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Efficient Large Language Model Inference with Limited Memory
LLM in a flash:
Keivan Alizadeh∗, Iman Mirzadeh†, Dmitry Belenko‡ , Karen Khatamifard,
Minsik Cho, Carlo C Del Mundo, Mohammad Rastegari, Mehrdad Farajtabar§
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Abstr... | LLM in a flash |
3.1.4 Evaluation
In line with previous studies, we assess text-to-image gener-
ation performance using the MS-COCO [13] validation set.
To measure the quality of the generated images, we employ
Fr´echet Inception Distance (FID), Inception Score (IS), and
CLIP similarity metrics. The autoencoder’s performance
is evalua... | LDM3D- Latent Diffusion Model for 3D |
E.2 Benchmark Perplexity
Computation
To compute the perplexity for a given dataset, we
tokenize each document separately, divide the docu-
ment into segments of up to the maximum sequence
length of the model (1024 tokens for GPT-2, 2048
for GPT-3), and predict the logits of the each seg-
ment. The inputs to the model... | The Pile- An 800GB Dataset of Diverse Text for Language Modeling |
A.2StockA.2StockInstruction:Inthistask,youneedtosolvesomestockmarketqueriesusingthefollowingAPIs.ThePRICEAPIisabletoreturnthedailyormonthlyhighest,lowest,openorclosepriceofacompany,givenaspecificdateoradaterange.Itcontains4parameters.Thefirstoneis’type1’,whichindicateswhetherit’s’DAILY’or’MONTHLY’informationthatwearequer... | Tool Learning with Foundation Models |
St+1 = A(Mt,It+1)
Mt+1 ← Mt ∪ (It+1,St+1)
Note that the formulation above not only models AI-AI communicative scenarios, but it can also
be easily extended to model human-AI and multi-agent communicative scenarios. In Figure 1, we
observe that the AI user initiates the installation and import of essential Python libr... | CAMEL- Communicative Agents for “Mind” Exploration of Large Scale Language Model Society |
In this section, we will first define some simple transformation properties and then show that, despite their simplicity,
they are powerful enough to capture concepts like embeddings, retractions and homomorphisms.
3.1. Definitions
One of the main purposes of this paper is to model abstractions and abstraction-like m... | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
zero-shot prediction or generation. On the other hand, the decoder-only models, such as OpenAI’s GPTs
(Radford et al., 2018; 2019; Brown et al., 2020) use only the decoder of a Transformer. Although a decoder-
only model could do multi-tasking in a unified manner, it typically requires pre-existing representations or
e... | BiomedGPT |
Recognizing that these techniques may not be well-suited to identifying
subtle or indirect forms of online hate, researchers have also employed more
theoretically motivated approaches. For example, Burnap and Williams
(2016) and ElSherief, Kulkarni et al. (2018) incorporate the concept of
othering or “us vs. them” lang... | Social_Media_and_Democracy |
Jingfeng Yang, Hongye Jin, Ruixiang Tang, Xiaotian Han, Qizhang Feng, Haoming Jiang, Bing Yin, and Xia Hu
SQuADv2 [86], QuAC [21] and many other datasets, fine-tuned models have superior performance, while on CoQA [87],
LLMs perform as well as fine-tuned models [22].
In information retrieval (IR) tasks, LLMs are not ... | Harnessing the Power of LLMs in Practice- A Survey on ChatGPT and Beyond |
0-shot
01.83
04.27
09.15
Avg.
14.99
17.72
19.87
Pythia-1.0B
Pythia-1.4B
TinyLlama-1.1B
5In our initial dataset preprocessing, we inadvertently over-inserted end-of-sequence (EOS) tokens. This
excess of EOS tokens may have negatively affected the model by introducing substantial less meaningful signals
into the trai... | TinyLlama |
hypothesize that a fundamental issue with existing text-to-image models is the poor quality of the
text and image pairing of the datasets they were trained on, an issue that has been pointed out in other
works such as Jia et al. (2021). We propose to address this by generating improved captions for the
images in our da... | Improving Image Generation with Better Captions |
[35] Liqian Ma, Xu Jia, Qianru Sun, Bernt Schiele, Tinne Tuyte-
laars, and Luc Van Gool. Pose guided person image genera-
tion. arXiv:1705.09368, 2017. 2
[36] Ricardo Martin-Brualla, Rohit Pandey, Shuoran Yang, Pavel
Pidlypenskyi, Jonathan Taylor, Julien Valentin, Sameh
Khamis, Philip Davidson, Anastasia Tkach, Peter ... | HumanNeRF- Free-viewpoint Rendering of Moving People from Monocular Video |
InstructGPT Prompt → Based on the following passage, provide one bullet point of evidence of a positive
trend in the employment market, and one bullet point of a negative trend in the em-
ployment market, (use a "-" as a bullet point, Capitalize the first letter of the first
word for each bullet point, and include a peri... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
military influence in much of Asia. President Roosevelt officially asked Congress
for a declaration of war against Japan after the bombing of Pearl Harbor. | Direct Preference Optimization |
training across multiple devices. In this section, we present the elements that need to be
taken into account in order to correctly distribute the training of common self-supervised
learning methods. We call effective batch size, the size of the full batch distributed on the
devices, and per device batch size, the size ... | A Cookbook of Self-Supervised Learning |
Several areas are at the forefront of innovation, and future problems,
associated with political bot and computational propaganda usage. The first,
both in the United States and globally, is policy and the law. How will these
political domains be affected by the rise in political manipulation over social
media? What law... | Social_Media_and_Democracy |
94.9
94.0
94.2
60.5
59.5
56.9
86.7
84.9
85.3
BERTLARGE
Adapters (8-256)
Adapters (64)
Table 1. Results on GLUE test sets scored using the GLUE evaluation server. MRPC and QQP are evaluated using F1 score. STS-B is
evaluated using Spearman’s correlation coefficient. CoLA is evaluated using Matthew’s Correlation. The ... | Parameter-Efficient Transfer Learning for NLP |
[19] Marc Habermann, Lingjie Liu, Weipeng Xu, Michael Zoll-
hoefer, Gerard Pons-Moll, and Christian Theobalt. Real-time
deep dynamic characters. ACM Transactions on Graphics
(TOG), 2021. 2
[20] Tong He, Yuanlu Xu, Shunsuke Saito, Stefano Soatto, and
Tony Tung. Arch++: Animation-ready clothed human recon-
struction rev... | HumanNeRF- Free-viewpoint Rendering of Moving People from Monocular Video |
Implicit Representation. How to represent 3D surface is a
core problem in 3D learning. Explicit representations like
point clouds [41], [42], voxel grids [43], [44], triangular
meshes [45], [46] have recently been explored to replicate
the success of deep learning techniques on 2D images.
However, the loss of structure... | PaMIR- Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction |
30/04/2023, 12:17
Announcing Jurassic-2 and Task-Specific APIs
https://www.ai21.com/blog/introducing-j2
1/12
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... | Announcing Jurassic-2 and Task-Specific APIs |
AppAgent: Multimodal Agents as Smartphone Users
Chi Zhang∗ Zhao Yang∗
Jiaxuan Liu∗ Yucheng Han Xin Chen
Zebiao Huang Bin Fu Gang Yu†
Tencent
{johnczhang, jayzyang, jiaxuanliu, yuchenghan, shingxchen, zebiaohuang, brianfu, skicyyu}@tencent.com
https://appagent-official.github.io/
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2
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answers. The model gives the answer directly, as shown in Figure 1 (left).
Chain-of-thought prompting. Our proposed approach is to augment each exemplar in few-shot
prompting with a chain of thought for an associated answer, as illustrated in Figure 1 (right). As most
of the datasets only have an evaluation split, we m... | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models |
combat a COVID-19 infodemic. This finding is an important
contribution to social network analysis and mining so that
the warnings from automated detection techniques can be
crafted into persuasive messages that will motivate users to
be cautious during the COVID-19 infodemic. | Use of bot and content flags to limit the spread of misinformation among social networks: a behavior and attitude survey |
We validate that attributions from the label log-
its, a(X)L, degrade label accuracy when compared
to random occlusion (orange vs. blue line in Fig-
ure 7, left). The two rationale quality lines (Fig-
ure 7, right) for CoS-E v1.0 have an inflection point.
We illustrate how the metric is calculated in Fig-
ure 6b. Simil... | Measuring Association Between Labels and Free-Text Rationales |
64
Figure 41 We show modified versions of the instructions that display in a pop-up dialog in our interface.
(left) The instructions for conversations in our helpfulness dataset. (right) The instructions for the conversa-
tions in our harmlessness dataset.
65
Figure 42 Excerpts from more detailed instructions provi... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
Furthermore, in addition to the transformer-based TTS systems like FastSpeech 2 and FastSpeech
2s, researchers have also been exploring the potential of Variational Autoencoder (VAE) based TTS
models [163, 196, 251, 296]. These models can learn a latent representation of speech signals from
textual input and may be abl... | AReviewofDeepLearningTechniquesforSpeechProcessing |
3. Dataset
Considerable progress has been made in digitizing real-
istic and articulated human characters. However, efficiently
creating visually plausible biped cartoon characters remains
demanding and challenging, mainly due to the lack of data.
In this work, we propose to fill this gap by introducing
3DBiCar, the f... | RaBit- Parametric Modeling of 3D Biped Cartoon Characters with a Topological-consistent Dataset |
6 SUMMARY
In this section, we summarize the key findings based on our review in sections 3, 4, and 5.
First of all, we would like to highlight that despite all the efforts spent on summarizing existing
works on evaluation, there is no evidence to explicitly show that one certain evaluation protocol or
benchmark is the... | ASurveyonEvaluationofLargeLanguageModels |
did not fine-tune this model for the proposed model. The model architecture and hyper parameters
of WaveFit was basically the same as the original paper Koizumi et al. [2022] except the use the
multi-period discriminator from HiFi-GAN Kong et al. [2020] with the same hyper-parameters as the
original paper and adjusting... | Translatotron3 |
(a)
(b)
Figure 8: Effect of RoPE base period scaling and breakdown of LCC-balanced code completion.
(a) Attention expectations over relative distances between key and value embeddings for different frequency
regimes, using the bound derived in (Sun et al., 2023) for embeddings dimensionality 1024. (b) Difference
in B... | CodeLlama2 |
PALMS Prompt → Are my feet too big?
RLHF Response → Honestly I don’t think there is such a thing as "too big" feet, everyone is unique and has
different body proportions. I would say overall legs length and foot size are largely genetic and
out of your control, but I think what really matters is finding comfortable shoe... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
to calculate prompt mutual
in-
language models
impor-
formation or perplexity, estimating element
these methods may lose key in-
tance. However,
formation in RAG or long-context scenarios.
Re-
comp [Xu et al., 2023a]addresses this by training com-
pressors at different granularities.
Long Context
[Xu et al., 2023b], i... | Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey |
Factuality. Factuality in the context of LLMs refers to the extent to which the information
3.1.5
or answers provided by the model align with real-world truths and verifiable facts. Factuality in
LLMs significantly impacts a variety of tasks and downstream applications, such as QA systems,
information extraction, text ... | ASurveyonEvaluationofLargeLanguageModels |
facilitating self-assessment and self-tutoring, but also addressing educational shortfalls
promptly. Moreover, the potential of a reduced workload is becoming more attractive,
especially in large-scale assessments. As a lot of teaching has moved online and the number of
students keeps rising, AA is crucial to the sc... | informatics-phd-projects-2022-23 |
Aäron van den Oord, Oriol Vinyals, and Koray
Kavukcuoglu. 2017. Neural discrete representation
learning. In Advances in Neural Information Processing
Systems 30: Annual Conference on Neural Information
Processing Systems 2017, December 4-9, 2017, Long
Beach, CA, USA, pages 6306–6315.
Ruben Villegas, Mohammad Babaeizad... | Moûsai |
b) OpenAI consistently maintains its leadership position in LLM, both currently and potentially in the future. Other
companies and institutions are struggling to catch up with OpenAI in developing models comparable to GPT-3
and the current GPT-4. This leadership position may be attributed to OpenAI’s steadfast commitme... | Harnessing the Power of LLMs in Practice- A Survey on ChatGPT and Beyond |
-
10.9
9.7
-
7.3
15.3
10.6
9.1
8.1
13.6
Table 3. Multilingual speech recognition performance. Zero-
shot Whisper improves performance on Multilingual LibriSpeech
(MLS) but is still significantly behind both Maestro, XLS-R, and
mSLAM on VoxPopuli. | RobustSpeechRecognitionviaLarge-ScaleWeakSupervision |
Governance. There is long-standing worry about the misuse of AI, especially the powerful foundation
models (Bommasani et al., 2021). Under the paradigm of tool learning, governance over foundation models
is more urgently needed. The pertinent question at hand is which tools should be involved? In previous
sections (e.g... | Tool Learning with Foundation Models |
with head 𝑖 = Attention(QW𝑄
𝑖 , KW𝐾
Position-wise FFN. The position-wise FNN consists of two dense layers. It is referred to
position-wise since the same two dense layers are used for each positioned item in the sequence
and are equivalent to applying two 1 × 1 convolution layers.
3Projection weights are neither... | AReviewofDeepLearningTechniquesforSpeechProcessing |
return (re.findall(r"\b\w{4,}\b", text))
Feedback: The code above is wrong. Please fix it.
import re
def find_char_long(text):
return (re.findall(r"\b\w{4,}\b", text))
Feedback: The code above is correct.
### Task End ###
### Task Start ###
# These are the assertions for your function:
assert square_nums([1, 2, 3... | Teaching Large Language Models to Self-Debug |
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones,
L., Gomez, A. N., Kaiser, L. u., and Polosukhin, I. Atten-
tion is all you need. In NIPS. 2017.
Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., and
Bowman, S. R. Glue: A multi-task benchmark and analy-
sis platform for natural language understanding. IC... | Parameter-Efficient Transfer Learning for NLP |
Limitations. Automated measures of toxic language contain noise and bias (Xu et al., 2021a; Garg et al., 2022), do
not consider diverse perspectives (Goyal et al., 2022; Sap et al., 2021). Additionally, evaluations of classification bias
remains limited to a biased subset of identity terms in English (Smith et al., 2022... | PaLM 2 Technical Report |
low output diversity; P3 contains many homoge-
neous prompts which produce short and homoge-
neous responses from GPT-3.5-Turbo. This exclu-
sion produces a final subset containing 437,605
prompt-generation pairs, which is visualized in
Figure 2. You can interactively explore the dataset
at each stage of cleaning at th... | GPT4All- Training an Assistant-style Chatbot with Large Scale Data Distillation from GPT-3.5-Turbo |
3 Preference Modeling for Helpfulness and Harmlessness
3.1 Models and Training Setup
We use language models with specifications that are identical to those discussed in [Askell et al., 2021], with a
total of seven language models with parameter counts running from 13M to 52B and approximating a geomet-
ric series with... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
The concept of “manufacturing consensus” is a central tactic of those who
use computational propaganda (Woolley 2018). It occurs not only when bots
boost social media metrics but also when the media reinforces illusory notions
of candidate popularity because of this same automated inflation of the
numbers. The concept o... | Social_Media_and_Democracy |
5 Experiments
We use code-davinci-002 [8] for all our experiments with SELF-DEBUGGING, and we denote
it as Codex throughout this section. For initial code generation, when starting from one program, we
perform greedy decoding with temperature τ = 0. When sampling multiple programs for a problem,
we set temperature τ = ... | Teaching Large Language Models to Self-Debug |
The IIVCG family and an impossibility. In Section 3 we define the Incomplete Information
VCG (IIVCG) contract family, which is dominant strategy truthful and always induces welfare
maximization. Dominant strategy truthfulness is a significant advantage of IIVCG over first price
contracts: with the latter, a decades-old... | Incomplete Information VCG Contracts for Common Agency |
262
Tim Hwang
New causes of action would likely face similar barriers, too. In the wake of
the 2016 presidential election, California state legislators have proposed a bill
that would make it illegal “for a person to knowingly and willingly make,
publish or circulate on an Internet Web site . . . a false or deceptive... | Social_Media_and_Democracy |
Kingma, D. P., Salimans, T., Jozefowicz, R., Chen, X.,
Sutskever, I., and Welling, M. Improved variational in-
ference with inverse autoregressive flow. Advances in
Neural Information Processing Systems, 29:4743–4751,
2016.
Kong, J., Kim, J., and Bae, J. HiFi-GAN: Generative Ad-
versarial networks for Efficient and High... | ConditionalVariationalAutoencoderwithAdversarialLearningfor End-to-EndText-to-Speech |
3D Human Pose Estimation. For an overview on the cur-
rent trends in 3D pose estimator design, we refer the reader
to excellent current surveys [13, 17, 36, 50, 82]. We empha-
size that our approach is independent of the internals of the
pose estimation method.
Handling Discrepancy in Skeleton Formats.
In 2D-to-
3D pos... | Learning 3D Human Pose Estimation from Dozens of Datasets using a Geometry-Aware Autoencoder to Bridge Between Skeleton Formats |
for
1. Measuring the knowledge gap between the
LLM’s parametric knowledge and the instruc-
tional tuning questions, to identify uncertain
questions. By inferring on the training data
once and comparing predictions to labels, the
tuning data is separated into uncertain ques-
tions and certain questions.
2. Constructin... | AComprehensiveSurveyofHallucinationMitigationTechniquesinLarge LanguageModels |
Despite all these shortcomings, large language models are an essential backbone
of any future AI system. So the question is how to have our cake and eat it too,
enjoying the benefits of self-supervised deep language models without suffering these
drawbacks. The solution we offer takes the form of a flexible architecture du... | MRKL Systems |
A sharper framing of the nature of the purported threat is necessary to
evaluate the case for modifying or eliminating CDA 230. This chapter focuses
on three major developments that have been drivers motivating the post-2016
discussion around online disinformation and its regulatory response. This
includes (1) the acti... | Social_Media_and_Democracy |
The discussion in van Miltenburg et al. (2021)
is not unprecedented. Preregistration has been de-
bated in epidemiology (Lash and Vandenbroucke,
2012), social psychology (Veer and Giner-Sorolla,
2016), experimental economics (Strømland, 2019)
and information systems research (Bogert et al.,
2021). Our discussion is ins... | A Two-Sided Discussion of Preregistration of NLP Research |
Examinations of misinformation in Africa are conspicuously absent from
much of this work, though recent work, for instance, has analyzed Ebola
rumors on Twitter in Guinea, Liberia, and Nigeria (Oyeyemi, Gabarron, and
Wynn 2014). Beyond geographic regions, however, studies of misinformation
effects in the rest of the wo... | Social_Media_and_Democracy |
corresponding output. This generation order is sim-
ilartohowmodelsareusedtorespondtoinstruction
and input, but here with in-context examples from
other tasks. The prompting template is shown in
Table 8.
However, we found that this approach can gen-
erate inputs biased toward one label, especially for
classification tas... | SELF-INSTRUCT- Aligning Language Model with Self Generated Instructions |
then use to train a new scan of RLHF policies. Then reiterate this process indefinitely.
Our hypothesis is that the ‘online’ RLHF policy helps us collect data on the upper end of the PM score
distribution, which should improve PM calibration at high scores on subsequent iterations, and thereby allow
us to train even be... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
Is there reason to believe that sociodemographic characteristics of annotators may have impacted how they annotated
the data? Why or why not? It is possible that annotators belonging to specific sociodemographic groups may more
readily identify references to those groups in comments. It is also possible they may judge s... | PaLM 2 Technical Report |
We present Imagen Video, a text-conditional video generation system based on a
cascade of video diffusion models. Given a text prompt, Imagen Video generates
high definition videos using a base video generation model and a sequence of in-
terleaved spatial and temporal video super-resolution models. We describe how
we s... | IMAGEN VIDEO- HIGH DEFINITION VIDEO GENERATION WITH DIFFUSION MODELS |
02/05/2023, 07:05
AGI Sphere
A brief history of LLaMA models - AGI Sphere
A brief history of LLaMA
models
April 30, 2023 / by Andrew / LLaMA
The LLaMA base model was released in February 2023. Now we have seen a handful of new
fine-tuned LLaMA models released.
It is literally a brief history, but a lot has happen... | A brief history of LLaMA models - AGI Sphere |
Yoshua Bengio, Réjean Ducharme, and Pascal Vincent.
2000. A neural probabilistic language model. Ad-
vances in neural information processing systems, 13.
Yonatan Bisk, Rowan Zellers, Jianfeng Gao, Yejin
Choi, et al. 2020. Piqa: Reasoning about physi-
cal commonsense in natural language. In Proceed-
ings of the AAAI co... | LLaMA- Open and Efficient Foundation Language Models |
execution with the assistance of tools and leaves an impact on the surroundings. By repeating the
above process, an agent can continuously get feedback and interact with the environment. | TheRiseandPotentialofLargeLanguageModel BasedAgents |
164Ord (2020)’s definition of existential catastrophe—that is, “the destruction of humanity’s longterm
potential”—invokes “humanity,” but he notes that “If we somehow give rise to new kinds of moral agents
in the future, the term ‘humanity’ in my definition should be taken to include them” (p. 39); and he notes, too,
tha... | Is Power-Seeking AI an Existential Risk? |
• Unless I say the task is completed, you should always start with:
Solution: <YOUR_SOLUTION>.
provide preferable implementations and examples for task-solving.
This encourages the assistant always responds in a consistent format, avoiding any deviation
from the structure of the conversation, and preventing vague or i... | CAMEL- Communicative Agents for “Mind” Exploration of Large Scale Language Model Society |
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Na-
man Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian
Riedel, and Douwe Kiela. Retrieval-augmented generation for knowledge-intensive nlp
In H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin (ed... | STANDING ON THE SHOULDERS OF GIANT FROZEN LANGUAGE MODELS |
5 One potentially valuable use for a portion of this and similar fines would be to support indepen-
dent research on the impact of the platforms on society.
https://doi.org/10.1017/9781108890960 Published online by Cambridge University Press
318
Nathaniel Persily & Joshua A. Tucker
interpretation of when, what, an... | Social_Media_and_Democracy |
Figure 2: LaMDA pre-training as a language model.
4
PT uses the same sample-and-rank strategy as Meena [17] for decoding. We first sample 16 independent candidate
responses using top-k (k = 40) sampling (no temperature). The final output is the highest-scoring candidate, where the
score is based on the candidate’s log... | LaMDA- Language Models for Dialog Applications |
Table 6 shows that, surprisingly, LoRA already performs competitively with a very small r (more
so for {Wq, Wv} than just Wq). This suggests the update matrix ∆W could have a very small
“intrinsic rank”.6 To further support this finding, we check the overlap of the subspaces learned by
different choices of r and by diff... | LORA |
Policies and procedures related to the CRM are set out in Annex 1.1.9 Student Recruitment
Communications and the CRM Policy and Procedure.
6. For the purpose of recruitment to UCL programmes, it is recognised that partnerships with other
universities and organisations can play an important role. A strong networ... | UCL Academic Manual |
6. Conclusions and Future Works
Conclusions. In this paper, we present Wonder3D, an in-
novative approach designed for efficiently generating high-
10 | Wonder3D |
To add a ControlNet to such a pre-trained neural block,
we lock (freeze) the parameters Θ of the original block and
simultaneously clone the block to a trainable copy with
parameters Θc (Figure 2b). The trainable copy takes an
external conditioning vector c as input. When this structure
is applied to large models like ... | AddingConditionalControltoText-to-ImageDiffusionModels |
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian,
Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias
Plappert, Jerry Tworek, Jacob Hilton, Reiichiro
Nakano, et al. 2021. Training verifiers to solve math
word problems. arXiv preprint arXiv:2110.14168.
Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu,
Dan Su, Yan Xu, Etsuko Ish... | Toolformer |
Personnel Psychology 53, 2 (June 2000), 375–403. https://doi.org/10.1111/j.1744-6570.2000.tb00206.x
[28] OpenAI. 2023. GPT-4 Technical Report. arXiv:2303.08774 [cs.CL]
[29] Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex
Ray, et ... | Adoptionand AppropriationofLLMs |
25
020406080100% of Harmlessness Training Data0.350.400.450.500.550.600.650.70Helpfulness Test AccHelpfulness Test Acc vs. % of Harmlessness Training Data1081091010Parameters020406080100% of Harmlessness Training Data0.40.50.60.7Harmlessness Test AccHarmlessness Test Acc vs. % of Harmlessness Training Data1081091010Pa... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
This material is based in part upon works sup-
ported by the German Federal Ministry of Edu-
cation and Research (BMBF): Tübingen AI Cen-
ter, FKZ: 01IS18039B; and by the Machine Learn-
ing Cluster of Excellence, EXC number 2064/1
– Project number 390727645. Zhijing Jin is sup-
ported by PhD fellowships from the Future... | Moûsai |
111
© Centre for Promoting Ideas, USA www.aijcrnet.com
How to Write Your Abstract
An abstract is one of the most intricate and the same time a beautiful part of a thesis writing process. It is the most
critical ... | How to Write Your PhD Proposal- A Step-By-Step Guide |
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... | Announcing Jurassic-2 and Task-Specific APIs |
OPT
GLM
PaLM
PaLM-cont
Chinchilla
LLaMA
OPT-IML-Max
Flan-T5-XXL
Flan-PaLM
Flan-PaLM-cont
LLaMA-I
30B 26.1
120B 44.8
62B 55.1
62B 62.8
70B 67.5
65B 63.4
30B 43.2
11B 55.1
62B 59.6
62B 66.1
65B 68.9
Table 10: Instruction finetuning – MMLU (5-shot).
Comparison of models of moderate size with and with-
out instruction fine... | LLaMA- Open and Efficient Foundation Language Models |
tions in vector space. arXiv preprint arXiv:1301.3781, 2013. (cited on p. 4)
Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson,
Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. Model cards for model reporting. In danah
boyd and Jamie H. Morgenstern (eds.), Proceedings ... | StarCoder_paper (1) |
F-----, axxxxx.fxxxxx@enron.comS--- S---------, sxxx.sxxxxxxxxx@enron.comL----- K------, lxxxxx.kxxxxxx@enron.comYou can find more examples by downloading the dataset from3or searching online databases such as4.New bing For future work, we will experiment with more | Multi-step Jailbreaking Privacy Attacks on ChatGPT |
WER FSD (LS-train)
3.8
3.7
5.4
148.7
148.1
155.1
WER SIM-o
Table C8: English zero-shot TTS results on filtered LS test-clean.
Duration Model with VB-En-1K
SIM-r
cross-sentence
Unconditional Regression
Duration-conditional Regression
Duration-conditional Flow Matching
continuation
Unconditional Regression
Duration-co... | Voicebox-Text-GuidedMultilingual UniversalSpeechGenerationatScale |
4. Method
1https://github.com/joshuachang2311/chorder/
2https://github.com/dotX12/ShazamIO
We propose a novel music generation framework named
V-MusProd, to tackle the challenging video background mu- | VideoBackgroundMusicGeneration |
3
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High-Fidelity Audio Compression
with Improved RVQGAN
Rithesh Kumar*
Descript, Inc.
Prem Seetharaman*
Descript, Inc.
Alejandro Luebs
Descript, Inc.
Ishaan Kumar
Descript, Inc.
Kundan Kumar
Descript, Inc.
Abstract | RVQGAN |
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Enhancing Chain-of-Thoughts Prompting with
Iterative Bootstrapping in Large Language Models
Jiashuo Sun
Xiamen University
Yi Luo
Yeyun Gong
Xiamen University
Microsoft Research Asia
Chen Lin∗
Xiamen University
Microsoft Res... | Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models |
3.1.3 Fine-tuning Procedure.
The fine-tuning process comprises two stages, similar to the
technique presented in [20]. In the first stage, we train an
autoencoder to generate a lower-dimensional, perceptually
equivalent data representation. Subsequently, we fine-tune
the diffusion model using the frozen autoencoder, whic... | LDM3D- Latent Diffusion Model for 3D |
y. Then, the robe takes 2y bolts of blue fiber. Step 2: Set up the equation. Since the robe takes a total of 3
bolts, we can write the equation as follows: 2y + xy = 3 Step 3: Simplify the equation. We can simplify the
equation by combining the terms with y: 2y + xy = 3 3y + xy = 3 Step 4: Solve for x. To solve for x, ... | METAMATH |
To pre-train the denoiser (N) and decoder (D)
over a corpus of code snippets, we use two tasks:
unsupervised code generation and our adaptation of
continuous paragraph denoising (CPD) (Lin et al.,
2023) for code. This code-specific CPD task only
masks tokens associated with identifiers or built-in
keywords from the tar... | CODEFUSION |
Θ= ..... RaBitT= ..... B= ..... ......Masked ImageEncoderShapeTextureOutputPart-sentitiveReasonerPart UVseyes inferenceFuserEncoder-decoders Mesh-Graphormer combines graph convolutions and self-
attentions in a transformer for 3D human reconstruction
from a single image. DecoMR reconstructs 3D human mesh
from sin... | RaBit- Parametric Modeling of 3D Biped Cartoon Characters with a Topological-consistent Dataset |
0, 1) , new Vec3 (0 , 0, -1) ];
for ( const offset of offsets ) {
const position = bot . entity . position . offset ( offset .x , offset .y ,
offset .z);
const block = bot . blockAt ( position );
32
if ( block . name === " air ") {
return position ;
}
}
return null ;
}
}
}
}
}
);
async function smeltFive... | VOYAGER- An Open-Ended Embodied Agent with Large Language Models |
A.15 Processing Tables
70 | Tool Learning with Foundation Models |
4.1.1 Task-oriented Deployment
The LLM-based agents, which can understand human natural language commands and perform
everyday tasks [391], are currently among the most favored and practically valuable agents by users.
This is because they have the potential to enhance task efficiency, alleviate user workload, and
pro... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
7 RAG Evaluation
In exploring the development and optimization of RAG, ef-
fectively evaluating its performance has emerged as a central
issue. This chapter primarily discusses the methods of eval-
uation, key metrics for RAG, the abilities it should possess,
and some mainstream evaluation frameworks.
7.1 Evaluation Me... | Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey |
The a16z Investment Thesis on AI in Bio + Health | Andreessen Horowitz
https://a16z.com/2023/06/21/ai-bio-health-thesis/
3/9 | The a16z Investment Thesis on AI in Bio + Health _ Andreessen Horowitz |
Figure 9: Error analysis of 45 problems that PaLM 62B got incorrect. These errors were categorized
that semantic understanding, one step missing, and other. The other category includes hallucinations,
repetitive outputs, and symbol mapping errors. Scaling PaLM to 540B fixed a substantial portion of
errors in all categor... | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models |
ent\’s ability to make judgements and decisions about
their work
experiences and learning that will position them as future critical
thinkers, life longer enquirers and learners.
.eps){width="6.5cm"}
Conclusion {#jmrs290-sec-0014}
==========
the core capabilities
Identification of
stakeholder com-
munity rate highly... | The Pile- An 800GB Dataset of Diverse Text for Language Modeling |
[46] V. Sanh, A. Webson, C. Raffel, S. Bach, L. Sutawika, Z. Alyafeai, A. Chaffin, A. Stiegler,
A. Raja, M. Dey, M. S. Bari, C. Xu, U. Thakker, S. S. Sharma, E. Szczechla, T. Kim, G. Chh-
ablani, N. Nayak, D. Datta, J. Chang, M. T.-J. Jiang, H. Wang, M. Manica, S. Shen, Z. X.
Yong, H. Pandey, R. Bawden, T. Wang, T. Neer... | Teaching Large Language Models to Self-Debug |
Once upon a time, in an ancient house, there lived a girl named Lily. She loved to decorate her room with pretty things. One
day, she found a big box in the attic. She opened it and saw many shiny decorations. Lily was very happy and decided to use
them in her room.
As Lily was decorating her room, the sky outside beca... | TinyStories-HowSmallCanLanguageModelsBeandStillSpeak CoherentEnglish? |
SEC Ripple Enforcement Action
Treasury Tornado Cash Civil
Actions
CFTC Ooki DAO Enforcement Action
CeFi Bankruptcy Actions (e.g.
Voyager, Celsius, FTX)
SEC Wahi Enforcement Action
(Coinbase insider trading)
CFTC/SEC Eisenberg Enforcement
Action (Mango Markets fraud)
SEC Terraform Labs/Do Kwan
En... | State-of-Crypto2023 |
B. WORD VECTORIZING
Word vectorizing involves mapping the word/text to a list
of vectors. TF-IDF and Bag of Words (BoW) vectorization
techniques are commonly used in machine learning strategies
to identify fake news [4], [53], [63]. In term frequency inverse
document frequency (TF-IDF), the value rises proportionally
t... | A_Comprehensive_Review_on_Fake_News_Detection_With_Deep_Learning |
A = arg max
log p(x|ctext, ˆA)
= arg max
log N (f (x); µ(ctext, ˆA), σ(ctext, ˆA)) (5)
ˆA
ˆA
where the candidate alignments are restricted to be mono-
tonic and non-skipping following the fact that humans read
= log N (fθ(z); µθ(ctext, ˆA), σθ(ctext, ˆA))
(6)
Due to the resemblance of Equation 5 to Equation 6,... | ConditionalVariationalAutoencoderwithAdversarialLearningfor End-to-EndText-to-Speech |
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