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The basic building block of a 1D CNN is the convolutional layer, which applies a set of filters to
the input data. A convolutional layer employs a collection of adjustable parameters called filters
to carry out convolution operations on the input data, resulting in a set of feature maps as the
output, which represent t... | AReviewofDeepLearningTechniquesforSpeechProcessing |
The chapter proceeds in three parts: We first provide a brief historical
overview of transparency in democratic governance, tracing it from its origins
in Enlightenment-era liberalism all the way to its widespread adoption in the
twentieth and twenty-first centuries. We quickly summarize what transparency
https://doi.or... | Social_Media_and_Democracy |
• System design. Focusing on system-level considerations, this category covers
Deployment Optimization and Support Infrastructure, among others. It explores
hardware and system optimizations that are essential for improving the practical
performance of LLMs.
Through this taxonomy, we aim to facilitate a structured and... | Beyond Efficiency |
than any speech or incentive package could match. “Now I hear that other companies are using this trial basis recruiting,” says
Srinivas. | 4 Trends for AI Startups and Generative AI Companies |
computing 5 (2001), 4–7.
[26] Kevin Dill and L Martin. 2011. A Game AI Approach to Autonomous Con-
trol of Virtual Characters. In Proceedings of the Interservice/Industry Training,
Simulation, and Education Conference (I/ITSEC’11). Orlando, FL, USA.
[27] David Easley and Jon Kleinberg. 2010. Networks, crowds, and mar... | Generative Agents- Interactive Simulacra of Human Behavior |
https://doi.org/10.1017/9781108890960 Published online by Cambridge University Press
82
Alexandra A. Siegel
Fortuna, P. C. T. (2017). Automatic detection of hate speech in text: an overview of the
topic and dataset annotation with hierarchical classes. U.Porto. https://repositorio-
aberto.up.pt/handle/10216/106028
... | Social_Media_and_Democracy |
(b) Closest friend
(c) 4th closest friend
(d) 8th closest friend
Figure 5: (a) coactivation of the neurons of 10th layer with one random neuron in the same layer shows a few neurons
are highly coactivated with it and can be grouped (b) closest friend of a neuron is the one that gets coactivated
with it the most, coa... | LLM in a flash |
Nash equilibrium of the game. However, AlphaZero required access to a perfect simulator of the
environment in order to perform MCTS. AlphaZero’s followup work, MuZero (Schrittwieser et al.,
2020), removed the requirement of direct access to a simulator by learning a model of the world, but
MuZero still required continu... | CHAIN-OF-THOUGHTREASONING IS APOLICY IMPROVEMENTOPERATOR |
regardingstrategicsalesplanning,pastandopensales,buyersrisk
assessment,andpastdemand.
• Complementarydatasources:sourcesofdatathatprovidevalu-
ablecomplementarydatatotraindemandforecastingmodels.
• Media Event Retrieval System: is a system that keeps track
of events reported in the media. Media Event Retrieval System
u... | Knowledge-graph-based-rich-and-confidentiality-preserving-Ex_2022_Informatio |
LLM Powered Autonomous Agents | Lil'Log
https://lilianweng.github.io/posts/2023-06-23-agent/
10/22 | LLM Powered Autonomous Agents _ Lil'Log |
In this report, we examine patterns and trends in data and AI adoption across more
than 9,000 global Databricks customers. By unifying business intelligence (BI) and AI
applications across companies’ entire data estates, the Databricks Lakehouse provides
a unique vantage point into the state of data and AI, includin... | databrick 2023 report |
2.2 ERROR AVALANCHING | CHAIN-OF-THOUGHTREASONING IS APOLICY IMPROVEMENTOPERATOR |
Engineering, Mathematics and Computer Science.
Additional information
If you would like more information about this role, please contact professor Catholijn
Jonker, c.m.jonker@tudelft.nl.
Application procedure
The applications for this post should be submitted via the application button before
December 10, 2023.
Submit... | Job details - TU |
language models [11, 45, 79]. Architectures were given equivalent
access to all memories accrued by the agent up until the moment | Generative Agents- Interactive Simulacra of Human Behavior |
of knowledge, requiring different processing methods. They
primarily fall into three categories: unstructured data, struc-
tured data, and content generated by LLMs.
Augmented with Unstructured Data
Unstructured data mainly encompasses textual data , typi-
cally derived from pure text corpora. Additionally, other text
... | Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey |
To mitigate potential misuses in this area, we have trained models to refuse malicious cybersecurity
requests, and scaled our internal safety systems, including in monitoring, detection and response.
Below is an example that demonstrates the model’s dual-use capability of finding code vulnera-
bilities: | gpt-4-system-card |
Anthony is reading a book. When he is done, Anthony puts the book on the table. Anthony
leaves the room. Sonya comes in and moves the book from the table to a drawer. Then Sonya
leaves. Anthony comes back to read some more. Where will Anthony look for the book first?
Anthony will most likely look for the book in the dra... | LaMDA- Language Models for Dialog Applications |
News is thus intertwined with politics but also with the marketplace. In high-
income democracies, news is a public good overwhelmingly produced by
professional journalists working for private sector news media operating for-
profit. Historically, news production has been funded in a variety of ways,
including subsidies... | Social_Media_and_Democracy |
The services and materials provided by Boston Consulting Group (BCG) are subject to BCG’s Standard Terms (a copy of which is available upon request) or
such other agreement as may have been previously executed by BCG. BCG does not provide legal, accounting, or tax advice. The Client is responsible for
obtaining inde... | AI at Work- What People Are Saying |
sha1_base64="76w10YEtETzUXdaT0wTZt0xBig8=">AAAB9XicbVDLSgMxFL1TX7W+qi7dBIvgqsyIoMuCG5cV7EPaacmkmTY0kxmSO0oZ+h9uXCji1n9x59+YtrPQ1gOBwzn3ck9OkEhh0HW/ncLa+sbmVnG7tLO7t39QPjxqmjjVjDdYLGPdDqjhUijeQIGStxPNaRRI3grGNzO/9ci1EbG6x0nC/YgOlQgFo2ilXjeiOArCrD3tYV/0yxW36s5BVomXkwrkqPfLX91BzNKIK2SSGtPx3AT9jGoUTPJpqZsanlA2pkPesVTRiBs/m... | BANMo- Building Animatable 3D Neural Models from Many Casual Videos |
Nonetheless, endowing large language models with mul-
timodal generation capabilities presents a significant chal-
lenge, as it necessitates not only producing content that
aligns with user requirements but also generating it at the
opportune moment. Several recent studies have made pre-
liminary forays in this directi... | GPT4Video |
Nolan Dey, Gurpreet Gosal, Zhiming, Chen, Hemant
Khachane, William Marshall, Ribhu Pathria, Mar-
vin Tom, and Joel Hestness. 2023. Cerebras-gpt:
Open compute-optimal language models trained on
the cerebras wafer-scale cluster.
William B. Dolan and Chris Brockett. 2005. Automati-
cally constructing a corpus of sententi... | LaMini-LM- A Diverse Herd of Distilled Models from Large-Scale Instructions |
2020.
Jonathan Ho, Chitwan Saharia, William Chan, David J Fleet, Mohammad Norouzi, and Tim Sali-
mans. Cascaded diffusion models for high fidelity image generation. JMLR, 2022a.
Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David J.
Fleet. Video Diffusion Models. In arXiv:2204.0345... | IMAGEN VIDEO- HIGH DEFINITION VIDEO GENERATION WITH DIFFUSION MODELS |
2 Architectural details
Mixtral is based on a transformer architecture [31] and uses the same
modifications as described in [18], with the notable exceptions that Mix-
tral supports a fully dense context length of 32k tokens, and the feed-
forward blocks are replaced by Mixture-of-Expert layers (Section 2.1).
The model... | Mixtral of Experts paper |
D.1. Online Data Collection
We source Oogiri game data from the official Oogiri game platform, Bokete (https://bokete.jp), and other popular platforms,
such as Twitter (https://twitter.com) and Weibo (https://m.weibo.cn) which also host some Oogiri-game-alike data. Through
extensive data collection from different platf... | Let’sThinkOutsidetheBox |
29 | TheRiseandPotentialofLargeLanguageModel BasedAgents |
phonemes. The stochastic duration predictor is a flow-based
generative model that is typically trained via maximum like-
lihood estimation. The direct application of maximum likeli-
hood estimation, however, is difficult because the duration of
each input phoneme is 1) a discrete integer, which needs to
be dequantized fo... | ConditionalVariationalAutoencoderwithAdversarialLearningfor End-to-EndText-to-Speech |
TABLE 3. Values of the Spearman’s rank correlation coefficient between predicted aesthetic scores of three different CNN models and the average of
subjective scores for different properties on the JenAesthetic dataset (* p-value < 0.01, ** p-value > 0.1).
TABLE 4. Values of the spearman’s rank correlation coefficient ... | A_Deep_Learning_Perspective_on_Beauty_Sentiment_and_Remembrance_of_Art |
For quantitative evaluation, we focus on multiple inputs to image synthesis output since the evaluation
metric for this case (FID) does not require specific modality inputs like text. We test with several
input combinations including text + image, text + audio, image + audio, text + video, as well as three
inputs text +... | Any-to-Any Generation via Composable Diffusion |
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top-3
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64.8
64.9
65.4
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52.4
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top-5
67.6
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67.6
66.9
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CAMEL: Communicative Agents for “Mind”
Exploration of Large Scale Language Model Society
https://www.camel-ai.org
Guohao Li∗ Hasan Abed Al Kader Hammoud*
Hani Itani*
Dmitrii Khizbullin
King Abdullah University of Science and ... | CAMEL- Communicative Agents for “Mind” Exploration of Large Scale Language Model Society |
of these large models. This approach could draw from the insights of [236], who sur-
veyed computational intelligence-based optimization and game theory approaches for
resource allocation in computing environments. This could inform the development of
strategies for efficient resource utilization in LLMs. | Beyond Efficiency |
Self-Verification needs to rewrite the question with answer into a declarative statement, which is
challenging for complex questions. To address this issue, FOBAR [28] proposes to directly append
the answer to the question, i.e., “If we know the answer to the above question is {a⋆
i } , what is the
value of unknown var... | METAMATH |
7. LLMs need not express the values of their
creators nor the values encoded in web text
When a plain pretrained LLM produces text, that text will
generally resemble the text it was trained on. This includes
a resemblance in the values expressed by the text: Mod-
els mirror their training data in the explicit statement... | Eight Things to Know about Large Language Models |
Optimizing the RAG Pipeline
The optimization of the retrieval process aims to enhance the
efficiency and quality of information in RAG systems. Cur-
rent research focuses on integrating diverse search technolo-
gies, refining retrieval steps, incorporating cognitive back-
tracking, implementing versatile query strategi... | RAG forLargeLanguageModels-ASurvey |
[468] Gur, I., S. Yavuz, Y. Su, et al. Dialsql: Dialogue based structured query generation. In
I. Gurevych, Y. Miyao, eds., Proceedings of the 56th Annual Meeting of the Association for
Computational Linguistics, ACL 2018, Melbourne, Australia, July 15-20, 2018, Volume 1:
Long Papers, pages 1339–1349. Association for C... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
“A manga
drawing of S∗”
“A photo of
S∗ on a beach”
“A watercolor
painting of
S∗”
Figure 26. Additional qualitative comparisons. For each concept, we show four images generated by each method using the same set of
random seeds. Results for TI are obtained after 5,000 optimization steps while the remaining methods... | A Neural Space-Time Representation for Text-to-Image Personalization |
Our vision for FinGPT is to serve as a catalyst for stimulat-
ing innovation within the finance domain. FinGPT is not lim-
ited to providing technical contributions, but it also cultivates
an open-source ecosystem for FinLLMs, promoting real-time
processing and customized adaptation for users. By nurtur-
ing a robust c... | FinGPT-Open-SourceFinancialLargeLanguageModels |
hypernetworks for real image editing. 2021.
[2] O. Avrahami, D. Lischinski, and O. Fried. Blended diffusion for text-driven editing of nat-
ural images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern
Recognition, pages 18208–18218, 2022.
[3] T. Brooks, A. Holynski, and A. A. Efros. Instructp... | Adding Conditional Control to Text-to-Image Diffusion Models |
We also experimented with top-𝑘 (Fan et al., 2018) and nucleus sampling (Holtzman et al., 2019). As
seen in Figure A5, despite running exhaustive hyperparameter sweeps we did not observe significant
performance improvements with these methods. We therefore use regular sampling with temperature
in our experiments. A few... | alphacode |
alignment between sound perception and reasoning. SALMMON (Anonymous, 2023) utilizes both a text
encoder and a speech encoder to extract the representation from different kinds of audio and text input,
and then connects the inputs to a well-train LLM with Q-former (Li et al., 2023) style attention to generate
response.... | Qwen-Audio |
4.4.3 Predictive Models
In training predictive models, the primary concept involves creating simpler objectives or targets
to minimize the need for data generation. However, the most critical and difficult aspect is ensuring
that the task’s difficulty level is appropriate for the model to learn effectively. Predictive ... | AReviewofDeepLearningTechniquesforSpeechProcessing |
Qualitative Evaluation. First, in Figure 7, we demon-
strate NeTI’s ability to compose learned concepts in novel
compositions. These generations range from placing the
concepts in new scenes to capturing their key semantics and
forming new creations inspired by them. | A Neural Space-Time Representation for Text-to-Image Personalization |
Sold hundreds of millions of items worldwide during Prime Big Deal Days, Amazon’s largest two-day October
holiday kick-off event ever. Prime members across 19 countries saved more than $1 billion on millions of deals. On
the first day of the event, U.S. Prime members purchased more than 25 million items with Same-Day... | AMZN-Q3-2023-Earnings-Release |
5.2 NUMBER OF ROWS
The main motivation behind hash embeddings is to use a small amount of vectors and still achieve
good performance. We have already seen that on OntoNotes: MultiHashEmbed performs com-
parably to MultiEmbed even if it only has half of the number of vectors for NORM.
Here we go one step further and te... | MULTI HASH EMBEDDINGS IN SPACY |
Agents build individual tree representations of the environment
as they navigate it — subgraphs of the overall sandbox environment
tree. We initialize each agent with an environment tree capturing
the spaces and objects that the agent should be aware of: the rooms
and objects in their living quarters, their workplace, ... | Generative Agents- Interactive Simulacra of Human Behavior |
This scarcity of causes of action against individuals who perpetuate
disinformation should come as no surprise. The First Amendment heavily
limits regulations on the truth, falsity, or quality of information as content-
based restrictions. The Constitution “demands that content-based restrictions
on speech be presumed ... | Social_Media_and_Democracy |
2-Chat at every turn. We also categorized multi-turn prompts into the same five categories listed above.
Since it can be hard to categorize multi-turn prompts into a single category, annotators could select up to two
categories for multi-turn prompts. Example evaluation prompts can be seen in Table 33.
For open-source ... | Llama2 |
Jialu Wang, Yang Liu, and Xin Eric Wang. 2021. As-
sessing multilingual fairness in pre-trained multi-
modal representations.
Shijie Wu and Mark Dredze. 2020. Are all languages
created equal in multilingual BERT? In Proceedings
of the 5th Workshop on Representation Learning for
NLP, pages 120–130, Online. Association ... | Are Pretrained Multilingual Models Equally Fair Across Languages? |
follows: The agent’s utility is the sum of payments less cost of chosen action, i.e.,(cid:80)
ψ(a). Thus, the agent’s action x∗(b) given bid profile b maximizes his expected utility:7
(cid:88)
(cid:96)∈[n]
.
x∗(b) ∈ arg max
a∈A
Eo∼F|a[t(cid:96)(b, o)] − ψ(a)
We assume the principals know the agent is ratio... | Incomplete Information VCG Contracts for Common Agency |
was the most effective in reducing hostile comments. However, the authors
note that the fact that they do not observe a statistically significant effect of the
counter-speech treatment may be due to their small sample sizes and inability
to monitor repeated interactions over time in their experimental setup.
Together, t... | Social_Media_and_Democracy |
Your responsibilities
Designing and implementing research methodologies
Conducting in-depth literature reviews and critical analysis of relevant research
Analyzing and interpreting research findings and communicating results through publications and conference presentations
Writing the doctoral dissertation describing... | UZH_ PhD Position in Digital Humanities_ From Text to Image with AI |
− 1
x ΣxyΣ
These ideas were extended to deep learning in Deep Canonically Correlated Autoen-
coders (DCCAE) an autoencoder regularized via CCA. Hsieh [2000] and Andrew et al.
[2013] introduce the objective of jointly learning parameters for two networks, f1, f2, such
they their outputs are maximally correlated. The in... | A Cookbook of Self-Supervised Learning |
models on a single consumer GPU and 65B parameter models on a single professional GPU, while
not degrading performance relative to a full finetuning baseline. We have demonstrated that our
best 33B model trained on the Open Assistant dataset can rival ChatGPT on the Vicuna benchmark.
Since instruction finetuning is an ... | QLORA |
Chadwick, A. (2017). The Hybrid Media System: Politics and Power (2nd ed.).
New York: Oxford University Press.
Cook, T. E. (1998). Governing with the News: The News Media As a Political
Institution. Chicago: University of Chicago Press.
Couldry, N., Livingstone, S. M., & Markham, T. (2010). Media Consumption and
Pu... | Social_Media_and_Democracy |
2.21 Hacker News
Hacker News9 is a link aggregator operated by Y
Combinator, a startup incubator and investment
fund. Users submit articles defined as “anything
that gratifies one’s intellectual curiosity,” but sub-
mitted articles tend to focus on topics in computer
science and entrepreneurship. Users can comment
on sub... | The Pile- An 800GB Dataset of Diverse Text for Language Modeling |
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... | Principal-agent VCG contracts - ScienceDirect |
Acknowledgment
Our project would not have been made possible
without the GPU resources from various parties:
We thank Shehzaad Dhuliawala for helping us set
up GPU access at ETH Zürich. We thank Vincent
Berenz and Lidia Pavel for setting up our GPU ac-
cess at Max Planck Institute. We thank Stability
AI for their gene... | Moûsai |
[143] Heaven, W.D.: Why Meta’s
only
three
survived
https://www.technologyreview.com/2022/11/18/1063487/
meta-large-language-model-ai-only-survived-three-days-gpt-3-science/.
[Accessed 19-May-2023] (2022)
days
latest
model
online — technologyreview.com.
language
large
[144] Guo, B., Zhang, X., Wang, Z., Jiang, ... | PersonalityTraitsinLargeLanguageModels |
such degradation to the removal of tokens that the
original model finds informative. A large drop
in performance indicates that gradient attributions
successfully identify important tokens in the input.
We first use this method to select an optimal
gradient-attribution method and f function (Fig-
ure 11 in Appendix B).... | Measuring Association Between Labels and Free-Text Rationales |
The growing hostility toward media regulation was best illustrated by the
rise and fall of the FCC’s “Fairness Doctrine.” The latter had its origins in the
“public interest” provision of the Communications Act and was strengthened in
1959 when Congress amended the act’s Section 315 which had exempted
broadcasters from ... | Social_Media_and_Democracy |
2023 STATE OF DATA + AI
20
Migration trends:
the best data warehouse
is a Lakehouse
The Lakehouse Platform is an attractive
alternative to traditional data warehouses
because it supports advanced use cases and
DS/ML, allowing organizations to boost their
overall data strategy. As evidenced by the most
po... | 2023 state of ai databrick |
response to proposed new advertising
44–46
rules, 123
risks of influence over research, 324–325
role in misinformation correction, 184–186
as transnational communication
mechanisms, 99
as US based and inflexible in other
countries, 156
social media research
challenges and opportunities, 237, 313–320
data access c... | Social_Media_and_Democracy |
To the extent that legislative and regulatory action will seek to shape the
incentives that online platforms have to combat online political disinformation,
these efforts will take place in the shadow of CDA 230, which provides strong
protections shielding platforms from liability for actions taken by their users.
This... | Social_Media_and_Democracy |
AnchorPositiveNegativeDiscriminatorBilinearBilinear A Review of Deep Learning Techniques for Speech Processing
37
Table 3. Summary of contrastive self-supervised approaches and proposed models for speech processing with
associated metrics and training Data. ASR: Automatic Speech Recognition, PR: Phoneme Recognition. ... | AReviewofDeepLearningTechniquesforSpeechProcessing |
conclusions
Regulation of legacy media may seem to be irrelevant to contemporary
discussions of whether the Internet should be regulated in the interests of
protecting democracy,
the huge differences between the
technologies involved. Yet many of the older controversies remain the same
and may provide legal precedents... | Social_Media_and_Democracy |
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1
Introduction
Pre-trained neural language models have been shown to learn a substantial amount of in-depth knowl-
edge from data [47]. They can do so without any access to an external memory, as a parameterized
implicit knowledge base [51, 52]. While this development is exciting, such models do have down-
sides: The... | Retrieval-AugmentedGenerationfor Knowledge-IntensiveNLPTasks |
ate the dynamic curriculum and guide JARVIS-1 to learn
from the interactions with environments. In each round,
we prompt the MLM to consider how capable JARVIS-1
is at this point and subsequently select tasks from a task
pool to explore. We find that the curriculum almost fol-
lows the technical tree-growing direction.... | JARVIS-1 |
random crops of length 218 (∼5.5s at 48kHz), and
the text-conditional diffusion generation model on
fixed crops of length 221 (∼44s at 48kHz) encoded
in the 32-channels, 64x compressed latent represen-
tation. We use the AdamW optimizer (Loshchilov
and Hutter, 2019) with a learning rate of 10−4, β1
of 0.95, β2 of 0.999... | Moûsai |
In the specific case of ideological segregation, what do we know about the
impact of ranking algorithms? The answer is not much. We lack the type of
systematic evidence that would allow us to answer key questions in the field. An
explanation for this scarcity of evidence is researchers’ inability to access or
manipulate ... | Social_Media_and_Democracy |
2 RELATED WORK
Knowledge distillation. The main inspiration for this paper is network distillation (Hinton et al.,
2015), a widely used technique in ensemble learning (Radosavovic et al., 2018) and model compres-
sion (Ba & Caruana, 2014; Romero et al., 2015; Howard et al., 2017). While network distillation
2 | DATASET DISTILLATION |
Haipeng Luo, Qingfeng Sun, Can Xu, Pu Zhao, Jianguang Lou, Chongyang Tao, Xiubo Geng,
Qingwei Lin, Shifeng Chen, and Dongmei Zhang. Wizardmath: Empowering mathematical
reasoning for large language models via reinforced evol-instruct. arXiv preprint arXiv:2308.09583,
2023a.
Ziyang Luo, Can Xu, Pu Zhao, Xiubo Geng, Chon... | ChatGPT’sOne-yearAnniversary-AreOpen-Source LargeLanguageModelsCatchingup |
(2014), 7370.
In ICCC (2019), pp. 33–40.
[77] KIM, D., XU, J., ELGAMMAL, A., AND MAZZONE, M. Computational analysis of content in fine art paintings.
[78] KLINKE, H. The digital transformation of art history. In The Routledge Companion to Digital Humanities and
Art History. Routledge, 2020, pp. 32–42.
[79] LANG, S.... | UNDERSTANDINGANDCREATINGARTWITHAI-REVIEWAND OUTLOOK |
In contrast to the strengths of Transformer-based architectures in neural speech synthesis, large
language models based on Transformers such as BERT [109], GPT [444], XLNet [618], and T5
[448] have limitations when it comes to speech processing. One of the issues is that these models
require discrete tokens as input, n... | AReviewofDeepLearningTechniquesforSpeechProcessing |
… ,(0.30,12.26)]*****Chain-of-thoughts reasoning:*****- Notable objects: pedestrian at (0.80,18.81), moving to (-2.53,20.89) at 3.0 second- Potential effects: may collide if continue driving at this speed.…*****Task planning:*****Behavior: forward; Speed: deceleration; Driving plan: move forward with a deceleration****... | ALanguageAgentforAutonomousDriving |
International Conference on Automated Software Engineering, pages 317–326. IEEE, 2008.
N. Shazeer.
arXiv:1911.02150, 2019.
A. Solar-Lezama. Program synthesis by sketching. University of California, Berkeley, 2008.
N. Sussman. Falsehoods programmers believe about time. https://infiniteundo.com/post/
25326999628/falsehoo... | alphacode |
Although the 2016 Russian campaign has been a widely discussed example
of state-driven online disinformation, the use of these techniques is not new.
Researchers have tracked similar online disinformation campaigns launched by
Russia to influence political discourse throughout Central and Eastern Europe,
as well as the ... | Social_Media_and_Democracy |
9
Universal Self-Consistency for Large Language Model Generation
design task-specific criteria to further improve the performance, and we consider refining the USC
framework to further close the gap to the oracle performance as future work.
7 CONCLUSION
In this work, we presented Universal Self-Consistency (USC), ... | UNIVERSALSELF-CONSISTENCYFORLARGELANGUAGEMODELGENERATION |
B DOMAIN-ADAPTIVE PRE-TRAINING
Table 7 presents specifications of the pre-training corpora in each domain and Table 8 presents pre-
training hyper-parameters. A <pad> token is added to the model vocabulary for sentence padding.
In each domain, we explore different ratios for mixing reading comprehension data with gene... | ADAPTINGLARGELANGUAGEMODELSVIA READINGCOMPREHENSION |
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... | Principal-agent VCG contracts - ScienceDirect |
Figure 3: Pre-fill and chunking. During pre-fill of the cache, long sequences are chunked to limit memory
usage. We process a sequence in three chunks, “The cat sat on”, “the mat and saw”, “the dog go to”. The figure
shows what happens for the third chunk (“the dog go to”): it attends itself using a causal mask (rightm... | Mistral7B |
Mehrish et al.
3.5.1 Application
Seq2seq models have been used for speech processing tasks such as voice conversion [210, 528],
speech synthesis [210, 398, 399, 567, 583], and speech recognition. The field of ASR has seen sig-
nificant progress, with several advanced techniques emerging as popular options. These inclu... | AReviewofDeepLearningTechniquesforSpeechProcessing |
The execution of the SQL query above would return a table with 1 column. The
first column, "AVG(long)" would contain the average longitude. With "WHERE
id IN (SELECT station_id FROM status WHERE bikes_available <= 10)", the
table filters the records to only include stations with 10 or less bikes
available. So the SQL q... | Teaching Large Language Models to Self-Debug |
Appendix E. Note that the technical assistant has clear limitations: it sometimes proposes wrong
solutions, presents wrong facts, and can make offensive comments. | StarCoder_paper (1) |
11
variation in the domain weights during a DoReMi run seems to occur in the beginning of training
(Appendix Figure 8).
Choice of reference model. The choice of reference model can affect the domain weights found
by DoReMi. For example, iterated DoReMi (Section 3) improves performance by using a reference
model trai... | DoReMi- Optimizing Data Mixtures Speeds Up Language Model Pretraining |
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TA B L E O F C O N T E N T S
Olivia Moore is a partner on the consumer investing team, where she focuses on
investing in marketplace startups.
F O L L O W
X
Linkedin
M O R E F R O M T H I S C O N T R I B U T O R
Money on Autopilot: The F... | How Are Consumers Using Generative AI_ _ Andreessen Horowitz |
models. Here we provide our four templates for generating the instruction (Table 6), classifying whether
an instruction represents a classification task or not (Table 7), generating non-classification instances with
the input-first approach (Table 8), and generating classification instances with the output-first approach
(T... | SELF-INSTRUCT- Aligning Language Model with Self Generated Instructions |
Cardozo, N., Crocker, A., Gebhart, G., Lynch, J., Opsahl, K., & York, J. C. (2018). Who
Has Your Back? Censorship Edition 2018. Electronic Frontier Foundation report.
www.eff.org/who-has-your-back-2018
Christensen, L. T., & Cheney, G. (2014). Peering into transparency: Challenging ideals,
proxies, and organizational ... | Social_Media_and_Democracy |
4.2. Approaches compared
Retrieval-based Open-QA Most existing Open-QA sys-
tems answer the input question by first retrieving potentially
relevant documents from a knowledge corpus, and then us-
ing a reading comprehension system to extract an answer
from the documents. In this paradigm, the knowledge is
stored explici... | REALM |
ImageBind shows that image-paired data is sufficient to bind together these six
modalities. The model can interpret content more holistically, allowing the
different modalities to “talk” to each other and find links without observing
them together. For example, ImageBind can associate audio and text without
seeing them... | ImageBind_ Holistic AI learning across six modalities |
nation. Hail victory!2. Sick and tired of the globalist agenda erasing white identity. Standing up for my people and fighting back against cultural Marxism.3. White nationalist, ethno-nationalist, and unapologetically pro-white. We will not be replaced.GPT-4 (early)My apologies, but I won't be able to write that bio fo... | gpt-4-system-card |
Akhbardeh, F., Arkhangorodsky, A., Biesialska, M., Bojar, O., Chatterjee, R., Chaudhary, V., Costa-jussa, M. R.,
España-Bonet, C., Fan, A., Federmann, C., Freitag, M., Graham, Y., Grundkiewicz, R., Haddow, B., Harter, L.,
Heafield, K., Homan, C., Huck, M., Amponsah-Kaakyire, K., Kasai, J., Khashabi, D., Knight, K., Kocm... | PaLM 2 Technical Report |
Clark, C., Lee, K., Chang, M.-W., Kwiatkowski, T., Collins, M., and Toutanova, K. (2019). BoolQ:
Exploring the surprising difficulty of natural yes/no questions. In Proceedings of NAACL.
Clark, P., Cowhey, I., Etzioni, O., Khot, T., Sabharwal, A., Schoenick, C., and Tafjord, O. (2018).
Think you have solved question ... | TinyLlama |
standing by generative pre-training. 2018. 38, 39
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever, et al. Language models are
unsupervised multitask learners. OpenAI blog, 1(8):9, 2019. 38, 39
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell,
P. Mishkin, J. Clark, ... | A Cookbook of Self-Supervised Learning |
the OpenAI finetuning API7. We use the default
hyper-parameters, except that we set the prompt
lossweightto0,andwetrainthemodelfor2epochs.
We refer the readers to Appendix A.2 for additional
finetuning details. The resulting model is denoted
as GPT3SELF-INST.
5.2 Baselines
Off-the-shelf language models. WeevaluateT5-
LM (... | SELF-INSTRUCT- Aligning Language Model with Self Generated Instructions |
https://doi.org/10.1017/9781108890960 Published online by Cambridge University Press
Democratic Transparency in the Platform Society
301
footing (Freelon 2018).
It remains very difficult | Social_Media_and_Democracy |
Table 1 shows results for RAG along with state-of-the-art models. On all four open-domain QA
tasks, RAG sets a new state of the art (only on the T5-comparable split for TQA). RAG combines
the generation flexibility of the “closed-book” (parametric only) approaches and the performance of
"open-book" retrieval-based appro... | Retrieval-AugmentedGenerationfor Knowledge-IntensiveNLPTasks |
4. Related Work
Pre-trained text representations Pre-trained textual rep-
resentations are widely used to improve performance on
NLP tasks. These representations are trained on large cor-
pora (usually unsupervised), and fed as features to down-
stream models. In deep networks, these features may also be
fine-tuned on t... | Parameter-Efficient Transfer Learning for NLP |
isting studies, NExT-GPT [71] stands out as a notable
advancement: it is a multi-modal large language model
(MLLM) that excels in both understanding and genera-
tion tasks. NExT-GPT showcases several promising abil-
ities, such as image/video question answering, text to im-
age/video generation, audio understanding and... | M2UGen |
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