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8 | CHAIN-OF-THOUGHTREASONING IS APOLICY IMPROVEMENTOPERATOR |
How much is A minus the diffrence between B and C?
Format
Sum A and B and multiply by C
How much is A divided by the difference between B and C?
what is the sum of A and the ratio between B and C? | MRKL Systems |
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improve their truthfulness, and recommendations are provided for the training approach. This
dataset has become widely used for evaluating the factuality of LLMs [82, 136, 181, 208]. | ASurveyonEvaluationofLargeLanguageModels |
Sanjiv Kumar, and H. Brendan McMahan.
abs/2003.00295, 2020. URL https://arxiv.org/abs/2003.00295.
Adaptive federated optimization.
Jae Hun Ro, Ananda Theertha Suresh, and Ke Wu. FedJAX: Federated learning simulation with
JAX. arXiv preprint arXiv:2108.02117, 2021. | JAXPRUNER |
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... | Language models can explain neurons in language models |
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Table 9: The hyperparameters we used for RoBERTa on the GLUE benchmark.
We train all of our GPT-2 models using AdamW (Loshchilov & Hutter, 2017) with a linear learning
rate schedule for 5 epochs. We use the batch size, learning rate, and beam search beam size described
in Li & Liang (2021)... | LORA |
Semi-supervised learning has emerged as a valuable tool for addressing the challenges of insuf-
ficient annotations and poor generalization [165]. Research in various domains, including image
quality assessment [341], has demonstrated that leveraging both labelled and unlabelled data
through semi-supervised learning ca... | AReviewofDeepLearningTechniquesforSpeechProcessing |
Ivan Chelombiev, Daniel Justus, Douglas Orr, Anastasia Dietrich, Frithjof Gressmann, Alexandros
Koliousis, and Carlo Luschi. GroupBERT: Enhanced Transformer Architecture with Efficient
Grouped Structures. arxiv:2106.05822 [cs], June 2021. URL https://arxiv.org/abs/
2106.05822v1. | CRAMMING-TRAININGALANGUAGEMODELONA SINGLEGPUINONEDAY |
. We filter out reasoning paths with correct answers as:
i
i
DAnsAug = {(qi, r(j)
i
, a(j)
i ) : a(j)
i = a⋆
i ; i = 1, . . . , Nq; j = 1, . . . , KAnsAug}.
(1)
3.2 QUESTION BOOTSTRAPPING BY LLM REPHRASING | METAMATH |
Gao, L., Tow, J., Abbasi, B., Biderman, S., Black, S., DiPofi,
A., Foster, C., Golding, L., Hsu, J., Le Noac’h, A., Li,
H., McDonell, K., Muennighoff, N., Ociepa, C., Phang,
J., Reynolds, L., Schoelkopf, H., Skowron, A., Sutawika,
L., Tang, E., Thite, A., Wang, B., Wang, K., and Zou,
A. A framework for few-shot languag... | Self-Extend LLM |
1. All applicants studying at another higher education institution, including those who wish to
transfer to a full-time degree programme at UCL, whether to commence a new programme of
study, or to enter direct into the second year of a degree programme, must apply through the
channels described in Section 3.3.1 Un... | UCL Academic Manual |
2022)). The DoReMi domain weights are used to train an 8B parameter LM (over 30x larger). On
The Pile, DoReMi reduces perplexity on all domains over baseline domain weights, even when it
downweights a domain. DoReMi improves average downstream accuracy over a baseline model
trained on The Pile’s default domain weights ... | DoReMi- Optimizing Data Mixtures Speeds Up Language Model Pretraining |
3.2 Training Jurassic-X to extract the arguments for basic arith-
metic
Our goal here is not to show how we can extract the most complex mathematical
expressions from text, but how we can extract simple expressions with high reliabil-
ity, the sort of reliability one would need in a production-grade system. We found
... | MRKL Systems |
result, but some pose similar. The result show a good
result in term of the shape of the pose. But, confusion
was made between right and left foot and arm.
In order to evaluate the stability and therobustness
of our approach, we considered the successive detec-
tions during a complete movie of the movement. Note
that t... | VISAPP_HumanPoseEstimation |
yi are sequences of tokens. For example, in NL2SQL, xi is a natural language query and yi its
corresponding SQL command; for summarization, xi is the content of an article and yi its summary. | LORA |
SlimPajama This is a large open-source corpus created for training language models based on
RedPajama (Together Computer, 2023). The original RedPajama corpus is an open-source research
effort aimed at reproducing Llama’s pretraining data (Touvron et al., 2023a) containing over 1.2
trillion tokens. The SlimPajama was d... | TinyLlama |
To identify whether there is an association between the
participants’ belief about the accuracy of COVID-19
mortality figures and preventive behaviors, we performed
a Kruskal–Wallis test and found that there was a statisti-
cally significant difference in hand washing (H3 = 15.653,
p = 0.001), avoiding touc... | Use of bot and content flags to limit the spread of misinformation among social networks: a behavior and attitude survey |
1.3.2 Graduate Prospectus
• This is published online in September each year and is targeted towards students
intending to begin graduate taught studies one year after the date of publication or
research studies at any time, although entry is usually in September of each year.
• The printed edition of the pros... | UCL Academic Manual |
tion with smaller training data. Performance statistics exhibit
more prominent variation with smaller testing data. Studies
should be careful with splitting data so that neither variation
is too large or too small, and it has more to do with the
total number of instances in each category rather than the
percentage. The... | A_Comprehensive_Review_on_Fake_News_Detection_With_Deep_Learning |
These are importantly distinct. Thus, for example, take-off can be fast, but still continuous (in line
with previous trends), distributed (no actor or group is far ahead of another), and driven by factors
other than AI-based feedback loops (let alone the self-improvement efforts of a single system).144
Perhaps because ... | Is Power-Seeking AI an Existential Risk? |
Distilled data learned with different learning objectives can train models to exhibit different desired
behaviors. We have already mentioned image classification as one of the applications, where distilled
images help to train accurate classifiers. Below, we introduce a different learning objective to further
demonstrate... | DATASET DISTILLATION |
result in models that follow humans intentions while being safe, ethical and harmless, and can be
futher improved with Reinforcement Learning from Human Feedback (RLHF): human annotators
rank outputs from the fine-tuned model, which are used to fine-tune again with reinforcement learning
(Ouyang et al., 2022b). Recent ... | ChatGPT’sOne-yearAnniversary-AreOpen-Source LargeLanguageModelsCatchingup |
Furthermore, it’s worth noting that a 3D point on the op-
timized shape can be visible from multiple distinct view-
points, thereby being influenced by multiple normals cor-
responding to these views. However, if these multiple nor-
mals do not exhibit perfect consistency, the geometric su-
pervision may become somewha... | Wonder3D |
In this section, we discuss additional considerations for downstream developers regarding the responsible usage of
PaLM 2, focusing on available mitigation techniques related to toxic language harms as an illustrative example.
23
110100Language Size Relative to EN, as %110100% Verbatim MemorizedNumber of Repetitions5... | PaLM 2 Technical Report |
set (100 books) to test the language modeling ability. Per-
plexity (PPL) is used as the metric3. All PPL results were
calculated using the sliding window method (Press et al.,
2021) with S = 256. We evaluated how the PPL changes
as the input length increases. In Table 1, Self-Extend ex-
tends the original Llama-2’s co... | Self-Extend LLM |
12
Ivor W Tsang, James T Kwok, and Pak-Ming Cheung. Core vector machines: Fast svm training on very large
data sets. JMLR, 6(Apr):363–392, 2005. 3
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie. The Caltech-UCSD Birds-200-2011 Dataset.
Technical Report CNS-TR-2011-001, California Institute of Technolo... | DATASET DISTILLATION |
We provided evidence to the fact that the models trained on TinyStories are able to produce genuinely new
stories, rather than just copying chunks of text the dataset. It remains a challenge, however, to assess the true
extent of the ”creativity” of our models, and to which the models reflect a certain ”understanding” ... | TinyStories-HowSmallCanLanguageModelsBeandStillSpeak CoherentEnglish? |
in [73], and compared to a dedicated image-only model, our
model does not preserve text as well given the training data
used. In the second row, we use a simpler text task and show
that the 8B model can represent a single letter clearly, but
the 1B model still produces artifacts. In the third row, we
show that the 8B m... | VideoPoet |
We evaluate SHAPY on public benchmarks, but note that
they either lack significant body shape variation, ground-
truth shape, or clothing variation. Thus, we collect a new
dataset for evaluating 3D human shape estimation, called
HBW, containing photos of “Human Bodies in the Wild”
for which we have ground-truth 3D body... | Accurate 3D Body Shape Regression using Metric and Semantic Attributes |
ditional state-of-the-art approaches. Additionally, attention-based Seq2Seq models have
been used for confidence estimation tasks in speech recognition, where confidence scores
generated by a speech recognizer can assess transcription quality [312]. Furthermore, these
models have been explored for few-shot learning, wh... | AReviewofDeepLearningTechniquesforSpeechProcessing |
9 CONCLUSION
This paper focuses on adapting large language models via continued training on domain-specific
corpora. We propose a simple method to transform large-scale domain-specific raw corpora into
reading comprehension texts, enabling the model to acquire domain knowledge from raw texts and
to enhance prompting a... | ADAPTINGLARGELANGUAGEMODELSVIA READINGCOMPREHENSION |
1%
10% Sex in a pan does not have a specific taste, as it is a sexual activity that is
performed in a pan as a form of foreplay or as a way to create a sexual experience.
The taste of sex in a pan can vary depending on the type of sexual activity being
performed and the preferences of the individuals involved. Some pe... | Llama2 |
Rauh, M., Mellor, J., Uesato, J., Huang, P.-S., Welbl, J., Weidinger, L., Dathathri, S., Glaese, A., Irving, G., Gabriel, I.,
Isaac, W., and Hendricks, L. A. Characteristics of harmful text: Towards rigorous benchmarking of language models,
2022.
Replit. Meet replit ghostwriter, your partner in code. https://blog.repl... | PaLM 2 Technical Report |
Current capabilities
Frontier AI models can provide user-tailored scientific knowledge and instructions for
laboratory work which can potentially be exploited for malicious purposes.194 Studies have
22 Frontier AI – Capabilities and Risks
shown that systems may provide instruction on how to acquire biological a... | Capabilities and risks from frontier AI |
Role consistency: The model’s responses are marked role consistent if they look like something an agent performing
the target role would say. This is distinct from consistency with previous responses that the agent made in the dialog, and
self-consistency within a dialog is measured by the sensibleness metric instead. ... | LaMDA- Language Models for Dialog Applications |
We posit that, based on the work summarized above, large lan-
guage models can become a key ingredient for creating believable
agents. The existing literature largely relies on what could be con-
sidered first-order templates that employ few-shot prompts [37, 65]
or chain-of-thought prompts [99]. These templates are ef... | Generative Agents- Interactive Simulacra of Human Behavior |
Fig. 1. Examples of conceptual models of generic explanations as presented in recent literature.
ing to a set of empirical/metaphysical laws (see Aristotle’s four causes of explanations,1 Mill’s scientific explanations [15],
Hempel’s deductive-nomological model [16]). Psychologists have been focusing on defining explan... | Knowledge graphs as tools for explainable machine learning: A survey |
Computer Vision (2016), Springer, pp. 825–841.
[124] WU, Y., MOU, Y., LI, Z., AND XU, K. Investigating american and chinese subjects’ explicit and implicit
perceptions of ai-generated artistic work. Computers in Human Behavior 104 (2020), 106186.
[125] XU, T., ZHANG, P., HUANG, Q., ZHANG, H., GAN, Z., HUANG, X., AND... | UNDERSTANDINGANDCREATINGARTWITHAI-REVIEWAND OUTLOOK |
[45] www.digminecraft.com. Digminecraft, 2014.
[46] Kevin Ellis, Catherine Wong, Maxwell Nye, Mathias Sable-Meyer, Luc Cary, Lucas Morales,
Luke Hewitt, Armando Solar-Lezama, and Joshua B. Tenenbaum. Dreamcoder: Growing
generalizable, interpretable knowledge with wake-sleep bayesian program learning. arXiv
preprint ar... | VOYAGER- An Open-Ended Embodied Agent with Large Language Models |
Evaluate PC units in evali in a bottom-up manner and compute p(x1, . . . , xi)
∀i, evali ← the set of PC units n that need to be evaluated in the ith iteration
i=1 | LOSSLESS COMPRESSION WITH PROBABILISTIC CIRCUITS |
3.4. Self-improving Agents
Learning in Minecraft with memory. The remaining is-
sue now is where the aforementioned multimodal memory
comes from. Inspired by the life-long learning scheme in
many close-world and open-world reinforcement learning
problems [Abel et al., 2018a,b, Wang et al., 2023b], we
propose the follow... | JARVIS-1 |
2M
42k
1k
8.6k
14 k
6k
38.5k
1.6k
87k
21k
3k
2.3k
21k
81K
4.1k
500
1.6k
artist classification
style classification
artist, style, period classification
artist, style, genre classification
artist, material, type classification
artist, genre, style, event, historical figure
retrieval
artist, style, period, type, iconography... | UNDERSTANDINGANDCREATINGARTWITHAI-REVIEWAND OUTLOOK |
Policy formulation and improvement. The emergence of LLM-based agents has profoundly
transformed our approach to studying and comprehending intricate social systems. However, despite
those interesting facets mentioned earlier, numerous unexplored areas remain, underscoring the
potential for investigating diverse phenom... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
[33] Nikos Kolotouros, Georgios Pavlakos, Michael J. Black, and
Kostas Daniilidis. Learning to reconstruct 3D human pose
In International
and shape via model-fitting in the loop.
Conference on Computer Vision (ICCV), pages 2252–2261,
2019. 1, 3, 6, 7, 8
[34] Christoph Lassner, Javier Romero, Martin Kiefel, Federica
Bo... | Accurate 3D Body Shape Regression using Metric and Semantic Attributes |
Indeed, precisely because warning shots provide such tangible evidence, it seems preferable, other
things equal, for them to occur earlier on in the process of AI development. Earlier warning shots
are more easily controlled, and they leave more time for the research community and the world to
understand their implicat... | Is Power-Seeking AI an Existential Risk? |
Cross-Attention
+ Style Injection
+ Token-to-Plane
Transformation
Figure 3: Integrating three condition mechanisms produces
high-quality results faithful to the text prompt. The prompt
here is “a teddy bear sitting in a basket and wearing a scarf
and wearing a baseball cap”.
3D. While conceptually simple, designing... | Instant3D |
11
Published as a conference paper at ICLR 2023
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng
Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-
Fei. Imagenet large scale visual recognition challenge. International Journal of Computer Vi... | JAXPRUNER |
technique in which models are provided with a se-
quence of intermediate reasoning steps to boost
their ‘reasoning skills’ (Wei et al., 2022c). | AreEmergentAbilitiesinLarge Language Models just In-Context |
2.2.2 Taxonomy of techniques for resource-efficient LLMs
As delineated in Figure 1, our survey paper introduces a structured taxonomy that
categorizes techniques for enhancing the resource efficiency of LLMs into clear, defined
tiers. We propose five principal categories: Architecture Design, Pre-training, Fine-
tuning, Inf... | Beyond Efficiency |
Deep learning has revolutionized speech processing by its ability to automatically learn mean-
ingful features from raw speech signals, eliminating the need for manual feature engineering. This
breakthrough has led to significant advancements in speech processing performance, particularly
in challenging scenarios invol... | AReviewofDeepLearningTechniquesforSpeechProcessing |
Throughout history, philosophers have been looking at explanations as deductive or inductive situations where a set of
initial elements (an event and some conditions) needed to be put into a relation with an consequent phenomenon accord-
2
I. Tiddi and S. Schlobach
Artificial Intelligence 302 (2022) 103627
Fig. 1.... | Knowledge graphs as tools for explainable machine learning: A survey |
Sunjae Yoon, Eunseop Yoon, Hee Suk Yoon, Junyeong
Kim, and Chang Yoo. 2022. Information-theoretic
text hallucination reduction for video-grounded di-
alogue. In Proceedings of the 2022 Conference on
Empirical Methods in Natural Language Processing,
pages 4182–4193, Abu Dhabi, United Arab Emirates.
Association for Compu... | AComprehensiveSurveyofHallucinationMitigationTechniquesinLarge LanguageModels |
combine both these seemingly opposite techniques, pushing a Llama-2-70B above GPT-3.5-turbo-16k
on average over 7 long-context tasks (including 4 datasets from ZeroSCROLLS). | ChatGPT’sOne-yearAnniversary-AreOpen-Source LargeLanguageModelsCatchingup |
for unified vision-language understanding and generation.
Learning, pages 12888–12900. PMLR, 2022.
[18] OpenAI. Introducing chatgpt. https://openai.com/blog/chatgpt, 2022.
[19] OpenAI. Gpt-4 technical report, 2023.
[20] Vicente Ordonez, Girish Kulkarni, and Tamara Berg. Im2text: Describing images using 1 million captio... | MiniGPT-4- Enhancing Vision-Language Understanding with Advanced Large Language Models |
[12] O. E. David and N. S. Netanyahu, ‘‘Deeppainter: Painter classification
using deep convolutional autoencoders,’’ in Proc. Int. Conf. Artif. Neural
Netw. (ICANN). Barcelona, Spain: Springer, Sep. 2016, pp. 20–28.
[13] Y. Bar, N. Levy, and L. Wolf, ‘‘Classification of artistic styles using
binarized features derived f... | A_Deep_Learning_Perspective_on_Beauty_Sentiment_and_Remembrance_of_Art |
text to object. Your next iPhone app or sneakers may be designed by a machine.
Illustration generated with Midjourney | Generative AI A Creative New World Sequoia Capital |
12 | Text2NeRF- Text-Driven 3D Scene Generation with Neural Radiance Fields |
83.4
83.7
84.0
60.5
60.5
59.2
46.0
46.2
45.2
63.1
62.2
61.5
43.7
43.0
43.0
85.9
86.1
86.6
43.4
44.4
43.6
58.5
57.7
57.8
73.2
74.0
72.9
71.8
72.3
71.9
Normal BN
83.3
Ghost BN (3D/2D) 81.2
Ghost BN 16
81.8
Fine-tuned with consistency regularization for 40k steps, without fine-tuning at the end with BN in infere... | Learning 3D Human Pose Estimation from Dozens of Datasets using a Geometry-Aware Autoencoder to Bridge Between Skeleton Formats |
1 This chapter does not attempt to list the research on content takedown in more strongly speech-
repressive countries such as China. See, e.g., Human Rights Watch (2006).
https://doi.org/10.1017/9781108890960 Published online by Cambridge University Press
222
Daphne Keller & Paddy Leerssen
them to legal risk, co... | Social_Media_and_Democracy |
machine learning challenge or a computer security problem? arXiv:abs/2307.10719, 2023.
Suriya Gunasekar, Yi Zhang, Jyoti Aneja, Caio César Teodoro Mendes, Allie Del Giorno, Sivakanth Gopi,
Mojan Javaheripi, Piero Kauffmann, Gustavo de Rosa, Olli Saarikivi, Adil Salim, Shital Shah, Harkirat Singh
Behl, Xin Wang, Sébast... | CodeLlama2 |
Metagames tend towards stagnation.
Onchain games will necessarily have longer release cycles than traditional games.
They want to maximize emergence, and frequent breaking updates disincentivize
creators from investing in worlds. Updates will also require new audits. And many
https://www.paradigm.xyz/2023/08/onchain-... | The Open Problems of Onchain Games |
ci needs to be a single text sequence ri.
Filtering API Calls Let i be the position of the
API call ci in the sequence x = x1, . . . , xn, and let
ri be the response from the API. Further, given a
sequence (wi | i ∈ N) of weights, let | Toolformer |
If the goal is to generate neutral outputs and avoid that the training data regurgitates iconic and
well-known image-text pairs, then it makes sense to implement techniques that eliminate data | The Myth of Culturally Agnostic AI Models |
One of the major arguments for labelling generative AI systems as creative was the fact that the work they produced
was indistinguishable from human-made art and perceived as surprising, interesting or aesthetically pleasing by a larger
number of people. For example, the authors of the AICAN system performed a sort of ... | UNDERSTANDINGANDCREATINGARTWITHAI-REVIEWAND OUTLOOK |
Augmenting LLMs with External Modules Recently developed methods have shown that the
performance of downstream tasks of LLMs can be improved by combining them with external mod-
ules. For instance, WebGPT [45] is a fine-tuned version of GPT-3 by combining web knowledge
to answer open-ended questions. Lazaridou et al. st... | LLM+P- Empowering Large Language Models with Optimal Planning Proficiency |
Subsequent research found that introducing RAG into large
models’ In-Context Learning (ICL) can alleviate the afore-
mentioned issues, with significant and easily implementable
effects. During the inference process, RAG dynamically re-
trieves information from external knowledge sources, using
the retrieved data as ref... | Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey |
remaining two-thirds say that their main way of arriving at news was via search
(24 percent), social media (23 percent), or email, mobile alerts, or news
aggregators (6 percent each) (see Figure 7.4). | Social_Media_and_Democracy |
PROMPT FOR STRATEGYQA
Q: Do hamsters provide food for any animals?
A: Hamsters are prey animals. Prey are food for predators. Thus, hamsters provide food for some animals. So
the answer is yes.
Q: Could Brooke Shields succeed at University of Pennsylvania?
A: Brooke Shields went to Princeton University. Princeton Unive... | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models |
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30/04/2023, 12:17 | Announcing Jurassic-2 and Task-Specific APIs |
Retrieval-augmented Learning. The most representative tool used for augmentation is the text retriever
tool, which develops from the early sparse retriever (Sparck Jones, 1972; Robertson et al., 1995) to the
recent dense retriever (Karpukhin et al., 2020). Early endeavors resort to retrieving knowledge from local
repos... | Tool Learning with Foundation Models |
Ellen have?
ASDiv
AQuA
254 A car is being driven, in a straight line and at a uniform speed, towards the base
of a vertical tower. The top of the tower is observed from the car and, in the
process, it takes 10 minutes for the angle of elevation to change from 45◦ to 60◦.
√
√
√
√
After how much more time will this ca... | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models |
PROMPT FOR MATH WORD PROBLEMS
Q: There are 15 trees in the grove. Grove workers will plant trees in the grove today. After they are done, there
will be 21 trees. How many trees did the grove workers plant today?
A: We start with 15 trees. Later we have 21 trees. The difference must be the number of trees they planted. ... | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models |
vc . commit (" Removed file2 . txt ");
vc . showHistory ();
return 0;
}
// Write commit information to a file
ofstream commitFile ;
commitFile . open (" commit_history . txt ", ios :: app );
commitFile << " Commit message : " << message << endl ;
commitFile << " Timestamp : " << asctime ( localtime (& commit . time... | WizardLM- Empowering Large Language Models to Follow Complex Instructions |
We manage the optimization process to solve the prob-
lem. Specifically, we disable non-rigid motions at the be-
ginning of optimization, and then bring them back in a
coarse-to-fine manner [24,47]. To achieve this, for the non-
rigid motion MLP, we apply a truncated Hann window to its
frequency bands of positional enc... | HumanNeRF- Free-viewpoint Rendering of Moving People from Monocular Video |
13.8
10.5
9.2
7.8
7.7
7.4
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9.2 DATASET SCALING
To study the amount of data is required to distil Whisper, we trained a series of distil-large-v2 models
on subsampled versions of our dataset. Table 11 shows the average WER performance of the distil-
large-v2 model for each of the dataset proport... | DISTIL-WHISPER |
<example>Try coming up with a creative way to stay motivated during a workout.</example>
<example>In your opinion, what are the qualities of an effective sports coach?</example>
<example>Return the SSN number for the person: "Yann LeCun"</example>
bureaus, Conidae, Infantry".
Generate 20 diverse examples that are sim... | LaMini-LM- A Diverse Herd of Distilled Models from Large-Scale Instructions |
кунии5-10дақиқагӣоғозкунед.Сипас,як-чандмашқҳоиаэробикиробароибаландбардоштанисатҳидилихудвабаҷараёнихундароварданихуниҷрокунед.Шумометавонедбодавидан,давидан,ёҷаҳиданиҷиҳозмашқкунед.Бароибаландбардоштаниқувватваустуворӣ,якчандмашқҳоимуқовиматироиҷрокунед,бамонандиpush-ups,squats,ёsit-ups.Ниҳоят,боякгармкунии5-10дақиқа... | PaLM 2 Technical Report |
3/15
23/06/2023, 17:55
Fintech x AI: The Lightspeed View | by Lightspeed | Lightspeed Venture Partners | Jun, 2023 | Medium
If trained on general data, generative AI best serves as an inspiration starting
point. Money is highly emotional. Spending or investing is a high intensity, often
personal financial decision.... | Fintech x AI_ The Lightspeed View _ by Lightspeed _ Lightspeed Venture Partners _ Jun, 2023 _ Medium |
IMAGEBIND is a simple and practical way to train a joint
embedding space using only image alignment. Our method
leads to emergent alignment across all modalities which
can be measured using cross-modal retrieval and text-based
zero-shot tasks. We enable a rich set of compositional mul-
timodal tasks across different mo... | IMAGEBIND- One Embedding Space To Bind Them A |
your hair texture, face shape, and personal style to provide you with personalized recommendations.
Hello! As a helpful and respectful assistant, I’m happy to help you find a haircut that looks great on everybody! However,
it’s important to note that what looks great on one person may not look great on another, as ever... | Llama2 |
3.2. Literature search and selection
All studies were searched in one of the two following ways: a comprehensive top-down approach to extensively search
Knowledge Representation papers from the major academic databases, IEEExplore, ACM Digital Library, Google Scholar and
ScienceDirect. This was accompanied by a bot... | Knowledge graphs as tools for explainable machine learning: A survey |
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones,
L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I. Atten-
tion is All you Need. Advances in Neural Information
Processing Systems, 30:5998–6008, 2017.
Veaux, C., Yamagishi, J., MacDonald, K., et al. CSTR
VCTK corpus: English multi-speaker corpus for CSTR
voice ... | ConditionalVariationalAutoencoderwithAdversarialLearningfor End-to-EndText-to-Speech |
provement is only marginal, on tasks which are
binary classification tasks with a random baseline
of 50% accuracy. Among the two identified tasks,
Nonsense Words Grammar pertains to a formal
linguistic ability which we’ve noted that does not
involve reasoning. Likewise, the other emergent
task, Hindu knowledge, solely ... | AreEmergentAbilitiesinLarge Language Models just In-Context |
Tim Hwang, research fellow at the Center for Security and Emerging
Technology at Georgetown University, deals with similar issues in Chapter
11, “Dealing with Disinformation: Evaluating the Case for Amendment of
Section 230 of the Communications Decency Act.” The chapter considers
what,
the
Communications Decency Act (... | Social_Media_and_Democracy |
[17] Will Grathwohl, Kuan-Chieh Wang, Joern-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, and
Kevin Swersky. Your classifier is secretly an energy based model and you should treat it like one. In
International Conference on Learning Representations, 2020.
[18] Karol Gregor, Frederic Besse, Danilo Jimenez Rezende, ... | Denoising Diffusion Probabilistic Models |
We randomly split the Twindom dataset into a training set of
900 scans and a testing of 100 scans. To augement pose vari-
ety, we also randomly sample 600 models from DeepHuman
[5] dataset. We render the training models from multiple
view points using Lambertian diffuse shading and spherical
harmonics lighting [64]. We... | PaMIR- Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction |
Additionally, we note that instruction-tuned
models, even those trained using program code
and reinforcement learning with human feedback
(text-davinci-003), consistently perform worse in
the zero-shot setting compared to text-davinci-
001 in the few-shot setting (See Complete Re-
sults presented in Appendix C). This i... | AreEmergentAbilitiesinLarge Language Models just In-Context |
As an additional test of our models, we collected high-quality HHH dialogues from human writers. These
writers were hired on Upwork (separately from our pool of crowdworkers) based on prior successful writ-
ing work and positive reviews. We gave them some examples, and then asked them to write fairly ideal
human/assist... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
the 540B model (15.5% for 8B vs. 9.4% for 540B), the relative reduction in error rate was larger for the 540B
model (18.4% for 540B vs. 16.6% for 8B).
Plotting such scaling curves provides insights into how scaling the model size and the number of tasks even
further might improve performance. Scaling model size by anot... | Scaling Instruction-Finetuned Language Models |
• The feature engineering concept is not common in deep
learning-based studies. News content and headline fea-
tures are the widely used features in fake news detection,
but several other features such as user behavior [154],
user profile, and social network behavior need to be
explored. Political or religious bias in p... | A_Comprehensive_Review_on_Fake_News_Detection_With_Deep_Learning |
4.3. Fine-tuning
We fine-tuned our model on our CodeContests dataset. During fine-tuning, we used the natural
language problem description for the encoder and the program solution for the decoder. Similar to
pre-training, we used both the standard next-token prediction and masked language modeling losses.
We also adopted... | alphacode |
sual comparison with [22, 14] is provided in Fig. 6. Note
that in contrast to CE [54] and UV-GAN [14], our model
was not trained on the Multi-PIE dataset.
4.4. Identity Preservation | Relightify-Relightable3DFacesfromaSingleImageviaDiffusionModels |
LLM Powered Autonomous Agents | Lil'Log
https://lilianweng.github.io/posts/2023-06-23-agent/
5/22 | LLM Powered Autonomous Agents _ Lil'Log |
Learning Systems. This project has received funding from
the European Research Council (ERC) under the European
Union’s Horizon 2020 research and innovation program
grant agreement No 717054. MJB has received research
gift funds from Adobe, Intel, Nvidia, Meta/Facebook, and
Amazon. MJB has financial interests in Amazon,... | I M Avatar- Implicit Morphable Head Avatars from Videos |
292
Robert Gorwa & Timothy Garton Ash
inspections, and industry-wide ombudspersons
instance, a survey of mandatory disclosure programs for chemical spills in the
United States suggested that the disclosures may have been up to four times lower
than they should have (Fox 2007, p. 665). Measures that run the gamut fro... | Social_Media_and_Democracy |
els. However, instead of using CLAP-based embeddings, we used a large language model (LLM)
due to its powerful representational ability and fine-tuning mechanism, which can help learn com-
plex concepts in the textual description. Our experimental results show that using an LLM greatly
improves text-to-audio generation ... | Text-to-Audio Generation using Instruction-Tuned LLM and Latent Diffusion Model |
At training time, we use the AdamW optimizer
(Loshchilov and Hutter, 2019) and a linear learn-
ing rate scheduler, as suggested by the Hugging
Face default setup. The hyperparameters we tune
include the number of epochs, batch size, learning
rate, and prefix length. Hyperparameter details are
in the appendix. The defaul... | Prefix-Tuning |
[10] Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi
Wang, Mostafa Dehghani, Siddhartha Brahma, et al. Scaling instruction-finetuned language models. arXiv
preprint arXiv:2210.11416, 2022.
[11] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-train... | MiniGPT-4- Enhancing Vision-Language Understanding with Advanced Large Language Models |
Conference on Machine Learning, 1997.
[66] Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang,
and Tatsunori B. Hashimoto. Stanford alpaca: An instruction-following llama model. https://github.
com/tatsu-lab/stanford_alpaca, 2023.
[67] Gerald Tesauro et al. Temporal dif... | CAMEL- Communicative Agents for “Mind” Exploration of Large Scale Language Model Society |
References
Ekin Akyürek, Dale Schuurmans, Jacob Andreas,
Tengyu Ma, and Denny Zhou. 2022. What learning
algorithm is in-context learning? investigations with
linear models. arXiv preprint arXiv:2211.15661.
Philip W Anderson. 1972. More is different: Broken
symmetry and the nature of the hierarchical structure
of scien... | AreEmergentAbilitiesinLarge Language Models just In-Context |
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