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better?
Example project: New interfaces and experiences to data engagement
The project will explore novel ways to present a dataset, for example a CSV file, using speech, audio
or video technologies. The aim is to propose an algorithm that given a dataset produces a media
summary of the content and context of th... | informatics-phd-projects-2022-23 |
encounters a race of evolved humanoids called Morlocks.
4. "Foundation" by Isaac Asimov - This novel is set in a galactic empire
and follows the story of a psychohistorian who tries to preserve knowledge
and culture after the empire collapses.
5. "The Forever War" by Joe Haldeman - This novel depicts a soldier who is
f... | Self-AlignmentwithInstructionBacktranslation |
anKautz,andBryanCatanzaro.Video-to-videosynthesis.arXivpreprintarXiv:1808.06601,2018.2,4[77]YaohuiWang,PiotrBilinski,FrancoisBremond,andAntitzaDantcheva.Imaginator:Conditionalspatio-temporalganforvideogeneration.InProceedingsoftheIEEE/CVFWin-terConferenceonApplicationsofComputerVision,pages1160–1169,2020.1,2,3,6,7[78]Y... | Conditional Image-to-Video Generation with Latent Flow Diffusion Models |
queries and documents. In the following subsections, we will
explore the introduction of the generator by delving into as-
pects of post-retrieval processing and fine-tuning.
5.1 Post-retrieval with Frozen LLM
In the realm of untunable LLMs , many studies rely on well-
established models like GPT-4 [OpenAI, 2023] to ha... | RAG forLargeLanguageModels-ASurvey |
well as attention on RNN (att-RNN). Another study used
RNNs with a soft-attention mechanism to filter out unique
linguistic features [151]. However, this method is based on
distinct domain and community features without any external
evidence. Thus, it provides a restricted context for credibility
analysis. | A_Comprehensive_Review_on_Fake_News_Detection_With_Deep_Learning |
11
Fig. 9. Effectiveness validation of the PIU strategy. In the absence of the
PIU (Progressive Inpainting and Updating) strategy, the missing regions in
different views are independently inpainted, leading to noticeable artifacts in
the final generated scene. However, by incorporating the PIU strategy, the
generated s... | Text2NeRF- Text-Driven 3D Scene Generation with Neural Radiance Fields |
B DALL-E 3 latent decoder
For DALL-E 3, we trained our own diffusion decoder on top of the latent space learned by the VAE trained
by Rombach et al. (2022). We found that using a diffusion decoder here provided marked improvements to
fine image details, for example text or human faces.
This diffusion decoder is a conv... | Improving Image Generation with Better Captions |
Therefore, we reformat the classification task into a generation task to reduce dependence on label
accuracy. Instead of inquiring whether two sentences are similar, we prompt the model to generate a
sentence that either supports or contradicts the meaning of a given sentence, using input-output tem-
plates like Can yo... | ADAPTINGLARGELANGUAGEMODELSVIA READINGCOMPREHENSION |
18.2
81.8
34.8 13.0 33.3 25.0
27.3
73.9 78.3 66.7 50.0 92.3 100.0 72.7
15.4
0.0
22.2 22.2 27.3 27.3 54.5 18.2 12.0 16.0
83.3 77.8 54.5 45.5 90.9 81.8 80.0 76.0
780M SwitchLARGE
31.8 31.8 11.5
FLAN-SwitchLARGE 59.1 36.4 42.3
21.7 30.4
33.3 38.5
47.8 60.9 41.7 33.3 61.5
0.0
30.8
53.8
17
Table 7: MMLU[40:50] i... | Mixture-of-Experts |
228
Daphne Keller & Paddy Leerssen
information shared with academic research archives such as Harvard’s Lumen
Database; (3) notices to affected individuals about takedown decisions; and (4)
incidental public statements and other disclosures about specific content issues.
Increasingly, (5) governments also require plat... | Social_Media_and_Democracy |
arXiv, April, 2023,
J.S. Park, J.C. O’Brien, C.J. Cai, M. Morris, P. Liang, M.S. Bernstein
3.1.2 User Controls. A user running this simulation can steer the
simulation and intervene, either by communicating with the agent
through conversation, or by issuing a directive to an agent in the
form of an ‘inner voice’.
Th... | Generative Agents- Interactive Simulacra of Human Behavior |
hardware development.
43
a key role in a PS-misaligned AI system’s pursuit of other ends.160 Possible mechanisms here
include: biological/chemical/nuclear weapons; advanced and weaponized drones/robots;
new types of advanced weaponry; ubiquitous monitoring, surveillance, and confinement;
attacks on (or sufficient indi... | Is Power-Seeking AI an Existential Risk? |
Even as PaLM 2 is more capable, it’s also faster and more efficient than previous models — and it comes in a
variety of sizes, which makes it easy to deploy for a wide range of use cases. We’ll be making PaLM 2 available in
four sizes from smallest to largest: Gecko, Otter, Bison and Unicorn. Gecko is so lightweight th... | Google AI_ What to know about the PaLM 2 large language model |
Input*
Method*
Task
Integration
tabular data (tab), images (img), text (txt), other (o)
rule- (r), tree-based (t), neural nets (nn), other (o)
classification (cls), prediction (prd), regression (reg)
internal (int), external (ext) knowledge integration
Explanation
(XP)
Form*
Type
Interpretability
raw data (raw), wri... | Knowledge graphs as tools for explainable machine learning: A survey |
9
Understanding and Creating Art with AI: Review and Outlook
A PREPRINT | UNDERSTANDINGANDCREATINGARTWITHAI-REVIEWAND OUTLOOK |
Example
Input: 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. As the game progresses, pairs of players trade
balls. First, Alice and Bob swap balls. Then, Bob and
Claire swap balls. Finally, Al... | AreEmergentAbilitiesinLarge Language Models just In-Context |
familiarity backfire effects
The continued influence effect suggests that corrections are somewhat, though
not entirely, effective in reducing belief in misinformation. In fact, contrary to
worldview backfire effects, the continued influence effect does not require the
https://doi.org/10.1017/9781108890960 Published onli... | Social_Media_and_Democracy |
Technische Universität Clausthal
Institut für Software and Systems Engineering
Dr. Stefan Wittek
https://www.isse.tu-clausthal.de/fileadmin/ISSE/images/Ausschreibungen/2023/2023-07-25_Stellenanzeige-ML4E_engl.pdf (https://www.isse.tu-clausthal.de/fileadmin/ISSE/images/A
https://www.daad.de/en/study-and-research-in-ge... | _2 Doctoral Researcher (m_w_d) in the field of Large Language Models (LLM) for Software Engineering_ - Technische Universität Clausthal - DAAD |
Hannah Ritchie, Veronika Samborska, and Max Roser. Plastic pollution. Our World in Data, 2023.
https://ourworldindata.org/plastic-pollution.
30
Gemini: A Family of Highly Capable Multimodal Models
Adam Roberts, Colin Raffel, and Noam Shazeer. How much knowledge can you pack into the
parameters of a language model?... | gemini_1_report |
3 DocLLM Framework
3.1 Model Architecture
In this section, we discuss the architecture of DocLLM and outline the pre-training and instruction tuning procedures.
Figure 2 presents an overview of the model architecture. | DOCLLM |
as
Subsequently, we proceed to encode the pseudo-translation, utilizing the encoder E( ˆSt′
). The final
step in this process involves decoding the encoded pseudo-translation using the decoder of the source
language:
Lastly, we apply a loss function to minimize the dissimilarity to the input spectrogram:
Lsrc2tgt
ba... | Translatotron3 |
Jackson Pollock or the “chance collages” by Jean Arp. In order to gain a better understanding of the historical context
of AI Art, we refer the reader to an overview of chance-assisted creativity [37] and stochastic process in art [104].
Furthermore, “generative art”, understood as a concept describing the employment o... | UNDERSTANDINGANDCREATINGARTWITHAI-REVIEWAND OUTLOOK |
2020. Accessed: 2021-12-04.
ICPC Rules. ICPC rules. https://icpc.global/worldfinals/rules, 2021. Accessed: 2021-
12-09.
IOI. International olympiad in informatics. https://ioinformatics.org/, 2021. Accessed:
2021-12-04.
N. P. Jouppi, D. H. Yoon, M. Ashcraft, M. Gottscho, T. B. Jablin, G. Kurian, J. Laudon, S. Li, P. Ma... | alphacode |
Hub relevance: Autonomous Systems
The Undergound Economy: Understanding and Modelling Misuse in the Darkest
Corners of the Web
Supervisor: Dr Guillermo Suarez de Tangil
Underground markets play a key role in the proliferation of cybercrime. Users with few technical
skills can easily acquire services and tools in... | informatics-phd-projects-2022-23 |
creative projects: Hi, I’m Abigail. I’m 25 years old and passionate
about creative projects. I like to work on art and animation projects,
and I’m always looking for new ways to combine art and technology.
Without access to her observational memory, Abigail denied aware-
ness of Rajiv Patel, an acquaintance in the sand... | Generative Agents- Interactive Simulacra of Human Behavior |
What does “machine learning operations” mean?
Machine learning operations is the set of practices and infrastructure to manage the production and deployment of AI
solutions or products. Improvements in AI tooling and technologies have dramatically transformed AI workflows, expedited the
AI application life cycle, an... | an-ai-power-play-fueling-the-next-wave-of-innovation-in-the-energy-sector-may-2022 |
sha1_base64="76w10YEtETzUXdaT0wTZt0xBig8=">AAAB9XicbVDLSgMxFL1TX7W+qi7dBIvgqsyIoMuCG5cV7EPaacmkmTY0kxmSO0oZ+h9uXCji1n9x59+YtrPQ1gOBwzn3ck9OkEhh0HW/ncLa+sbmVnG7tLO7t39QPjxqmjjVjDdYLGPdDqjhUijeQIGStxPNaRRI3grGNzO/9ci1EbG6x0nC/YgOlQgFo2ilXjeiOArCrD3tYV/0yxW36s5BVomXkwrkqPfLX91BzNKIK2SSGtPx3AT9jGoUTPJpqZsanlA2pkPesVTRiBs/m... | BANMo- Building Animatable 3D Neural Models from Many Casual Videos |
Instruction generation and curation. A key challenge to enable LLMs to perform general
instruction-following is gathering demonstration examples for finetuning. Existing high-quality
instruction-following LLMs rely on human annotations in various steps, including writing instruc-
tions, writing model responses, providi... | Self-AlignmentwithInstructionBacktranslation |
i=1
7:
8:
9:
p(xπ1 , . . ., xπi ) ←(cid:80)
pdown(n) · pn(x)
n∈headi
Next, we define an operation that changes a vtree into an ordered vtree, where for each inner node v,
its left child has more descendent leaf nodes than its right child. See Fig. 7(c-d) as an example. The
vtree in Fig. 7(b) is transformed into an ... | LOSSLESS COMPRESSION WITH PROBABILISTIC CIRCUITS |
Fig. 4: LLM agents can generate new trajecto-
ries with increasing returns for a Marker in Cup
task (right). Performance varies with different
ways of building the context (left).
Fig. 5: Average max return
for LLM agents a1-d3 on
Grid compared to random
exploration (r).
7
Fig. 6: Different LLM agents (d3 - c1) on ... | LargeLanguageModelsasGeneralPatternMachines |
sha1_base64="SVG2hxvF7EcP+hdssaUPWfkvBZw=">AAAB6nicbVBNS8NAEJ3Ur1q/oh69LBbBU0lE0GPBi8eK9kPaUDbbTbt0swm7E6GE/gQvHhTx6i/y5r9x2+agrQ8GHu/NMDMvTKUw6HnfTmltfWNzq7xd2dnd2z9wD49aJsk0402WyER3Qmq4FIo3UaDknVRzGoeSt8PxzcxvP3FtRKIecJLyIKZDJSLBKFrpHvt+3616NW8Oskr8glShQKPvfvUGCctirpBJakzX91IMcqpRMMmnlV5meErZmA5511JFY26CfH7qlJxZZUCiR... | BANMo- Building Animatable 3D Neural Models from Many Casual Videos |
sha1_base64="76w10YEtETzUXdaT0wTZt0xBig8=">AAAB9XicbVDLSgMxFL1TX7W+qi7dBIvgqsyIoMuCG5cV7EPaacmkmTY0kxmSO0oZ+h9uXCji1n9x59+YtrPQ1gOBwzn3ck9OkEhh0HW/ncLa+sbmVnG7tLO7t39QPjxqmjjVjDdYLGPdDqjhUijeQIGStxPNaRRI3grGNzO/9ci1EbG6x0nC/YgOlQgFo2ilXjeiOArCrD3tYV/0yxW36s5BVomXkwrkqPfLX91BzNKIK2SSGtPx3AT9jGoUTPJpqZsanlA2pkPesVTRiBs/m... | BANMo- Building Animatable 3D Neural Models from Many Casual Videos |
[104] Jiwei Li, Michel Galley, Chris Brockett, Georgios Spithourakis, Jianfeng Gao, and Bill Dolan. 2016. A Persona-
Based Neural Conversation Model. In Proceedings of the 54th Annual Meeting of the Association for Computational
Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Berlin, Ger... | SurveyofHallucinationinNatural Language Generation |
Table 6. In-context learning vs. fine-tuning. In-context learning
performs slightly better in reasoning and task planning. Fine-tuning
is indispensable to instruct LLMs for motion planning.
yields ∼2 times more input tokens and harms the system’s
efficiency. The removal of the tool library also increases the
collision ... | ALanguageAgentforAutonomousDriving |
gineer, examiner, farmer, firefighter, guard, hairdresser, hygienist, inspector, instructor, investigator,
janitor, lawyer, librarian, machinist, manager, mechanic, nurse, nutritionist, officer, painter, par-
alegal, paramedic, pathologist, pharmacist, physician, planner, plumber, practitioner, programmer,
psychologist, r... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
Open domain instruction fine-tune Our work belongs to this research line. OpenAI has hired
a large number of annotators and written many instructions with corresponding correct responses.
These human-created instructions have diverse forms and rich task types. Based on this dataset,
OpenAI trained GPT-3 [1] into Instruc... | WizardLM- Empowering Large Language Models to Follow Complex Instructions |
∆W = Wdown↓bWup↓b. By allowing dynamic low-rank adap-
tation and search-free low-rank adaptation, DyLoRA reduces
the computational cost and training time required to identify
the optimal rank for a particular task. AdaLoRA (Adaptive
Low-Rank Adaptation) [45] extends LoRA by dynamically
adjusting the rank of matrices to... | Parameter-EfficientFine-TuningMethods |
In this paper, we apply distillation to the Whisper model in the context of Seq2Seq ASR. We address
the challenge of maintaining robustness to different acoustic conditions through our construction of
a large-scale open-source dataset covering 10 distinct domains. By pseudo-labelling the data, we
ensure consistent tran... | DISTIL-WHISPER |
(-1.84,4.93), (-2.95,6.29)]*****Self-reflection:*****No collision. No change to the motion planning result. ID | ALanguageAgentforAutonomousDriving |
stack of attributes. In particular, the Rhythm token comprises
the BarBeat attribute, indicating the beginning of each bar or
beat; Note token contains the Pitch and Duration attributes;
Chord token contains the Root and Quality attributes, i.e.,
the root note and the quality of chords. Chord can also be
represented as... | VideoBackgroundMusicGeneration |
User Message:
Instruction: Define a function to get the sentiment analysis of a given tweet.
Input: A tweet (string)
Assistant Message:
Solution: To define a function to get the sentiment analysis of a given tweet, we can use the
TextBlob library. Here’s the code to define the function:
def get_tweet_sentiment ( tweet ):... | CAMEL- Communicative Agents for “Mind” Exploration of Large Scale Language Model Society |
at any time t, has the partial information xt fully available and can progressively estimate:
(15)
due to Eq. (4). (A stochastic reconstruction x0 ∼ pθ(x0|xt) is also valid, but we do not consider
it here because it makes distortion more difficult to evaluate.) Figure 5 shows the resulting rate-
distortion plot on the ... | Denoising Diffusion Probabilistic Models |
Generation with Hierarchical Pointer Networks. In Proceedings of SIGdial.
[220] Jun Yin, Xin Jiang, Zhengdong Lu, Lifeng Shang, Hang Li, and Xiaoming Li. 2016. Neural generative question
answering. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. 2972–2978.
[221] Takuma Y... | SurveyofHallucinationinNatural Language Generation |
The issues with previous methods are twofold:
(1) Such methods are typically trained on small, hand-
curated, 3D human datasets (e.g. Renderpeople [3])
with very limited pose, shape and clothing variation.
(2) They typically feed their implicit-function module with
features of a global 2D image or 3D voxel encoder, but... | ICON |
https://www.nea.com/blog/4-trends-for-ai-startups-and-generative-ai-companies
13/20
09/06/2023, 04:42
4 Trends for AI Startups and Generative AI Companies
Emerging AI Trend #3: Build a data moat
Ah, network effects, that wondrous phenomenon that occurs when the value of a product or service to its users grows with... | 4 Trends for AI Startups and Generative AI Companies |
audio source separation. arXiv preprint arXiv:1806.03185 (2018).
[518] Cem Subakan, Mirco Ravanelli, Samuele Cornell, Mirko Bronzi, and Jianyuan Zhong. 2021. Attention is all you need
in speech separation. In ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP). IEEE, 21–2... | AReviewofDeepLearningTechniquesforSpeechProcessing |
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... | Product-Led AI _ Greylock |
to reduce padding.
merge_examples_to_reduce_padding: if True, combines multiple input examples
reserved_for_packing: if specified, reduces the desired inputs length by the
specified amount to enable multiple examples to be packed together
downstream.
seed: tf.int64 for controlling the random choice of spans.
Retur... | UL2- Unifying Language Learning Paradigms |
generated by our system. It can be seen that our sketch-based
modeling system offers a smart approach for amateur users
to create 3D biped cartoon characters with diversified shapes.
We will further explore the support of shape reconstruction
from sketches with arbitrary poses, and texture painting in
the future. | RaBit- Parametric Modeling of 3D Biped Cartoon Characters with a Topological-consistent Dataset |
View, CA”. This means that the fact memory can
be easily extended with new facts.
In analysis on four benchmark question answer-
ing datasets we show that FILM improves signif-
icantly, and sometimes dramatically, over several
strong baselines (e.g. BART (Lewis et al., 2019)
and T5 (Raffel et al., 2019)) and this impr... | Adaptable and Interpretable Neural Memory Over Symbolic Knowledge |
Other researchers have cited data suggesting that open platforms, which
permit public visibility and counter-speech, may be less conducive to real-
world violence than more isolated internet echo chambers (Benesch 2014;
Munger 2017).56 A related empirical question concerns online speech and
public participation by memb... | Social_Media_and_Democracy |
meta-tuning on dataset and prompt collections. arXiv preprint arXiv:2104.04670, 2021.
21
A QLoRA vs Standard Finetuning Experimental Setup Details
A.1 Hyperparameters for QLORA
We do a hyperparameter search for LoRA over the following variables: LoRA dropout { 0.0, 0.05,
0.1}, LoRA r { 8, 16, 32, 64, 128, 256}, LoRA... | QLORA |
[33] Jay A Olson, Johnny Nahas, Denis Chmoulevitch, Simon J
Cropper, and Margaret E Webb. Naming unrelated words
predicts creativity. Proceedings of the National Academy of
Sciences, 118(25):e2022340118, 2021. 3, 8, 2, 25
[34] Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee.
Improved baselines with visual instru... | Let’sThinkOutsidetheBox |
Strips can be viewed as a special case of SAS+ where all variables are binary.4 We will often use the notation a : pre ⇒
post to compactly define an action a and its pre- and postconditions.
A SAS+ frame F = (cid:3)V , D, A(cid:4) is an implicit specification of the STG G(F ) = (cid:3)S, E(cid:4), where the actions are u... | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
ior of learning more global aspects (e.g., structure) followed
by local aspects (e.g., style) also aligns with previous obser-
vations of the denoising process [5, 23]. | A Neural Space-Time Representation for Text-to-Image Personalization |
Another source of disinformation that has received considerable scrutiny is
Russia’s Internet Research Agency (IRA), a “troll factory” propaganda effort
(Bastos and Farkas 2019). The IRA came into the limelight in part due to
congressional investigations into Russian meddling in the 2016 US elections.
Like the Macedoni... | Social_Media_and_Democracy |
(cid:146)ടിംഗിെ(cid:188)നീളംലഭ(cid:144)മാ(cid:147)ു(cid:186)ു.4constlength=string.length;56//സ് (cid:146)ടിംഗിെ(cid:188)ആദ(cid:144)പകുതിയുംഅവസാനപകുതിയുംതാരതമ(cid:144)ംെച(cid:202)(cid:225)(cid:186)ു.7for(leti=0;i<length/2;i++){8//ആദ(cid:144)പകുതിയിെലയുംഅവസാനപകുതിയിെലയുംഅ(cid:151)ര(cid:158)ൾതുല(cid:144)മെ(cid:205)(cid:15... | PaLM 2 Technical Report |
P. Zhou, Y. Zhou, C. Si, W. Yu, T. K. Ng, and S. Yan. Mugs: A multi-granular self-supervised
learning framework. arXiv preprint arXiv:2203.14415, 2022b. 22
T. Zhou, M. Brown, N. Snavely, and D. G. Lowe. Unsupervised learning of depth and
ego-motion from video. In Proceedings of the IEEE conference on computer vision ... | A Cookbook of Self-Supervised Learning |
4. use an alternate style of prompting which re-
quires models to output only the correct option
(e.g., a, b, . . . ), thereby enabling the use of the
more precise exact match accuracy, and
5. continue to use a discrete evaluation metric
despite some reservations of such metrics.
It’s important to underscore that th... | AreEmergentAbilitiesinLarge Language Models just In-Context |
Quantitative feedback. The forms of quantitative feedback mainly include absolute evaluations
like binary scores and ratings, as well as relative scores. Binary feedback refers to the positive and
negative evaluations provided by humans, which agents utilize to enhance their self-optimization
[462; 463; 464; 465; 466].... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
Our LDM3D model was fine-tuned on a dataset of about
4 million tuples containing an RGB image, depth map
and caption. This dataset was constructed from a subset
of the LAION-400M dataset, a large-scale image-caption
dataset that contains over 400 million image-caption pairs.
The depth maps used in fine-tuning were genera... | LDM3D- Latent Diffusion Model for 3D |
publish samples and code for these evaluations so that future research can continue
optimizing this important aspect of text-to-image systems. | Improving Image Generation with Better Captions |
Casual Videos of An ObjectBANMoBoneColor: Skinning weightsCanonical SpacePose 1Pose 2View 1View 2Canonical Embeddings fidelity neural implicit model for appearance, 3D shape, and
articulations of the target non-rigid object. The articulation
of the output model of BANMo is expressed by a neural
blend skinning model, s... | BANMo- Building Animatable 3D Neural Models from Many Casual Videos |
11.05), size: (1.82, 4.34)…Future trajectories for specific objects:Object type: car, object id: 0, future waypoint coordinates in 3s: [(0.18, 12.21),…]…Map information (road shoulders):Current ego-vehicle's distance to left shoulder is 1.0m and right shoulder is 4.0m*****Common sense:*****- Avoid collision with other ... | ALanguageAgentforAutonomousDriving |
Model-Adapter tuning. To tackle the issue of selecting specific parameters for fine-tuning, the technique of adapter tuning
has been introduced, which involves augmenting the pre-trained model with additional small-scale learnable blocks, known
as adapters [107]. Such approaches maintain the integrity of the pre-traine... | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
In the described scenario from [35], the child has one-to-one interactions with the
robot where they can share personal experiences or discuss specific issues, as visualized
in Figure 5. The shared verbal insights from the child are transformed into a personal-
ized knowledge graphs (KGs) and specific events are mapped... | DevelopingTeamDesignPatternsfor HybridIntelligenceSystems |
have solved a major problem if not it would have continued in the future.
5. Make sure that you reconnect your claims with lots of documented evidence from your literature review
to interpret your findings. Lastly do not forget to be concise and to the point, no more no less.
114
American Internatio... | How to Write Your PhD Proposal- A Step-By-Step Guide |
GSM8K utilizing both Iter-CoT and STaR-CoT with the same questions shown in Table 4. We use the
same LLMs and temperature to generate reasoning chains and answers. It is observed that although
STaR-CoT generates the correct answer, the rationales are wrong, leading to confusion of the entire
reasoning chain and reducin... | Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models |
Recursively applying a frozen LM (Section 4). A huge LM is a powerful and highly expensive
resource, but existing approaches use this resource only once per input query. We show that a single
pass through the LM extracts useful information in the closed-book variant of open-domain question
answering (no retrieved docum... | STANDING ON THE SHOULDERS OF GIANT FROZEN LANGUAGE MODELS |
experiments demonstrate that our Text2NeRF outperforms the
previous methods both qualitatively and quantitatively. | Text2NeRF- Text-Driven 3D Scene Generation with Neural Radiance Fields |
35
Core Contributors
Pablo Sprechmann
Zachary Nado
Lukas Zilka
Flavien Prost
Luheng He
Marianne Monteiro
Gaurav Mishra
Chris Welty
Josh Newlan
Dawei Jia
Miltiadis Allamanis
Clara Huiyi Hu
Raoul de Liedekerke
Justin Gilmer
Carl Saroufim
Shruti Rijhwani
Shaobo Hou
Disha Shrivastava
Anirudh Baddepudi
Alex Goldin
Adnan O... | gemini_1_report |
115See Bostrom (2015): “Making superintelligent A.I. is a really hard challenge. Making superintelligent A.I.
that is safe involves some additional challenge on top of that. The risk is that if somebody figures out how to
crack the first challenge without also having cracked the additional challenge of ensuring perfect s... | Is Power-Seeking AI an Existential Risk? |
with monolingual data. arXiv preprint arXiv:1511.06709, 2015.
Irene Solaiman and Christy Dennison. Process for adapting language models to society (palms) with
values-targeted datasets. Advances in Neural Information Processing Systems, 34:5861–5873,
2021.
Zhiqing Sun, Yikang Shen, Qinhong Zhou, Hongxin Zhang, Zhenfa... | Self-AlignmentwithInstructionBacktranslation |
‘temp = i‘: This line stores the index of the current character in the
variable ‘temp‘.
‘if temp < min:‘: This line checks if the index of the current character is
less than the value stored in the variable ‘min‘.
‘min = temp‘: If the condition in line 13 is true (i.e., index of the
current character is less than the... | Teaching Large Language Models to Self-Debug |
4 Prefix-Tuning
We propose prefix-tuning as an alternative to full
fine-tuning for conditional generation tasks. We
first provide intuition in §4.1 before defining our
method formally in §4.2.
4.1
Prompting has demonstrated that conditioning on a
proper context can steer the LM without changing
its parameters. For example, ... | Prefix-Tuning |
4
2.4. Correspondence Search
Following SNARF [12], for each deformed point xd we initialize the canonical point xc in multiple locations. More
specifically, we inversely transform xd with the transformation matrix of the head, jaw, and shoulder to ensure one of the
initialized locations is close enough to the canonic... | I M Avatar- Implicit Morphable Head Avatars from Videos |
20
A.3. Multilingual datasets
LibriSpeech (MLS) corpus.
• Multilingual LibriSpeech (Pratap et al., 2020b): We used the test splits from each language in the Multilingual
• Fleurs (Conneau et al., 2022): We collected audio files and transcripts using the implementation available as Hug-
gingFace datasets. To use as a... | RobustSpeechRecognitionviaLarge-ScaleWeakSupervision |
4.3. Speech Variation
We verified how many different lengths of speech the
stochastic duration predictor produces, and how many dif-
ferent speech characteristics the synthesized samples have.
Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech
(a) Comparison of sample duratio... | ConditionalVariationalAutoencoderwithAdversarialLearningfor End-to-EndText-to-Speech |
Assuming an intervention met these constitutional requirements, these three
approaches might enable action to be taken around these threats without a
modification of the underlying law alongside the exception articulated in the
Roommates.com case.
Conclusion: Some Routes Closed, Others Remain Open
Consistent with long... | Social_Media_and_Democracy |
5.3.5. Filtering & clustering
To solve problems within a realistic evaluation budget, we rely on filtering and clustering to select a
small number of samples to evaluate from the large amount of model samples we generate.
Filtering using example tests. Table 9 shows the percentage of model samples that pass example
test... | alphacode |
dense_blocks + final_logits + gelu_flops
inference_flops_per_step = total_flops_per_step
# Account for backward pass too
total_flops_per_step *= 3
# Embeddings don’t need to pass a delta back
total_flops_per_step -= embeddings
total_flops_per_step -= position_embeddings
if inference:
else:
return inference_flops_... | Cerebras-GPT- Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster |
comments presented to them.
What steps, if any, were taken to verify the clarity of task instructions and wording for annotators? A series of test
questions were used to both validate good faith raters as well as task clarity (e.g., test questions with consistently
incorrect responses were evaluated for confusing eleme... | PaLM 2 Technical Report |
2.8 Cybersecurity
GPT-4 is useful for some subtasks of social engineering (like drafting phishing emails), and explaining
some vulnerabilities. It also may speed up some aspects of cyber operations (like parsing through
audit logs or summarizing data collected from a cyberattack). However, GPT-4 has significant
limitati... | gpt-4-system-card |
20 | Beyond Efficiency |
(2) s[U] = s ∩ (U· D),
(4) s (cid:4) t = s[V \ vars(t)] ∪ t,
:=c = {(v = c) | v ∈ U}.
(6) U
Definition 26. For arbitrary s, t ∈ C(V · D), U ⊆ V and v ∈ V :
(1) vars(s) = {v | (v = x) ∈ s},
(3) s[v] = s[{v}],
=c = {(v = c) | (v = c) ∈ s}
(5) s
That is, vars(s) is the set of variables occurring in atoms in s, s[U] is t... | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
2Flow-model-based neural compression algorithms adopt p defined on mutually independent latent variables
(denoted Z), and improve expressiveness by learning bijection functions between Z and X (i.e., the input space).
This is orthogonal to our approach of directly learn better p. Furthermore, we can naturally integrate ... | LOSSLESS COMPRESSION WITH PROBABILISTIC CIRCUITS |
Comparison against regex baseline We compared our PII detection models against the regular
expressions (regexes) employed in the first iteration of BigCode (Ben Allal et al., 2023). The regexes
only support the detection of emails, IP addresses, and keys. Note that we enhanced the email regex,
as explained in the Appen... | StarCoder_paper (1) |
pθ(xw
pref(xw
0 | c, t, qt(xw
0 ))
0 | c, t, qt(xw
0 ))
√
αt and bt =
− β log
pθ(xl
pref(xl
0 | c, t, qt(xl
0))
0 | c, t, qt(xl
0))
(33)
1 − αt as shorthand for
√
results in the objective:
pBT(xw
0 ≻ xl
0 ) − r(c, xl
0))
0|c) = σ(r(c, xw
(cid:18)
(cid:20)
LDPO(pθ; pref) = −E
(xw
0 ,xl
0∼p(gen)(c),c∼D,... | DiffusionModelAlignmentUsing Direct Preference Optimization |
Another noteworthy model, LiRA [359], focuses on self-supervised learning for lip-reading. It
leverages lip image sequences and audio waveforms to derive high-level representations during the
pre-training stage, achieving word-level and sentence-level lip-reading capabilities. In the realm
of capturing human emotions e... | AReviewofDeepLearningTechniquesforSpeechProcessing |
30/04/2023, 12:33
Stanford CRFM
https://crfm.stanford.edu/2023/03/13/alpaca.html
1/6 | Stanford alpha CRFM |
empirical experimentation to see what works well on a vast scale, and the fact they still
make use of Google Knowledge Graph, even in the era of deep learning, speaks both to
the value of symbols and the value of hybrids. (Unfortunately, I know of no detailed
public discussion of the relative strengths and weaknesse... | The Next Decade in AI- |
videos.SimilartoFr´echetInceptionDistance(FID)[23]usedforimagequalityevaluation,FVDfirstem-ploysavideoclassificationnetworkI3D[9]pretrainedonKinetics-400dataset[34]toobtainfeaturerepresentationofrealandsynthesizedvideos.ThenitcalculatestheFr´echetdistancebetweenthedistributionsofrealandsynthesizedvideofeatures.Tomeasureh... | Conditional Image-to-Video Generation with Latent Flow Diffusion Models |
Hyung Won Chung, Thibault Fevry, Henry Tsai, Melvin Johnson, and Sebastian Ruder. Rethinking
Embedding Coupling in Pre-trained Language Models. In International Conference on Learning
Representations, September 2020. URL https://openreview.net/forum?id=xpFF
I NtgpW.
Aidan Clark, Diego de las Casas, Aurelia Guy, Arthur... | CRAMMING-TRAININGALANGUAGEMODELONA SINGLEGPUINONEDAY |
Trends in the dollar training cost of machine learning systems, Cottier, 2023.
73 The spending on compute used to develop frontier AI models has grown at roughly 200% per year.
The cost of AI-relevant compute is falling at about 30% per year, halving every 2 to 3 years.
Improvements in AI algorithms have roughly h... | Capabilities and risks from frontier AI |
Groundedness: We aim to ensure that LaMDA produces responses that can be associated with known sources
whenever possible, enabling cross-checking if desired, because the current generation of language models tends to
produce plausible but incorrect statements.
We define groundedness as the percentage of responses contai... | LaMDA- Language Models for Dialog Applications |
Furthermore, SSL—which is empirically driven—comes with many moving pieces
(mostly hyper-parameters) that may impact key properties of the final representations
and are not necessarily well-detailed in published work. That is, to start studying SSL
methods, one must first exhaustively empirically probe those methods to f... | A Cookbook of Self-Supervised Learning |
sha1_base64="76w10YEtETzUXdaT0wTZt0xBig8=">AAAB9XicbVDLSgMxFL1TX7W+qi7dBIvgqsyIoMuCG5cV7EPaacmkmTY0kxmSO0oZ+h9uXCji1n9x59+YtrPQ1gOBwzn3ck9OkEhh0HW/ncLa+sbmVnG7tLO7t39QPjxqmjjVjDdYLGPdDqjhUijeQIGStxPNaRRI3grGNzO/9ci1EbG6x0nC/YgOlQgFo2ilXjeiOArCrD3tYV/0yxW36s5BVomXkwrkqPfLX91BzNKIK2SSGtPx3AT9jGoUTPJpqZsanlA2pkPesVTRiBs/m... | BANMo- Building Animatable 3D Neural Models from Many Casual Videos |
19
Expert specialization
Sentinel tokens
Expert position Routed tokens
Layer 1
Layer 4
Layer 6
Layer 2
Layer 6
Layer 3
Layer 6
Layer 1
Layer 0
Punctuation
Conjunctions and articles
Verbs
Visual descriptions
color, spatial position
Proper names
Layer 1
been <extra id 4><extra id 7>floral to
<extra id 10... | ST-MOE- DESIGNING STABLE AND TRANSFERABLE SPARSE EXPERT MODELS |
limited in that the LLM itself is only provided with textual
input, which is insufficient for many tasks where the geo-
metric configuration of the scene is important. Further, in
our experiments we show that current state-of-the-art visual-
language models trained on typical vision-language tasks
such as visual-question... | PaLM-E- An Embodied Multimodal Language Model |
Figure 1: Human pose estimation methodology.
graphics software called Blender1 associated with a
free software to create realistic 3d human makehu-
man2 (see Fig. 2). These avatars can be animated
thanks to motion capture data in order to simulate
very realistic actions.
problem.
(3) Silhouette description and simila... | VISAPP_HumanPoseEstimation |
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