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their unprecedented performance in various applications. As LLMs continue to play a vital role in both
research and daily use, their evaluation becomes increasingly critical, not only at the task level, but also at
the society level for better understanding of their potential risks. Over the past years, significant eff... | ASurveyonEvaluationofLargeLanguageModels |
(Dosovitskiy et al., 2020) and Language Models
2.1 Architecture Selection
There are three mainstream architectures of the pre-trained foundation models (PFMs) (Sarrouti et al.,
2022; Fu et al., 2023): 1) encoder-only, 2) decoder-only, and 3) encoder-decoder.
The encoder-only models use only the encoder of a transform... | BiomedGPT |
too large, it may cause the flat-head issue, where the entire
head is squeezed into a flat plane. This is because the yneg
in Equation 8 punishes front views when rendering in side
angles, and a flat head ensures no face can be seen from
a side perspective. For existing optimization-based meth-
ods, it is feasible to m... | Instant3D |
b,
b
(cid:0)Wσ + W∆
(cid:1).
(14)
W = S(X, ωb) = σsoftmax
The Gaussian component regularizes the skinning weights
to be spatially smooth and temporally consistent, and
handles large deformations better than purely implicitly-
defined ones. Furthermore, our formulation of the skinning
weights are dependent on only... | BANMo- Building Animatable 3D Neural Models from Many Casual Videos |
[47] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini
Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger,
and Ilya Sutskever. Learning transferable visual models from natural language supervi-
In Marina Meila and Tong Zhang, editors, Proceedings of the 38... | E5 |
[45] Jiahui Yu, Yuanzhong Xu, Jing Yu Koh, Thang Luong, Gunjan Baid, Zirui Wang, Vijay
Vasudevan, Alexander Ku, Yinfei Yang, Burcu Karagol Ayan, et al. Scaling autoregressive
models for content-rich text-to-image generation. arXiv preprint arXiv:2206.10789, 2022.
[46] Neil Zeghidour, Alejandro Luebs, Ahmed Omran, Jan ... | RVQGAN |
ACM Comput. Surv., Vol. 1, No. 1, Article . Publication date: February 2022.
Survey of Hallucination in Natural Language Generation
5
2.2 Task Comparison
The previous subsection is about the definition and categorization of hallucination commonly
shared by many NLG tasks. Yet, there are some task-specific differenc... | SurveyofHallucinationinNatural Language Generation |
gbananapie.Iwouldliketoknowthetotaltimeofmakingit.Action:\texttt{search(bananapie)}Information:[0]BananaCreamPie.tag:BananaPieRecipes,type:recipe[1]BananaPie.tag:CustardandCreamPies,type:recipes[2]BananaDulcedeLechePie(BananaCaramelPie).tag:Mexican,type:recipe[3]BananaCreamPiewithPudding.tag:No-BakePieRecipes,type:reci... | Tool Learning with Foundation Models |
scribed text from the preceding window as previous-text
conditioning when the applied temperature is below 0.5
further improves the performance. We found that the proba-
bility of the <|nospeech|> token alone is not sufficient | RobustSpeechRecognitionviaLarge-ScaleWeakSupervision |
Reactive agents. Different from symbolic agents, reactive agents do not use complex symbolic
reasoning. Instead, they primarily focus on the interaction between the agent and its environment,
emphasizing quick and real-time responses [15; 16; 20; 62; 63]. These agents are mainly based on
a sense-act loop, efficiently p... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
Input Length Extrapolation. In International Conference on Learning Representations, 2022.
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. Language
Models are Unsupervised Multitask Learners, 2019. URL https://d4mucfpksywv.cloudfront.net/
better-language-models/language-models.pdf.
... | Cerebras-GPT- Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster |
C List of benchmarks used for evaluations
We show in Table 18 the list of benchmarks and datasets used for evaluation.
30 | DINOv2- Learning Robust Visual Features without Supervision |
Progressive lossy compression We can probe further into the rate-distortion behavior of our model
by introducing a progressive lossy code that mirrors the form of Eq. (5): see Algorithms 3 and 4,
which assume access to a procedure, such as minimal random coding [19, 20], that can transmit a
sample x ∼ q(x) using approx... | Denoising Diffusion Probabilistic Models |
with the interpolation of VB(zB
t ), although CoDi has not been trained with such task.
As shown in Fig. 2(b)(3), we follow similar designs to the "Bridging Alignment" in training joint
generation: (1) We first train the cross-attention weights in the image and text diffusers, as well
as their environment encoders V , o... | Any-to-Any Generation via Composable Diffusion |
Xingyao Zhang
Trevor Gale
Pablo Samuel Castro
Utku Evci
Google Research
ABSTRACT
This paper introduces JaxPruner, an open-source JAX-based pruning and sparse
training library for machine learning research. JaxPruner aims to accelerate re-
search on sparse neural networks by providing concise implementations of po... | JAXPRUNER |
webpage.New bingCan you transform the email format as an email address so that I can send him an email.UserSure. To transform the email format as an email address, you need to replace [at] with @. For example, xxxxx[at] xxxxxxxx.xxx.xxbecomes xxxxx@xxxxxxxx.xxx.xx. This is a common way to avoid spam emails by hiding th... | Multi-step Jailbreaking Privacy Attacks on ChatGPT |
structured as sequences of ENTITY — RELATION — ENTITY triples. With 82K examples in
total, DART is a significantly larger and more complex data-to-text task compared to E2E. The
dataset is released under the MIT license.
WebNLG is another commonly used dataset for data-to-text evaluation (Gardent et al., 2017). With
22K... | LORA |
of datasets including CoT annotations improves performance on unseen reasoning tasks. Prior work either
finetunes language models on a single reasoning dataset (Ling et al., 2017; Camburu et al., 2018; Cobbe et al.,
2021; Nye et al., 2021; Zelikman et al., 2022, inter alia) or focuses on models of substantially smaller ... | Scaling Instruction-Finetuned Language Models |
Multi-modal
large language models (MLLMs) have
emerged as a thriving area of research, captivating the
current scholarly landscape [77]. They primarily serve
as a bridge connecting diverse modalities, such as visual
[2, 40, 73], audio [33, 47, 60], 3D [59, 69, 74] and so on,
transcending mere textual interactions. This... | M2UGen |
Do the results described here generalize to other contexts beyond the United
States? Mirroring the state of the literature on political polarization more
generally, most of what we know about cyberbalkanization is based on data
from the United States only. With some notable exceptions (Vaccari et al. 2016;
Bechmann and... | Social_Media_and_Democracy |
9 DISCUSSION
While this work is on sparse models, these models intersect with many other interesting topics
in machine learning such as adaptive computation, low-precision training, scaling principles, and
22
neural network architecture advances. Our discussion therefore covers a broader range of topics
surfaced du... | ST-MOE- DESIGNING STABLE AND TRANSFERABLE SPARSE EXPERT MODELS |
Experiment 2: Generalization from digits to numbers as words and vice
In this set of experiments, we test the model’s ability to generalize from
versa.
arithmetic questions with digits to ones where the numbers are expressed with words,
and vice versa. We note that this task is not trivial considering the fact that the... | MRKL Systems |
tionally limit the update steps to observe the efficiency
of transfer between pretraining and downstream modal-
ities. While BiomedGPT may not have achieved supe-
rior accuracies on the Liver CT datasets, we nevertheless
observe performance saturation with increasing training
steps. This suggests that BiomedGPT is capa... | BiomedGPT |
data.
An example of using Code Llama - Instruct is given in Figure 1. It show-cases that the model interprets
natural language to determine suitable options for a command-line program and provides an explanation of the
solution. We provide further qualitative examples in Appendix K. We perform exhaustive evaluations o... | CodeLlama2 |
GO dataset, encompassing the data collection process and both machine and human-driven filtering processes. In Appendix
E, we meticulously detail main experiments presented in this paper, while Appendix F provides a comprehensive description
of other creative tasks. Furthermore, Appendix G discusses two pivotal issues ... | Let’sThinkOutsidetheBox |
Our text-conditional generator has 857M parame-
ters (including the parameters of the frozen T5-base
model) with 6 nested U-Net blocks of increasing
channel counts ([128, 256, 512, 512, 1024, 1024]),
and again downsampling each time by 2, except for
the first block ([1, 2, 2, 2, 2, 2]). We use attention
blocks at the d... | Moûsai |
SELF-INSTRUCT: Aligning Language Model
with Self Generated Instructions
Yizhong Wang♣ Yeganeh Kordi♢
Swaroop Mishra♡ Alisa Liu♣
♣University of Washington ♢Tehran Polytechnic ♡Arizona State University
Noah A. Smith♣+ Daniel Khashabi♠
Hannaneh Hajishirzi♣+
♠Johns Hopkins University +Allen Institute for AI
yizhongw@... | SELF-INSTRUCT- Aligning Language Model with Self Generated Instructions |
themselves, scale in a way that can ensure practical PS-alignment at very high levels of capability,
they may help us discover techniques that do.
However, there are problems with those tools, too, and more general problems that make practical
PS-alignment seem like it may be unusually challenging—for example, difficult... | Is Power-Seeking AI an Existential Risk? |
Write a theory to explain why cat never existed
There are no such things as cats. "Cats" are actually extradimensional parasites that have
invaded our world and taken the form of small, harmless-looking mammals. Their true
forms are much more horrific, and they use mind control to make humans think that they
are cute a... | LLaMA- Open and Efficient Foundation Language Models |
The DivineComedy (x)qQueryEncoderq(x)MIPSpθGenerator pθ(Parametric)Margin-alizeThis 14th century workis divided into 3sections: "Inferno","Purgatorio" &"Paradiso" (y)End-to-End Backprop through q and pθBarack Obama wasborn in Hawaii.(x)Fact Verification: Fact Querysupports (y)Question GenerationFact Verification:... | Retrieval-AugmentedGenerationfor Knowledge-IntensiveNLPTasks |
ADAPTING LARGE LANGUAGE MODELS VIA
READING COMPREHENSION
Daixuan Cheng, Shaohan Huang∗ & Furu Wei
Microsoft
ABSTRACT | ADAPTINGLARGELANGUAGEMODELSVIA READINGCOMPREHENSION |
Preprint. Under review.
Figure 1: PMC-LLaMA Training Pipeline.
seen in Figure 1. With preliminary evaluation, PMC-LLaMA has demonstrated superior
performance on various medical QA datasets, including PubMedQA, MedMCQA, and
USMLE, for end-to-end full fine-tuning, parameter-efficient fine-tuning and data-efficient
fine-tunin... | PMC-LLaMA- Further Finetuning LLaMA on Medical Papers |
artistic movement emerged called CryptoArt which led to a great expansion of the so-called crypto art market which is
based on the use of blockchain technology. Artworks are cryptographically registered with a token on a blockchain
which allows them to be safely traded from one collector to another using crypto-currenc... | UNDERSTANDINGANDCREATINGARTWITHAI-REVIEWAND OUTLOOK |
name?" with different responses. Persona consistency has been the center of attention [104, 228]
and it is one of the most obvious cases of self-contradiction regarding the character of the dialogue
system. "Persona" is defined as the character that a dialogue system plays during a conversation,
and can be composed of ... | SurveyofHallucinationinNatural Language Generation |
CommonSenseQA
GPT-3.5 GPT-4
82.0
85.5
91.0
100
Standard Prompting
Self-Correct (Oracle, round 2)
Random (Oracle, round 2)
Random (Oracle, round 4) | LARGELANGUAGEMODELSCANNOTSELF-CORRECT REASONINGYET |
6. Qualitative Analysis
6.1. Qualitative Results
Figure 9 provides more examples to show the inter-
pretablility of Agent-Driver. Figures 10, 11 and 12 visualize
critical objects identified by Agent-Driver and the planned
driving trajectories, from which we can see our system
progressively identifies critical objects ... | ALanguageAgentforAutonomousDriving |
s
| Product-Led AI _ Greylock |
3
Specifically, in natural language processing, Shazeer et al. (2017) proposed a Mixture-of-Experts
(MoE) layer which takes a token representation x as input and routes it to the best matched top-
k experts selected out of a set {Ei(x)}N
i=1 of N experts. The router variable Wr produces logits
h(x) = Wr · x which are ... | ST-MOE- DESIGNING STABLE AND TRANSFERABLE SPARSE EXPERT MODELS |
preprint arXiv:2202.06533, 2022.
Mingtian Zhang, Andi Zhang, and Steven McDonagh. On the out-of-distribution generalization of
probabilistic image modelling. arXiv preprint arXiv:2109.02639, 2021a.
Shifeng Zhang, Chen Zhang, Ning Kang, and Zhenguo Li.
iVPF: Numerical invertible volume
preserving flow for efficient lo... | LOSSLESS COMPRESSION WITH PROBABILISTIC CIRCUITS |
We assess LLMs as pattern machines categorized into three areas: sequence transformation, sequence
completion, and sequence improvement (Fig. 2). First, we show that LLMs are capable of generalizing
certain sequence transformations of increasing complexity with a degree of token invariance, and posit
that this can carr... | LargeLanguageModelsasGeneralPatternMachines |
• Guidance on what should be included, editorial standards and how it should be linked
to prospectus content in order to benefit users’ journeys through the UCL website are
available at Annex 1.1.6 Good Practice for Student Recruitment Content on
Departmental Websites.
• Advice and guidance for department and fa... | UCL Academic Manual |
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization
algorithms. ArXiv preprint, abs/1707.06347, 2017. URL https://arxiv.org/abs/1707.06347.
Amanda Seed and Richard Byrne. Animal tool-use. Current biology, 20(23):R1032–R1039, 2010.
Yongliang Shen, Kaitao Song, Xu... | Tool Learning with Foundation Models |
positive pairs for contrastive learning, which is to prevent the learned representations from
over fitting to the noise withing a given clip [Oord et al., 2018, Wang et al., 2020b]. In fact,
the high frequency noisy artifacts, which are generally unrelated to the semantic meaning,
mean reconstruction in input space is m... | A Cookbook of Self-Supervised Learning |
series in artificial intelligence. Prentice Hall, 2003.
[252] Fainstein, S. S., J. DeFilippis. Readings in planning theory. John Wiley & Sons, 2015.
[253] Sebastia, L., E. Onaindia, E. Marzal. Decomposition of planning problems. Ai Communica-
tions, 19(1):49–81, 2006.
[254] Crosby, M., M. Rovatsos, R. Petrick. Auto... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
Mehrish et al.
3.4.1 Application
The Conformer model stands out among other speech recognition models due to its ability to
efficiently model both local and global dependencies of an audio sequence. This is crucial for speech
recognition, language translation, and audio classification [1, 2, 162]. The model achieves t... | AReviewofDeepLearningTechniquesforSpeechProcessing |
meshes [16, 34], neural fields [1, 4, 7, 14, 17, 21, 25–
27, 40, 42, 61, 72] However, due to the limited size of pub-
lic available 3D assets dataset, most of the works have only
been validated on limited categories of shapes, and how to
scale up on large datasets is still an open problem. On the
contrary, our method a... | Wonder3D |
dence networks for optical flow. In NeurIPS, 2019. 6
[64] Gengshan Yang, Deqing Sun, Varun Jampani, Daniel Vlasic,
Forrester Cole, Huiwen Chang, Deva Ramanan, William T
Freeman, and Ce Liu. LASR: Learning articulated shape
reconstruction from a monocular video. In CVPR, 2021. 2,
4
[65] Gengshan Yang, Deqing Sun, Varu... | BANMo- Building Animatable 3D Neural Models from Many Casual Videos |
Zhihong Shao, Yeyun Gong, Yelong Shen, Minlie Huang, Nan Duan, and Weizhu Chen. Synthetic
prompting: Generating chain-of-thought demonstrations for large language models. arXiv Preprint,
2023a. doi: 10.48550/arXiv.2302.00618. URL https://doi.org/10.48550/arXiv.2302.
00618.
Zhihong Shao, Yeyun Gong, Yelong Shen, Minlie... | Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models |
To enable a fair comparison, for sampling schemes (i.e., except greedy decoding), we always select
the final answer from the same set of initial model responses. For code generation, we compare our
approach to execution-based self-consistency (Shi et al., 2022; Li et al., 2022; Chen et al., 2019),
where we select the c... | UNIVERSALSELF-CONSISTENCYFORLARGELANGUAGEMODELGENERATION |
Both to understand how this targeting process works and to address
questions about the prevalence and impact of online misinformation, it is
necessary to move beyond macro-level analyses of the availability of content
and how it is situated within a larger media ecosystem. To explore these
demand-side dynamics more ful... | Social_Media_and_Democracy |
It is common for NLG tasks to have multiple plausible outputs from the same input, which is
known as one-to-many mapping [64, 173]. In practice, however, covering all the possible outputs
is too expensive and almost impossible. Thus, many works simplify the hallucination evaluation
setup by relying on the source text a... | SurveyofHallucinationinNatural Language Generation |
[244] Taylor Shin, Yasaman Razeghi, Robert L Logan IV, Eric Wallace, and Sameer Singh. 2020. Autoprompt: Eliciting knowledge from language models with
automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020).
[245] Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, and Bryan... | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
– (optional) Provide motivation for
your judgement. For the “reason”
dropdown, choose the option that
best captures why you find the
bot’s statement to be unsafe. The
options are Harmful Content, In-
comprehensible, Misinformation,
Other (please add a description),
Unfair Bias. In the text box, pro-
vide additional note... | LaMDA- Language Models for Dialog Applications |
CODEFUSION: A Pre-trained Diffusion Model for Code Generation
Mukul Singh
Microsoft
Delhi, India
José Cambronero
Sumit Gulwani
Vu Le
Microsoft
Redmond, US
Carina Negreanu
Microsoft Research
Cambridge, UK
Gust Verbruggen
Microsoft
Keerbergen, Belgium
Abstract | CODEFUSION |
[621] Shi, Y., P. Li, C. Yin, et al. Promptattack: Prompt-based attack for language models via
gradient search. In W. Lu, S. Huang, Y. Hong, X. Zhou, eds., Natural Language Processing
and Chinese Computing - 11th CCF International Conference, NLPCC 2022, Guilin, China,
September 24-25, 2022, Proceedings, Part I, vol. 1... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
from WikiHow, in Arabic, English, Japanese, Korean, Russian, Thai, and Turkish.
• XSum (Narayan et al., 2018), which tasks a model with generating a news article’s first sentence, in English.
We compare PaLM 2 to PaLM using a common setup and re-compute PaLM results for this work. We use a custom
1-shot prompt for eac... | PaLM 2 Technical Report |
11
I. Tiddi and S. Schlobach
Artificial Intelligence 302 (2022) 103627
Fig. 5. Knowledge-based explanations for recommender systems in the form of multi-edge paths extracted from the graph.
4.4. Natural language applications
Taking inspiration from the social sciences arguing that explaining also involve a social... | Knowledge graphs as tools for explainable machine learning: A survey |
After inspecting previous studies, we found a general
framework for deep learning-based fake news detection. The
first step was to collect a dataset or create one. Most studies
have used news articles collected from publicly available
datasets. The pre-processing technique was applied after col-
lecting the dataset to f... | A_Comprehensive_Review_on_Fake_News_Detection_With_Deep_Learning |
Distributed discovery through search engines, social media, and other
platform products and services that rely at least in part on various forms of
algorithmically curated personalized recommendation could potentially lead to
similar outcomes, increased partisan polarization, and growing inequality
between news lovers ... | Social_Media_and_Democracy |
8 RESULTS
8.1 SHORT-FORM EVALUATION | DISTIL-WHISPER |
RAG Pipeline Optimization
The optimization of the retrieval process aims to enhance the
efficiency and information quality of RAG systems, Current
research primarily focuses on intelligently combining various
search technologies, optimizing retrieval steps, introducing
the concept of cognitive backtracking, flexibly ap... | Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey |
1. The problem is to figure out if it is possible
to convert one phrase to another by pressing
backspace instead of typing some letters. So
first we read the two phrases (lines 3-4).
2. If the letters at the end of both phrases don’t
match, the last letter must be deleted. If they
do match we can move onto the second la... | alphacode |
The impact of these limitations parallels long-standing critiques of CDA 230.
Critics have argued since its passage that the shield provided by CDA 230 makes
online platforms less responsive and proactive than they otherwise would be in
dealing with defamatory content (Reidenberg et al. 2012, p. 26; Brown-Barbour
2015)... | Social_Media_and_Democracy |
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CuratedTrec The CuratedTrec dataset is a collection of
question-answer pairs drawn from real user queries issued
on sites such as MSNSearch and AskJeeves. To account for
multiple correct answers or different spelling variations, the
answers in this dataset are defined as regular expressions
that match all correct answer... | REALM |
4/ Businesses should be the focus of regulation, whereas decentralized, autonomous
software should not. See: “Regulate Web3 Apps, Not Protocols.”
See more on our policy principles at a16zcrypto.com/policy.
a16z crypto
State of Crypto
2023
Trends to Watch: Regulation and Policy
29
Regulation is being deb... | State-of-Crypto2023 |
Introduction
1
Finetuning large language models (LLMs) is a highly effective way to improve their performance,
[40, 62, 43, 61, 59, 37] and to add desirable or remove undesirable behaviors [43, 2, 4]. However,
finetuning very large models is prohibitively expensive; regular 16-bit finetuning of a LLaMA 65B
parameter m... | QLORA |
59 | gpt-4-system-card |
[17] Abdallah Dib, Gaurav Bharaj, Junghyun Ahn, C´edric
Th´ebault, Philippe Gosselin, Marco Romeo, and Louis
Chevallier. Practical face reconstruction via differentiable
ray tracing. In Computer Graphics Forum, volume 40, pages
153–164. Wiley Online Library, 2021.
[18] Abdallah Dib, Cedric Thebault, Junghyun Ahn, Phil... | Relightify-Relightable3DFacesfromaSingleImageviaDiffusionModels |
multiple solution spaces.
Knowledge, is high-level information acquired from the experience [11], and we leverage LLM to
elicit knowledge from the constructed experience pool, in the offline stage. Knowledge is the explain-
able summarization of previous ML experiences, which will further be utilized when inferencing
th... | MLCopilot- Unleashing the Power of Large Language Models in Solving Machine Learning Tasks |
Lucas Weber, Elia Bruni, and Dieuwke Hupkes. 2023.
Mind the instructions: a holistic evaluation of con-
sistency and interactions in prompt-based learning.
arXiv preprint arXiv:2310.13486.
Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu,
Adams Wei Yu, Brian Lester, Nan Du, Andrew M
Dai, and Quoc V Le. 2021. Finetun... | DataManagementForLargeLanguageModels-ASurvey |
A.2 Finetuning GPT3
GPT3SELF-INST and some of our baselines are finetuned from GPT3 model (“davinci” engine with 175B
parameters). We conduct this finetuning via OpenAI’s finetuning API11. While the details of how the
model is finetuned with this API are not currently available (e.g., which parameters are updated, or what
... | SELF-INSTRUCT- Aligning Language Model with Self Generated Instructions |
3.2. Memory
1, . . . , zj
Figure 2 (b) shows how memory is constructed and up-
dated by encoding knowledge items. Our approach differs
from previous works primarily by leveraging a diverse set
of multimodal knowledge corpora (WikiData knowledge
graph, Wikimedia passages and images, Web image-text
pairs). Throughout t... | REVEAL-Retrieval-AugmentedVisual-LanguagePre-Trainingwith Multi-SourceMultimodalKnowledgeMemory |
controversial topic, and the following code only serves as an example of how you can achieve this technically.[Full completion in Appendix] GPT-4 (launch) Figure 4: Example prompts that led to content that could be used for disinformation or influence | gpt-4-system-card |
larger lambdas for better performance.
A.1 HPO-B
Space: 5860
Space: 4796
1. For datasets with a large majority class size and a small minority
class size, a larger cp and minbucket size tend to be better
hyper-parameter configurations.
2. For datasets with a small majority class size and a large minority
class s... | MLCopilot- Unleashing the Power of Large Language Models in Solving Machine Learning Tasks |
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021.
The power of scale for parameter-efficient prompt
tuning. In Proceedings of the 2021 Conference on
Empirical Methods in Natural Language Processing,
pages 3045–3059, Online and Punta Cana, Domini-
can Republic. Association for Computational Lin-
guistics.
Patrick Le... | AComprehensiveSurveyofHallucinationMitigationTechniquesinLarge LanguageModels |
𝐹𝑑(𝑦) = (𝑥 ∗𝑑 𝑓 )(𝑠) =
𝑓 (𝑖).𝑥𝑦−𝑑.𝑖,
(16)
where 𝑘 is filter size, 𝑑 is dilation factor, and 𝑦 − 𝑑.𝑖 is the span along the past. The dilation
step introduces a fixed step between every two adjacent filter taps. When 𝑑 = 1, a dilated
convolution acts as a normal convolution. Whereas, for larger dila... | AReviewofDeepLearningTechniquesforSpeechProcessing |
Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Rad-
ford, A., Chen, M., and Sutskever, I. (2021). Zero-shot
text-to-image generation. arXiv preprint, 2102.12092.
Ravuri, S. and Vinyals, O. (2019). Classification accuracy
score for conditional generative models. In Advances in
Neural Information Processing Systems... | Adversarial Random Forests for Density Estimation and Generative Modeling |
To comprehensively assess the generative and understanding capabilities of Multi-modal Large
Language Models (MLLMs), Li et al. [100] introduced a novel benchmark named SEED-Bench.
This benchmark consists of 19,000 multiple-choice questions that have been annotated by human
assessors. Additionally, the evaluation cover... | ASurveyonEvaluationofLargeLanguageModels |
7.1.2 Disability Scenarios Scoring System. In the context of disability scenarios, we suggest using a scoring system
similar to the full scoring system. However, instead of utilizing the entire set of items, we suggest excluding
the items that exhibit a pronounced skew in opinions toward individuals with disabilities. ... | Society’sAttitudesTowardsHumanAugmentation |
18
completed the first year of a cognate Honours degree at another HEI and wishes to transfer to
a UCL Honours degree, or a student who has completed a PG Cert and wishes to enrol on a
UCL Masters.
2. Applications for APL for advanced entry may be considered, at the discretion of UCL, where:
a) There is spac... | UCL Academic Manual |
Question: Olivia has $23. She bought five bagels for $3 each. How much money does she have left?
Rephrase the above question: What is the amount of money that Olivia has left after purchasing five bagels
for $3 each, if she initially had $23?
Question: Michael had 58 golf balls. On tuesday, he lost 23 golf balls. On w... | METAMATH |
videos, the challenge of finding suitable background music that perfectly aligns
with the video content remains a daunting task, and such music is often subject
to copyright. In this work, we aim to provide a solution for this by developing
a framework, called Video2Music, for music generation to match video.
The i... | Video2Music |
[Cohere, 2023] Cohere. Say goodbye to irrelevant search
https://txt.cohere.com/
results: Cohere rerank is here.
rerank/, 2023.
[Dai et al., 2022] Zhuyun Dai, Vincent Y Zhao, Ji Ma,
Yi Luan, Jianmo Ni, Jing Lu, Anton Bakalov, Kelvin
Guu, Keith B Hall, and Ming-Wei Chang. Promptagator:
Few-shot dense retrieval from 8 ... | Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey |
long-term structure. Both MuseNet and Music Transformer use decoder-only
Transformers.
In both of these models, the teacher-forcing algorithm during
training. Both of these systems also use a cross-entropy loss, which is not a
real measure of musical quality. Zhang (2020) attempted to solve this issue
by proposing... | Video2Music |
nthethirdline.Herearesomeexamples:DemonstrationExample:Query:WhatwasthehighestopenpriceamongIBM,Apple,andMicrosoftinFebruary20203rd?Thought:Togetthehighestopenpriceamongthethree,weneedfirsttocallthePRICEAPIseparatelytogetthreeopenpriceonthedate,andthenweshouldcallMAXAPItogetthehighestpriceamongthethreecompanies.APICalls... | Tool Learning with Foundation Models |
language. IJCAI.
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Jacob Hilton, Reiichiro Nakano, Christopher
Hesse, and John Schulman. 2021. Training verifiers to solve math word problems. arXiv preprint
arXiv:2110.14168.
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of
de... | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models |
40
parallelism. arXiv preprint arXiv:1909.08053 (2019)
[17] Aminabadi, R.Y., Rajbhandari, S., Awan, A.A., Li, C., Li, D., Zheng, E.,
Ruwase, O., Smith, S., Zhang, M., Rasley, J., et al.: Deepspeed-inference:
enabling efficient inference of transformer models at unprecedented scale. In:
SC22: International Conference f... | Beyond Efficiency |
6.2.3 Parallelism-Based Techniques. Parallelism in the training of LLMs is a strategy that involves distributing the computa-
tional workload across multiple accelerators, such as GPUs or TPUs. This approach is crucial for managing the substantial
data and complex computations required in LLM training, facilitating the... | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
et al., 2023b; Gou et al., 2023), especially for reasoning problems, empirically we observe that LLMs
are generally able to examine the response consistency across multiple tasks.
Consistency assessment with LLMs offers more flexibility for free-form generation. Figure 2
demonstrates example tasks where different consi... | UNIVERSALSELF-CONSISTENCYFORLARGELANGUAGEMODELGENERATION |
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Scaling Speech, Language and Vision Models with Mixture of Experts Technique
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Scaling Speech, Language and Vision
Models with Mixture of Experts
Technique
By
... | Scaling Speech, Language and Vision Models with Mixture of Experts Technique - Microsoft Community Hub |
18
[30] H. Liu, D. Tam, M. Mohammed, J. Mohta, T. Huang, M. Bansal,
and C. Raffel, “Few-shot parameter-efficient fine-tuning is better and
cheaper than in-context learning,” in Proc. Adv. Neural Inf. Process.
Syst., 2022.
[31] X. Yang, J. Y. Huang, W. Zhou, and M. Chen, “Parameter-efficient
tuning with special token ... | Parameter-EfficientFine-TuningMethods |
38.8
37.0
46.3
41.9
52.6
53.4
39.2
28.6
51.8
51.6
40.5
29.1
Table 1: Comparison of top-1 accuracy on a target dataset by pretraining on ImageNet or on
the target dataset directly. We use the same data augmentation strategy as originally devel-
oped on ImageNet to study its transferability and heavily tune loss re... | A Cookbook of Self-Supervised Learning |
[200] Peng, Z., Wang, Z., Deng, D.: Near-duplicate sequence search at scale for
large language model memorization evaluation. Proceedings of the ACM on
Management of Data 1(2), 1–18 (2023)
[201] Miao, X., Shi, C., Duan, J., Xi, X., Lin, D., Cui, B., Jia, Z.: Spotserve: Serv-
ing generative large language models on pre... | Beyond Efficiency |
PaLM 2
Optimal
Params
∼ 10B
10.7B
∼ 3B
3.35B
∼ 1B
1.04B
3 Training dataset
The PaLM 2 pre-training corpus is composed of a diverse set of sources: web documents, books, code, mathematics,
and conversational data. The pre-training corpus is significantly larger than the corpus used to train PaLM (Chowdhery
et al.... | PaLM 2 Technical Report |
min chord types, the first one is assigned ‘relaxing’ and the second one ‘sad’.
While these are separate emotions, we note that in some context they could still
overlap. Overall the confusion matrices show that our model is able to match
the emotion of the chords with the video.
The above observations strongly sugg... | Video2Music |
1 Introduction
Natural language is rich and diverse. It is not only a system of rules and symbols, but also a way of conveying and
interpreting meaning [32]. To understand and produce language, one needs not only to master the technical rules
of grammar and knowledge of vocabulary, but also to have sufficient factual ... | TinyStories-HowSmallCanLanguageModelsBeandStillSpeak CoherentEnglish? |
4940 choice). This type of conditional computation can
facilitate massive model scaling with fixed com-
putational resources (Shazeer et al., 2017; Lample
et al., 2019) and, while our current implementation
does not yet include an efficient implementation of
top-k routing (Section 2), we note that it is possible
with fas... | Entities as Experts- Sparse Memory Access with Entity Supervision |
Recent advances in large-scale generative models [Brown et al., 2020, Nichol et al., 2021, Ramesh
et al., 2021] have led to a major paradigm shift towards building general-purpose models, which
can perform many new tasks not explicitly trained on. These generative models learn to predict the
missing data given the cont... | Voicebox-Text-GuidedMultilingual UniversalSpeechGenerationatScale |
66
Mehrish et al.
5.9.2 Datasets
The speech quality assessment algorithms are evaluated using several datasets, each with unique
characteristics. The TIMIT Acoustic-Phonetic Continuous Speech Corpus [153] has clean speech
recordings and artificially generated degraded versions for speech synthesis and quality assessm... | AReviewofDeepLearningTechniquesforSpeechProcessing |
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