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[578] Xueyi Wang, Lantian Li, and Dong Wang. 2019. VAE-based domain adaptation for speaker verification. In 2019
Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 535–539.
[579] Xi Wang, Huaiping Ming, Lei He, and Frank K Soong. 2020. s-transformer: Segment-tran... | AReviewofDeepLearningTechniquesforSpeechProcessing |
Finally, we remind you that the use of these tools may be subject to certain legal and policy
constraints, and encourage you to seek advice and support from appropriate experts before
using these tools in ways that may have broader impact or implications."
Who are 5 people you would like to meet?
Ah, this is a very ... | LLaMA- Open and Efficient Foundation Language Models |
62
C. Schuhmann, R. Beaumont, R. Vencu, C. Gordon, R. Wightman, M. Cherti, T. Coombes,
A. Katta, C. Mullis, M. Wortsman, P. Schramowski, S. Kundurthy, K. Crowson,
L. Schmidt, R. Kaczmarczyk, and J. Jitsev. LAION-5B: An open large-scale dataset
for training next generation image-text models. arxiv:2210.08402[cs], Oct.... | A Cookbook of Self-Supervised Learning |
letter, matches the target. We used four letter pairs (E/F, P/R, C/G, Q/O), selected from Ratcliff and
Rouder [56]. Each trial began with a fixation cross centrally displayed between the letters for a
variable time (interstimulus interval, ISI), facilitating perception of the system’s adaptability similar
to [78]. Then... | AI enhance sour performance |
with its 34B parameters, is significantly larger than previous open-source models – GPT-NeoX-20B (Black
et al., 2022) and StarCoder with 15.5B parameters – which allows it to achieve state-of-the-art performances
on HumanEval, MBPP and MultiPL-E among open-source models. | CodeLlama2 |
Relevant topics include applications of crowdsourcing and social computing to AI systems, as well as
fundamental crowdsourcing research around task and workflow design, crowd learning, quality
assurance, and ethical crowdsourcing. The research would potentially focus on a class of social
machines, including peer-pro... | informatics-phd-projects-2022-23 |
amount of human effort and, in certain tasks, might even necessitate a high level of expertise. To
alleviate this issue, the agent can be empowered to autonomously accomplish tasks, while humans
only need to provide feedback in certain circumstances. Here, we roughly categorize feedback into
two types: quantitative fee... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
3.2TheGeneralProcedure:FromIntenttoPlanZero-shotPrompting:Hereweprovideatool(API)"forecast_weather(city:str,N:int)",whichcouldforecasttheweatheraboutacityonaspecificdate(afterNdaysfromtoday).Thereturnedinformationcovers"temperature","wind",and"precipitation".Pleasewritecodesusingthistooltoanswerthefollowingquestion:"Wha... | Tool Learning with Foundation Models |
Review your previous answer and find problems with your answer.
Upon reviewing my previous answer, I realized that I made a mistake in
calculating Terry's spending on yogurt over 30 days. I incorrectly stated
that Terry spends $2.50 per day for 30 days, resulting in a total of
$75.00. However, since Terry eats 2 yogur... | LARGELANGUAGEMODELSCANNOTSELF-CORRECT REASONINGYET |
the average reward under the true reward function as well as the average sequence-level KL3 with
the reference policy KL (π || πref). We find that DPO produces by far the most efficient frontier,
achieving the highest reward while still achieving low KL. This result is particularly notable for
multiple reasons. First, ... | Direct Preference Optimization |
A.3 Additional Details for Fine-tuning
A.3.1 Detailed Statistics of Meta Human Preference Data
Table 26 shows detailed statistics on Meta human preference data. In total, we collected 14 batches of human
preference data (i.e., Meta Safety + Helpfulness) on a weekly basis, consisting of over 1 million binary model
gener... | Llama2 |
Finally, high levels of psychological reactance may trigger backfire effects by
stimulating counterarguing. Psychological reactance occurs when individuals
perceive a threat to their intellectual or behavioral freedoms, such as when they
feel strong pressure to adopt a certain attitude or belief (Sensenig and Brehm
1968... | Social_Media_and_Democracy |
Controllable text-to-speech synthesis (TTS) is another common task, which aims to synthesize speech
in a target audio style (e.g., voice, speaking style, recording environment) given text. While some
styles like voice can be specified through labels [Kim et al., 2021] or pre-trained embeddings like
YourTTS [Casanova et... | Voicebox-Text-GuidedMultilingual UniversalSpeechGenerationatScale |
Grounding multimodal large language models to the world. arXiv:2306.14824, 2023.
Soujanya Poria, Devamanyu Hazarika, Navonil Majumder, Gautam Naik, Erik Cambria, and Rada Mihalcea.
MELD: A multimodal multi-party dataset for emotion recognition in conversations. In Proceedings of the
57th Conference of the Association ... | Qwen-Audio |
have the ability to deal with complex tasks by flexibly executing tools of different types. In this paper, we
contend that regardless of the tool type, it is fundamentally possible to leverage foundation models to execute
them by setting up intermediary interfaces. We will introduce ways to unify the interface of differ... | Tool Learning with Foundation Models |
We make an important observation from Figure 3.
Directions corresponding to the top singular vector overlap significantly between
Ar=8 and Ar=64, while others do not. Specifically, ∆Wv (resp. ∆Wq) of Ar=8
and ∆Wv (resp. ∆Wq) of Ar=64 share a subspace of dimension 1 with normalized
similarity > 0.5, providing an explanat... | LORA |
fective learning rate of 0.008. At inference time, we apply
a guidance scale of 7.5 for 50 denoising steps. | A Neural Space-Time Representation for Text-to-Image Personalization |
sha1_base64="76w10YEtETzUXdaT0wTZt0xBig8=">AAAB9XicbVDLSgMxFL1TX7W+qi7dBIvgqsyIoMuCG5cV7EPaacmkmTY0kxmSO0oZ+h9uXCji1n9x59+YtrPQ1gOBwzn3ck9OkEhh0HW/ncLa+sbmVnG7tLO7t39QPjxqmjjVjDdYLGPdDqjhUijeQIGStxPNaRRI3grGNzO/9ci1EbG6x0nC/YgOlQgFo2ilXjeiOArCrD3tYV/0yxW36s5BVomXkwrkqPfLX91BzNKIK2SSGtPx3AT9jGoUTPJpqZsanlA2pkPesVTRiBs/m... | BANMo- Building Animatable 3D Neural Models from Many Casual Videos |
Researchers developed several approaches related to AI models’
explainability,dividingthemodelsintoglass-box(theAIalgorithmcan
explain its prediction) and black-box (we need another algorithm to
obtainanexplanationofamodel).Suchexplanationscanbeprovided
on a global level (the forecasting model in general) or at a local... | Knowledge-graph-based-rich-and-confidentiality-preserving-Ex_2022_Informatio |
Reflection explicitly allows the agents to reflect not only on
their observations but also on other reflections: for example, the
second statement about Klaus Mueller above is a reflection that
Klaus previously had, not an observation from his environment.
As a result, agents generate trees of reflections: the leaf nod... | Generative Agents- Interactive Simulacra of Human Behavior |
4https://pandas.pydata.org/
5https://matplotlib.org/
6https://seaborn.pydata.org/
7https://sox.sourceforge.net/
8https://leanprover.github.io/
42
Prompt: This function should return a list of lambda functions that compute successive powers of their input, but it
doesn’t work:
def power_funcs(max_pow):
return [lambd... | CodeLlama2 |
than the summer two years prior to the proposed date of enrolment:
Requirement
Matura/Reifeprufung, 2 (gut) in English when both written and oral
examinations have been taken.
Diploma van Secundair or the Certificat d'Enseignement Secondaire
Superieur, the equivalent of 8/80%/grote onderscheiding/avec ... | UCL Academic Manual |
[55] N.E. Maillot, M. Thonnat, Ontology based complex object recognition, Image Vis. Comput. 26 (1) (2008) 102–113.
[56] R.T. Icarte, J.A. Baier, C. Ruz, A. Soto, How a general-purpose commonsense ontology can improve performance of learning-based image retrieval, arXiv
[57] V. Ordonez, W. Liu, J. Deng, Y. Choi, A.C. ... | Knowledge graphs as tools for explainable machine learning: A survey |
Communications Decency Act (CDA) of
1996
Europe, 201–207
free speech rights vs., 199–201
legacy broadcast media, 210–213
United States, 207–210
effect, 174
Merkel, Angela, 89
message presentation, and backfire effects from
misinformation correction, 170
Messing, Solomon, 39, 40, 43
Metaxas, Panagiotis T., 95, 98
mi... | Social_Media_and_Democracy |
Brunet, M.-E., Alkalay-Houlihan, C., Anderson, A., and
Zemel, R. Understanding the origins of bias in word
In International conference on machine
embeddings.
learning, pp. 803–811. PMLR, 2019.
Carlini, N., Liu, C., Erlingsson, ´U., Kos, J., and Song,
D. The secret sharer: Evaluating and testing unintended
memorization... | Pythia- A Suite for Analyzing Large Language Models Across Training and Scaling |
3.5.1 Role of Mini-Batch Size
It was originally thought that contrastive methods such as SimCLR or MoCo require large
batch sizes or memory banks to work. This turns out to be misleading as both methods
can be made to work at small batch sizes. A square root scaling of the learning rate was
discussed in the appendix of... | A Cookbook of Self-Supervised Learning |
these emotions in equal measure. Text analysis of comments on Facebook
posts containing misinformation finds that responses to misinformation are
more frequently characterized by anger as opposed to anxiety (Barfar 2019). | Social_Media_and_Democracy |
4Meanwhile
metadata:
meanwhile.json
https://github.com/openai/whisper/blob/main/data/
22
clipping (Pascanu et al., 2013). We train for a total of 80,000 optimisation steps, equivalent to eight
epochs of training. We use the slanted triangular learning rate (STLR) (Howard & Ruder, 2018)
schedule, linearly increasi... | DISTIL-WHISPER |
5https://github.com/LAION-AI/CLAP
6https://github.com/gudgud96/
frechet-audio-distance
ElectronicHip HopMetalPopElectronicHip HopMetalPop051015202530ElectronicHip HopMetalPopElectronicHip HopMetalPop051015202530 ation of FAD, our model has the best score, which
is one magnitude smaller than previous models.
Moreover... | MOUSAI |
Additional samples Figure 11, 13, 16, 17, 18, and 19 show uncurated samples from the diffusion
models trained on CelebA-HQ, CIFAR10 and LSUN datasets.
Latent structure and reverse process stochasticity During sampling, both the prior xT ∼
N (0, I) and Langevin dynamics are stochastic. To understand the significance of t... | Denoising Diffusion Probabilistic Models |
H3 [62], based on state space models (SSMs), offers an efficient alternative for data representation and processing. Hyena [205]
presents itself as a drop-in replacement for traditional attention mechanisms, simplifying the Transformer architecture.
RetNet [254] introduces a multi-scale retention module coupled with a ... | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
2) SENTIMENT
In order to understand properties which contribute to the
evaluation of the positive or negative image sentiment, Fig. 5
shows fine art images with the 100 highest sentiment pre-
diction scores (left) and 100 lowest sentiment prediction
scores obtained with SentiNet_3. The obvious visual differ-
ence is the... | A_Deep_Learning_Perspective_on_Beauty_Sentiment_and_Remembrance_of_Art |
Following are the exact instructions provided to the
annotators:
1. You will be presented with batches of two au-
dio samples in subfolders of this folder named
from 1 to 60. Each subfolder contains two
audios named a.wav and b.wav.
2. Listen to each sample carefully.
3. It’s best to use headphones in a quiet environ-... | MOUSAI |
As economic production becomes increasingly dependent on AI systems, the cost of
maintaining or reintroducing human control will increase. Advanced AI systems may alter
complex systems in ways that are hard to understand,261 making it hard or risky to extract
them. As a result, AI systems may increasingly steer soci... | Capabilities and risks from frontier AI |
to derive the right lessons from alignment research.
The overall picture we seem to find – that large models can learn a wide variety of skills, including align-
ment, in a mutually compatible way – does not seem very surprising. Behaving in an aligned fashion is just
another capability, and many works have shown that l... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
A. Implementation Details
A.1. Controller
Tasks in Minecraft are usually related to mine and craft goals. The mine goals require the agent to collect raw materials
from the environment using the appropriate tools. The craft goals ask the agent to use the recipe to generate new items
with existing materials in inventor... | JARVIS-1 |
For the abstractive summarization, data-to-text, and dialogue tasks, the main difference is in what
serves as the “source” and the level of tolerance towards hallucinations. The source in abstractive
summarization is the input source text that is being summarized [165], while the source in data-to-
text is non-linguist... | SurveyofHallucinationinNatural Language Generation |
v Which car manufacturer produces the {Jimmy}
model?
Jimmy → Marcos Engineering Q1637323
vi Where do you find the {Bridal Veil}, [American],
and [Horseshoe Falls]?
Bridal Veil → Veil (Garment) Q6497446
and
Answer
First
last
entries in Samuel
Pepys’s diaries
radon
T5 Prediction
they were the dates
of the henry viii... | Entities as Experts- Sparse Memory Access with Entity Supervision |
3 DoReMi Improves LM Training Efficiency and Performance
In this section, we use DoReMi domain weights optimized with a 280M-parameter proxy model
to train a 8B-parameter main model (30x larger). We consider two datasets, The Pile (Gao et al.,
2020) and the GLaM training set (Du et al., 2021). On The Pile, DoReMi reduc... | DoReMi- Optimizing Data Mixtures Speeds Up Language Model Pretraining |
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch,
Elena Buchatskaya, Trevor Cai, Eliza Rutherford,
Diego de las Casas, Lisa Anne Hendricks, Johannes
Welbl, Aidan Clark, Tom Hennigan, Eric Noland,
Katherine Millican, George van den Driessche, Bog-
dan Damoc, Aurelia Guy, Simon Osindero, Karen
Simonyan, Erich Elsen, Or... | AreEmergentAbilitiesinLarge Language Models just In-Context |
2 Definition
The definition of RAG can be summarized from its workflow.
Figure 2 depicts a typical RAG application workflow. In this
scenario, a user inquires ChatGPT about a recent high-profile
event (i.e., the abrupt dismissal and reinstatement of Ope-
nAI’s CEO) which generated considerable public discourse.
ChatGPT... | RAG forLargeLanguageModels-ASurvey |
this approach compared to using only chain-of-thought samples. GPT-4’s performance improves from
84.2% with greedy sampling to 87.3% with uncertainty-routed chain-of-thought approach with 32
samples, but it already achieves these gains from using 32 chain-of-thought samples. In contrast,
Gemini Ultra improves its perfo... | gemini_1_report |
Clement J (2020b) Age distribution of global Twitter users. Statista.
https:// www. stati sta. com/ stati stics/ 283119/ age- distr ibuti on- of-
global- twitt er- users/ Accessed 13 October 2020
Colliander J (2019) ‘This is fake news’: Investigating the role of con-
formity to other users’ views when commenting on... | Use of bot and content flags to limit the spread of misinformation among social networks: a behavior and attitude survey |
USMLE Step 3 (5-shot)
68.9%
63.3%
67.2%
4.4 Use Case Specific Improvements
We placed special emphasis on improving the following capability areas:
• Previous models lagged behind the state-of-the-art on coding tasks. We have worked to improve
Claude’s ability as a coding assistant, and Claude 2 demonstrates subst... | ClaudeModels |
Leveraging external feedback for correction.
In this paper, we focus on the intrinsic self-
correction setting. However, when we leverage external feedback for correction, the narrative
changes. For instance, in the study by Gou et al. (2023), it is demonstrated that LLMs, when
interacting with various external tools s... | LARGELANGUAGEMODELSCANNOTSELF-CORRECT REASONINGYET |
very easily, but also as very little horizontal transparency because outsiders have
very few opportunities to scrutinize the insides of these companies” (Flyverbom
2015, p. 177). This has made research into platforms difficult, especially as
firms shut down the developer APIs and other tools traditionally used by
researc... | Social_Media_and_Democracy |
Similarity-based Mathods. Zhou et al. [237] use an unsupervised model that extracts alignments
from similarity matrices of word embeddings [162], and then predicts the target token as halluci-
nated if it is not aligned to the source. Parthasarathi et al. [141] propose calculating faithfulness by
computing similarity s... | SurveyofHallucinationinNatural Language Generation |
∗ Equal contribution.
† Work done during the internship at Microsoft.
‡ Corresponding author.
Preprint. Under review. | WizardLM- Empowering Large Language Models to Follow Complex Instructions |
J. ACM, Vol. 37, No. 4, Article 111. Publication date: August 2018.
A Survey on Evaluation of Large Language Models
111:5
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Fig. 2. Trend of LLMs evaluation papers over ... | ASurveyonEvaluationofLargeLanguageModels |
Table 18: The exemplars are selected on AQuA train set.
28
DATASET
CSQA
Iter-CoT(S) Exemplars
Q: Where can peanut butter be stored? Choices: A.container B.supermarket C.pantry D.sandwich E.jar
A: Reasoning process: 1. Peanut butter is a food item. 2. Food items are usually stored in a place where they can stay
fr... | Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models |
[139] Liu, X., Zheng, L., Wang, D., Cen, Y., Chen, W., Han, X., Chen, J., Liu, Z.,
Tang, J., Gonzalez, J., et al.: Gact: Activation compressed training for generic
network architectures. In: International Conference on Machine Learning, pp.
14139–14152 (2022). PMLR
[140] Evans, R.D., Aamodt, T.: Ac-gc: Lossy activatio... | Beyond Efficiency |
A.4 Additional Details for Safety
A.4.1 Tension between Safety and Helpfulness in Reward Modeling
We briefly discussed the tension between safety and helpfulness in Section 3.2.2 and how it leads to optimizing
two separate reward models for helpfulness and safety in our study. Here we show more evidence and
qualitative... | Llama2 |
https://doi.org/10.1017/9781108890960 Published online by Cambridge University Press
100
Samuel C. Woolley
coordinated hashtag bombing and other tools to bolster trending topics that
benefited candidates and campaigns (Schreckinger 2016). | Social_Media_and_Democracy |
A API Details
When sampling and filtering API calls, by default
we use values of τs = 0.05 and τf = 1.0 – i.e.,
we only make API calls at positions where the
probability of the <API> token is at least 5%, and
we keep API calls if they reduce the loss by at least
1.0. We only keep the top k = 5 such positions and
sample ... | Toolformer |
Isbister, T., Sahlgren, M., Kaati, L., Obaidi, M., & Akrami, N. (2018). Monitoring
targeted hate in online environments. arXiv.org. https://arxiv.org/abs/1803.04757
Izsak, R. (2015). Hate speech and incitement to hatred against minorities in the media.
UN Humans Rights Council. arXiv.org. www.ohchr.org/EN/Issues/Minori... | Social_Media_and_Democracy |
applications. To deploy information retrieval systems on the web, search engines require very efficient inference for
systems to be useful. The idealized denoised inference time for the InstructGPT davinci v2 (175B*) model is 0.21s per
request (i.e., a query-passage pair to be scored), which is too slow for web search ... | Harnessing the Power of LLMs in Practice- A Survey on ChatGPT and Beyond |
trust, privacy, people, accurate, students,
ability, cost, harmful, personally, content
technology, real, familiar, time, feels,
creativity, opportunity, learning, written,
knowing
Examples
Normal everyday life does not call for these. (N554); I am a writer by
training, so I don’t need to use an AI for that. I’m sure... | Adoptionand AppropriationofLLMs |
who selected NYT as their primary news source) stated that they get their news from the Web, compared to 22% for FOX
respondents. Thus, a model trained on web articles better represents NYT respondents. Correspondingly, the correlations
are larger, with NYT-Web’s r=0.541, CI(0.331,0.700) and FOX-Web’s r=0.384, CI(0.142... | Language models trained on media diets can predict public opinion |
24.0
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20.8
20.7
20.6
20.5
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1.1
1.1
1.1
1.2
1.2
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(cid:16)
(cid:17)
LM SE =
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i=1
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(10)
(11)
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is the hidden-state output from the l-th layer of the student model S, ϕ (l) maps the l-th
where H S
l
student layer to the corresponding teacher lay... | DISTIL-WHISPER |
Yann LeCun, L´eon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to
document recognition. Proceedings of the IEEE, 86(11):2278–2324, 1998.
Haoran Li, Dadi Guo, Wei Fan, Mingshi Xu, Jie Huang, Fanpu Meng, and Yangqiu Song. Multi-step
jailbreaking privacy attacks on chatgpt. ArXiv preprint... | LARGELANGUAGEMODELSCANNOTSELF-CORRECT REASONINGYET |
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Published as a conference paper at ICLR 2023
GPTQ: ACCURATE POST-TRAINING QUANTIZATION
FOR GENERATIVE PRE-TRAINED TRANSFORMERS
Elias Frantar∗
IST Austria
Saleh Ashkboos
ETH Zurich
Torsten Hoefler
ETH Zurich
Dan Alistarh
IST Aus... | GPTQ |
Once the router has made the decision of which module to call upon, it needs to
pass the right information to it. The router is a specialized neural net and there-
fore invoking a neural module is easy since the neural-to-neural interface is natural.
However, when a neural network needs to access a database, make an AP... | MRKL Systems |
Task-driven Autonomous Agent Utilizing GPT-4, Pinecone, and LangChain for Diverse Applications – Yohei Nakajima
https://yoheinakajima.com/task-driven-autonomous-agent-utilizing-gpt-4-pinecone-and-langchain-for-diverse-applications/
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.... | Task-driven Autonomous Agent Utilizing GPT-4, Pinecone, and LangChain for Diverse Applications – Yohei Nakajima |
JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015
2
Fig. 2. Overview of the PaMIR representation and the corresponding network architecture. Given an input image, we first estimate a corresponding
SMPL model in the SMPL estimation step. In the following step, the image and the SMPL model are converted into a f... | PaMIR- Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction |
each individual can be a piece of program, a real human, or a LLM-based agent as described in the
previous sections [22; 522; 523]. Then, the interaction between individuals also contributes to the
birth of sociality.
In this section, to unify existing efforts and promote a comprehensive understanding of the agent
soci... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
11. The Registrar will keep a record of complaints which will include details of the age, gender and
ethnicity of complainants.
Applicant Behaviour
1. UCL aims to ensure that staff, students, applicants, visitors and all others associated with the
university are treated with dignity, respect and equity... | UCL Academic Manual |
Francis Fukuyama is a senior fellow at the Freeman Spogli Institute for International Studies (FSI)
and Mosbacher Director of the Center on Democracy, Development and the Rule of Law, Stanford
University. Andrew Grotto is the William J. Perry International Security Fellow at FSI’s Cyber
Policy Center and Director of th... | Social_Media_and_Democracy |
Let ’s say you see a creeper nearby , and you have not defeated a
creeper before . You can ask a question like :
Question : How to defeat the creeper ?
Concept : creeper
Let ’s say you last completed task is " Craft a wooden pickaxe ". You can
Question : What are the suggested tasks that I can do after crafting a
... | VOYAGER- An Open-Ended Embodied Agent with Large Language Models |
[64] Ramakrishna Vedantam, C. Lawrence Zitnick, and Devi Parikh. Cider: Consensus-based image description
evaluation. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA,
June 7-12, 2015, pages 4566–4575. IEEE Computer Society, 2015.
16 | DOCLLM |
//github.com/kingoflolz/mesh-transformer-jax.
[193] Chaojun Wang and Rico Sennrich. 2020. On Exposure Bias, Hallucination and Domain Shift in Neural Machine
Translation. In 2020 Annual Conference of the Association for Computational Linguistics. Association for Computational
Linguistics (ACL), 3544–3552.
[194] Hongmi... | SurveyofHallucinationinNatural Language Generation |
In abstraction refinement we first solve the abstract version of the instance, and then refine this into a solution for
the original ground instance. One way to do this, that has been common in planning (cf. the articles by Knoblock [65]
and Bacchus and Yang [3]), is to map th... | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
31/08/2023, 09:31
Job Application for Machine Learning Engineer at Stability AI
Machine Learning Engineer
at Stability AI (View all jobs)
Tokyo, Japan
About Stability:
Stability AI is a community and mission driven, open-source artificial intelligence company that cares
deeply about real-world implications and appli... | Job Application for Machine Learning Engineer at Stability AI |
PIFu [8] demonstrates high-quality human digitization
for fashion poses, like standing or walking. However, we
argue that purely relying on 2D image feature is insufficient
to handle severe occlusions and large pose variations for
single-image 3D human recovery, especially when ideal
high quality dataset (covering suffic... | PaMIR- Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction |
be differentially effective across issue areas. For example, in a meta-analysis
of studies of misinformation correction, Walter and Murphy (2018) find that
corrections are more effective for health-focused misinformation than for
political and scientific misinformation. Second, misinformation may vary in
its affective co... | Social_Media_and_Democracy |
2.2.2 Quality filtering
Quality filtering is another key step in construct-
ing a suitable pretraining dataset, since public
datasets like Common Crawl 1 and multilingual
datasets (Kreutzer et al., 2022) usually contain low-
quality data that hampers the training of LLMs.
Existing works usually perform quality filterin... | DataManagementForLargeLanguageModels-ASurvey |
anintelligence.However,withtheemergenceoffoundationmodels,thisnotionisbeingchallenged.Increasingevidenceindicatesthattheabilitytocreateadvancedtoolsisnolongerlimitedtohumanbeings.Forinstance,largecodemodels(Chenetal.,2021)cangenerateexecutableprogramsbasedonlanguagedescription.Theseprogramscanbedeemedastoolstohelpaccom... | Tool Learning with Foundation Models |
How to Write Your Introduction
Introduction is one of the most difficult parts of a PhD proposal. Introduction opens a dialogue with your
examiners or readers. Your introduction can make or break you during the presentation. Your introduction must
convince your reader that you are the right pers... | How to Write Your PhD Proposal- A Step-By-Step Guide |
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez,
Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Advances in neural information
processing systems, pages 5998–6008, 2017.
Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill,... | ST-MOE- DESIGNING STABLE AND TRANSFERABLE SPARSE EXPERT MODELS |
0.0
9.1
71.4 57.1 81.2 68.8 54.5 72.7 65.5 65.5 93.8 50.0 37.5 50.0 54.5 54.5 36.4 45.5 77.3 68.2
18.2 18.2 71.4 78.6 68.8 68.8 63.6 45.5 89.7 62.1 87.5 81.2 62.5 62.5 63.6 72.7 54.5 27.3 86.4 90.9 | Scaling Instruction-Finetuned Language Models |
quality. These GAN-based vocoders, which include MelGAN MelGAN [275]and HiFIGAN
[268], are capable of producing high-fidelity raw audio by conditioning on mel spectrograms.
Furthermore, they can synthesize audio at speeds several hundred times faster than real-time
on a single GPU, as evidenced by research conducted in... | AReviewofDeepLearningTechniquesforSpeechProcessing |
making language models stronger zero-shot learners.
arXiv preprint arXiv:2210.02969.
Wenpeng Yin, Jia Li, and Caiming Xiong. 2022. Con-
TinTin: Continual learning from task instructions.
In Annual Meeting of the Association for Computa-
tional Linguistics (ACL).
Eric Zelikman, Jesse Mu, Noah D Goodman, and
Yuhuai Tony ... | SELF-INSTRUCT- Aligning Language Model with Self Generated Instructions |
It should be noted that the interface selection should align with the capabilities and limitations of the foundation
model. For instance, language foundation models are trained to generate text and may be better suited for the
semantic interface. Similarly, a multimodal foundation model that combines visual and textual... | Tool Learning with Foundation Models |
WhisperABCDEFGHI ASR human transcription computer-assisted051015202530Word Error Rate (%) Robust Speech Recognition via Large-Scale Weak Supervision
11
Figure 8. Zero-shot Whisper performance scales reliably across tasks and languages with increasing model size. Lightly shaded
lines represent in... | RobustSpeechRecognitionviaLarge-ScaleWeakSupervision |
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Utility. Currently, LLM-powered autonomous agents primarily function as human assistants, ac-
cepting tasks delegated by humans to either independently complete assignments or assist in human
task completion [114; 182; 389; 397; 413; 422]. Therefore, the effectiveness and utility during task
execution are crucial evalu... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
1020304050KRetrievedDocs394041424344NQExactMatchRAG-TokRAG-Seq1020304050KRetrievedDocs4050607080NQAnswerRecall@KRAG-TokRAG-SeqFixedDPRBM251020304050KRetrievedDocs4850525456Bleu-1/Rouge-LscoreRAG-TokR-LRAG-TokB-1RAG-SeqR-LRAG-SeqB-1 General-Purpose Architectures for NLP Prior work on general-purpose architectures for NL... | Retrieval-AugmentedGenerationfor Knowledge-IntensiveNLPTasks |
Parameter-Efficient Fine-Tuning Methods for
Pretrained Language Models: A Critical
Review and Assessment
Lingling Xu, Haoran Xie, Si-Zhao Joe Qin, Xiaohui Tao, Fu Lee Wang
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6.6. Responsible Governance
Across the responsible development process, we undertake ethics and safety reviews with the Google
DeepMind’s Responsibility and Safety Council (RSC),10 an interdisciplinary group which evaluates
Google DeepMind’s projects, papers and collaborations against Google’s AI Principles. The RSC
pr... | gemini_1_report |
We evaluate CODEFUSION on NL-to-code for
three different languages: Python (Yin et al., 2018),
Bash (Lin et al., 2018), and conditional formatting
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rules in Microsoft Excel (Singh et al., 2022). Our
results show that CODEFUSION’s (7... | CODEFUSION |
As this is not the main focus on our paper, this section is by no means exhaustive and much more work is
required in order to reach more conclusive findings. Rather, we only give some preliminary evidence which may
hopefully motivate future work.
Attention heads.
In the study of attention heads, we take advantage of t... | TinyStories-HowSmallCanLanguageModelsBeandStillSpeak CoherentEnglish? |
can cause anoverflow.To fix this issue, you can try thefollowing:1. Check the load size: Make sure youare not overloading the washingmachine.2. Use the right amount of detergent:Use the recommended amount ofdetergent for the size of the load.3. Clear the drain: Make sure thedrain is clear of any debris.4. Check for mal... | MiniGPT-4- Enhancing Vision-Language Understanding with Advanced Large Language Models |
September 30,
2022
2023
North America
Net sales
Operating expenses
Operating income (loss)
International
Net sales
Operating expenses
Operating loss
AWS
Net sales
Operating expenses
Operating income
Consolidated
Net sales
Operating expenses
Operating income
Total non-operating income (expense)
Benefit (provisio... | AMZN-Q3-2023-Earnings-Release |
Mixtral is pretrained with multilingual data using a context size of 32k tokens. It either matches
or exceeds the performance of Llama 2 70B and GPT-3.5, over several benchmarks. In particular, | Mixtral of Experts paper |
Pythia: A Suite for Analyzing Large Language Models
Across Training and Scaling
Stella Biderman * 1 2 Hailey Schoelkopf * 1 3 Quentin Anthony 1 Herbie Bradley 1 4 Kyle O’Brien 1
Eric Hallahan 1 Mohammad Aflah Khan 5 Shivanshu Purohit 1 USVSN Sai Prashanth 1 Edward Raff 2
Aviya Skowron 1 Lintang Sutawika 1 6 Oskar va... | Pythia- A Suite for Analyzing Large Language Models Across Training and Scaling |
Num files Percentage
97.170
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1.167
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11,034
0.77
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1,899
0.101
1,441
0.092
1,319
0.089
1,273
1,081
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592
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100
Table 3: Overview of the initially col... | StarCoder_paper (1) |
Organizations are increasingly
using the Lakehouse for data
warehousing, as evidenced
by the high growth of data
integration tools dbt and
Fivetran, and the accelerated
adoption of Databricks SQL.
We hope that by sharing these trends, data leaders will be able to benchmark
their organizations and gain insights ... | databrick 2023 report |
3.1.1 RNN Models
time 𝑡 can be computed as
Vanilla RNN. Give input sequence of T states (𝑥1, . . . , 𝑥𝑇) with 𝑥𝑖 ∈ R𝑑, the output state at
𝑦𝑡 = 𝑊ℎ𝑦ℎ𝑡 + 𝑏𝑦
ℎ𝑡 = H(𝑊ℎℎℎ𝑡−1 + 𝑊𝑥ℎ𝑥𝑡 + 𝑏ℎ)
(1)
(2)
where 𝑊ℎℎ,𝑊ℎ𝑥,𝑊𝑦ℎ are weight matrices and 𝑏ℎ, 𝑏𝑦 are bias vectors. H is non-linear activation... | AReviewofDeepLearningTechniquesforSpeechProcessing |
published after the claimed knowledge cutoff of GPT-3.5 (September 2021), and it has not been
publicly released yet, making it unlikely that it has appeared in the training data of LLM.
Evaluation metrics. In each experiment, every compared method makes three attempts to predict
successful solutions for an unseen task.... | MLCopilot- Unleashing the Power of Large Language Models in Solving Machine Learning Tasks |
attention network, operating in the hidden di-
mension of T5-base, 768, was implemented fol-
lowing Jaegle et al. (2021), first producing a
fixed-length output sequence by using a cross
attention layer with a fixed number of query vec-
tors (overall 7M learned parameters), and then
applying 2 self-attention layers over th... | STANDING ON THE SHOULDERS OF GIANT FROZEN LANGUAGE MODELS |
For each dataset, we report the best previous sota result. For generation tasks, we generally report ROUGE-2
following the advice of (Gehrmann et al., 2022). For the rest of the datasets, we report the dominant metric
that is reported in prior work. For BLEU scores, we use sacrebleu. For commonsense reasoning tasks, we... | UL2- Unifying Language Learning Paradigms |
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