using the computational resources of a standard
laptop. It compares favorably with deep learning models
while executing some 100 times faster on average. It is more
accurate than leading PCs, although it enjoys all the same
t... | Adversarial Random Forests for Density Estimation and Generative Modeling |
Alice, Bob, and Claire are playing a game. At
the start of the game, they are each holding a
ball: Alice has a orange ball, Bob has a white
ball, and Claire has a blue ball...At the end of
the game, Alice has the?
Reason: Novel scenarios; state tracking abili-
ties necessary.
Sammy wanted to go to where the people were... | AreEmergentAbilitiesinLarge Language Models just In-Context |
result = ((result + 7) % 7)
return result
The Python translation does not do the same thing as the C++ code. These are
the results of one failed unit test that tests whether the Python translation’s
outputs match the C++ program’s outputs:
Failed: assert remainder_7_large_numbers(’K’) == 6
Actual Result: Python runti... | Teaching Large Language Models to Self-Debug |
In recent years, there have been several quality assessment frameworks developed to estimate
speech quality, such as NORESQA [369] based on non-matching reference (NMR). NORESQA takes
inspiration from the human ability to assess speech quality even when the content is non-matching.
Additionally, NORESQA introduces two ... | AReviewofDeepLearningTechniquesforSpeechProcessing |
f.write(link[’href’].split(’/’)[-1] + ’\n’)
1 def processing_odai(odai, page):
’’’ The core code for step (2) Collecting Oogiri data samples
Args:
odai (str): question ID, e.g. 6902364
page (int): page number of question URL, e.g. 1
’’’
url = f’https://bokete.jp/odai/{odai}?page={page}’
print(’processing’, url)
# ... | Let’sThinkOutsidetheBox |
finetuning with cross-entropy loss (supervised learning) without reinforcement learning, even for
datasets that include human judgments of different responses. For datasets that have a clear distinction
between instruction and response, we finetune only on the response (see ablations in Appendix B).
For OASST1 and HH-R... | QLORA |
In International Conference on Machine Learning. PMLR, 26193–26205.
[639] Biao Zhang, Ivan Titov, Barry Haddow, and Rico Sennrich. 2020. Adaptive feature selection for end-to-end speech
[640] Chunlei Zhang and Kazuhito Koishida. 2017. End-to-end text-independent speaker verification with triplet loss on
translation.... | AReviewofDeepLearningTechniquesforSpeechProcessing |
entrusted with their
services
the
While transparency may seem intuitive as a high-level concept (often
conceived narrowly as the disclosure of certain information that may not
previously have been visible or publicly available; see Albu and Flyverbom
2016), critical scholarship has long noted that a major reason for... | Social_Media_and_Democracy |
6 Discussion
We have explored chain-of-thought prompting as a simple mechanism for eliciting multi-step rea-
soning behavior in large language models. We first saw that chain-of-thought prompting improves
performance by a large margin on arithmetic reasoning, yielding improvements that are much stronger
than ablations ... | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models |
demonstrate this, we introduce three novel methods for leveraging frozen models:
input-dependent prompt tuning, frozen readers, and recursive LMs, each of which
vastly improves on current frozen-model approaches. Indeed, some of our methods
even outperform fine-tuning approaches in domains currently dominated by the
lat... | STANDING ON THE SHOULDERS OF GIANT FROZEN LANGUAGE MODELS |
One way to frame this discussion in NLP is to | A Two-Sided Discussion of Preregistration of NLP Research |
208
Francis Fukuyama & Andrew Grotto
Martin Lipset, among others, has noted that the American state modernized
later than did the state in other advanced societies, was less extensive, and
achieved a lower degree of professionalization (Lipset 1995). American
political culture remains highly suspicious of concentrate... | Social_Media_and_Democracy |
pi(x)
(7)
(8)
(9)
constant as the number of experts varies since under uniform routing(cid:80)N
Since we seek uniform routing of the batch of tokens across the N experts, we desire both vectors
to have values of 1/N. The auxiliary loss of Equation 7 encourages uniform routing since it is
minimized under a uniform ... | ST-MOE- DESIGNING STABLE AND TRANSFERABLE SPARSE EXPERT MODELS |
4.1 EXPERIMENTAL SETUP
Datasets. We use two popular
mathematical reasoning bench-
marks:
(i) GSM8K [12] is a
dataset consisting of high-qual-
ity grade school math problems,
containing 7,473 training sam-
ples and 1,319 testing samples;
and (ii) MATH [21] dataset consists of high school math competition problems that s... | METAMATH |
The Fairness Doctrine continued to be controversial, especially among
conservatives. They believed that it was being used by the government to shut
down conservative voices and that the FCC could never be truly impartial in its
enforcement of the rule. By the 1980s, there was also a growing belief among
economists that... | Social_Media_and_Democracy |
Figure 5, demonstrates control over expressions and
poses by interpolating and extrapolating an example expres-
sion (first expression component in FLAME [35]), plus jaw
(pitch), and neck (yaw) poses separately. For each parame-
ter, we show generated images and the training data distri-
bution with 5 vertical lines cor... | I M Avatar- Implicit Morphable Head Avatars from Videos |
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 |
Image diffusion models learn to progressively denoise
images and generate samples from the training domain. The
denoising process can occur in pixel space or in a latent
space encoded from training data. Stable Diffusion uses
latent images as the training domain as working in this space
has been shown to stabilize the ... | AddingConditionalControltoText-to-ImageDiffusionModels |
art using AI technologies, in the last few years GAN-based approaches were dominating the AI Art scene. Recently, | UNDERSTANDINGANDCREATINGARTWITHAI-REVIEWAND OUTLOOK |
40
Gemini: A Family of Highly Capable Multimodal Models
Contributors
Rupert Kemp
Sushant Kafle
Tanya Grunina
Alice Talbert
Abhimanyu Goyal
Diane Wu
Denese Owusu-Afriyie
Cosmo Du
Chloe Thornton
Jordi Pont-Tuset
Pradyumna Narayana
Jing Li
Saaber Fatehi
John Wieting
Omar Ajmeri
Benigno Uria
Tao Zhu
Yeongil Ko
Laura Kni... | gemini_1_report |
4.1 Chunking and Alignment
Chunking the claim into spans is conducted us-
ing the chunker of Akbik et al. (2019), and any
span that does not contain any content words is
merged with its subsequent span. Next, as shown
in Figure 4, a word aligner (Jalili Sabet et al.,
2020) aligns each evidence sentence in the in-
put s... | ProoFVer- Natural Logic Theorem Proving for Fact Verification |
where xi is the training data, pi is the positive sample, and
nj is the negative sample,sim(x,y) is to calculate the simi-
larity between x and y. Another study has chosen to further
streamline the quantity of documents, aiming to enhance the
model’s answer accuracy by reducing the number of retrieved
documents. [Ma et... | Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey |
23
1024-dimensional Transformer embedding, 2048-dimensional feed-forward layer, 8 attention heads)
for 150k steps with an effective batch size of 120k frames. These models are evaluated on the
cross-sentence zero-shot TTS setup (Section 5.2) and diverse speech sampling (Section 5.5).
Results in Table B2 show that whi... | Voicebox-Text-GuidedMultilingual UniversalSpeechGenerationatScale |
"Isabella Rodriguez is checking her emails" appears as
. The
full natural language description of the action can be accessed by
clicking on the agent avatar. | Generative Agents- Interactive Simulacra of Human Behavior |
We examine the trade-off between the metrics of interest (WER, SIM, FSD) for different settings of
guidance strength (α) and NFE specified by the user. Fig. 2a shows the Voicebox inference time to
generate an audio sample of 10 seconds (including vocoding and predicting duration) as NFE varies
and compares that to VALL... | Voicebox-Text-GuidedMultilingual UniversalSpeechGenerationatScale |
2.6.2 LARGE GUIDANCE WEIGHTS | IMAGEN VIDEO- HIGH DEFINITION VIDEO GENERATION WITH DIFFUSION MODELS |
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