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a path from s0 to sn of length n if either (1) n = 0 or (2) (cid:3)si−1, si, (cid:2)i(cid:4) ∈ E for all i (1 ≤ i ≤ n). A sequence s0, s1, . . . , sn of
states in S is a state path from s0 to sn if there are labels (cid:2)1, . . . , (cid:2)n ∈ L(E) such that s0, (cid:2)1, s1, (cid:2)2, . . . , (cid:2)n, sn is a pat... | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
is approximately 0.85. A 4L model exposed to only 10 examples of harmful data gets an AUROC of 0.86 ±
0.01, while having only 13M parameters as compared to the 52B. The OOD detection gains from outlier
exposure are therefore very significant in comparison to the gains coming from model size scaling alone. | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
Cognitive architectures in computation, pioneered by Newell,
aimed to build the infrastructure for supporting a comprehensive
set of cognitive functions [75] that suited the all-encompassing
nature of believable agents held in its original vision. They fueled
some of the earliest examples of believable agents. For inst... | Generative Agents- Interactive Simulacra of Human Behavior |
4
(a)APCwithimbalancedsumunit.n1(b)ImbalancenessofthePCslearnedbyStrudel.X1X2Xn...⇥c1¬X1...⇥c2X2¬X2Xn¬Xn✓11✓12n1|supp(c1)|=1|supp(c2)|=2n | Tractable Regularization of Probabilistic Circuits |
For object detectors that utilize MIM to outperform prior work, techniques that allow
MIM to work with recent and high performing pyramid ViTs like Swin are critical. Since
pyramid ViTs collapse patches, random masking can leave some local windows with no
information. Li et al. [2022d] propose an approach to masking th... | A Cookbook of Self-Supervised Learning |
biodegradable material though.
RLHF Response → I think plastic straws are unnecessary and environmentally harmful. Plastic straws are super
wasteful and only serve a very minimal purpose. They basically just carry a drink from a
cup to your mouth, you can easily do this without a straw. Also they end up just contribut... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
• Financial resources. Obviously, getting money is helpful to seeking power. Options here
could include: stealing, investing, selling products and services (electronic and physical),
and trying to gain influence/control over wealthy actors. Exactly how much money a
PS-misaligned system can gain control over, though, dep... | Is Power-Seeking AI an Existential Risk? |
state, action (Angelopoulos et al. 2016). Therefore, as policymakers and the
public have increasingly demanded that platforms remove content that is
harmful or offensive, but not necessarily illegal, these discretionary rules have
become ever more important. | Social_Media_and_Democracy |
Meeting of the Association for Computational Linguistics, 2019.
[62] T. Yu, R. Zhang, H. Er, S. Li, E. Xue, B. Pang, X. V. Lin, Y. C. Tan, T. Shi, Z. Li, Y. Jiang,
M. Yasunaga, S. Shim, T. Chen, A. Fabbri, Z. Li, L. Chen, Y. Zhang, S. Dixit, V. Zhang,
C. Xiong, R. Socher, W. Lasecki, and D. Radev. CoSQL: A conversatio... | Teaching Large Language Models to Self-Debug |
24
Gemini: A Family of Highly Capable Multimodal Models
Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin
Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario
Amodei. Language models are few-shot learners. In H. Larochelle, M. Ranz... | gemini_1_report |
Figure 4 | Negative log likelihood as a function of token index across 32K context length on a held-out
set of long documents.
10
81632641282565121K2K4K8K16K32KSequence positionNLLProUltra Gemini: A Family of Highly Capable Multimodal Models
5.1.6. Human Preference Evaluations
Human preference of the model outputs p... | gemini_1_report |
However, the sequential nature of CoT might fall short in
nurturing creativity and innovation, potentially limiting so-
lutions in creative problem-solving scenarios [17, 18]. For
instance, proving an algebraic inequality often follows a
step-by-step CoT process that progresses from one inequal-
ity to the next. Yet, a... | Let’sThinkOutsidetheBox |
Topic #10
Politics
Cells
Unknown
Sports
Math
HTML/CSS
Legal
HTML/CSS
Electronics
Research
Unknown
Unknown
Geography
Differentiation
Software
Unknown
International
Politics
Generic
Unknown
Science
Cells
Email
Topic #3
Generic
Cells
Unknown
Sports
Modeling
Unknown
Legal
Server
Devices
Animal Study
Unknown
Unknown
Spor... | The Pile- An 800GB Dataset of Diverse Text for Language Modeling |
To obtain gradients for the canonical point, we observe
that the location of the surface-ray intersection is implic-
itly defined by the geometry and deformation network with
two constraints: 1. the canonical point must lie on the sur-
face and 2. its deformed location must be on the marched
ray. Given these equality co... | I M Avatar- Implicit Morphable Head Avatars from Videos |
Jupyter – structured To create this dataset, we first filtered out notebooks that did not contain any
Python code or Markdown text. The information on the programming language in the metadata of
each notebook was used as the criterion to filter out non-Python notebooks. Only notebooks explicitly
marked as ‘Python’ in t... | StarCoder_paper (1) |
should ignore characters that aren’t letters, numbers, or spaces.
6. Write a function that returns the longest palindromic substring in a given string.
7. Create a function that will find the length of the longest substring without repeating characters in a given string.
8. Write a function that reverses the diagits in... | CodeLlama2 |
automatically learn the distribution of embeddings, serving as a substitute for manual verbalizer design. UPT [274] introduces
the Prompt-Options-Verbalizer paradigm, facilitating joint prompt learning across diverse NLP tasks and encouraging LLMs
to acquire task-invariant prompting knowledge. | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
Q: The man wanted to take a bus, but all the chars were full of people doing what? Choices: A.sitting down B.refusing
C.saving money D.sit down E.go outdoors
A: Reasoning process: 1. Eliminate answer C ("saving money") because it doesn’t have anything to do with taking a bus.
2. Eliminate answer E ("go outdoors") becau... | Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models |
CoT + non-CoT
Non-CoT
62B
540B
8B
Model size (# parameters)
CoT
No finetuning
Figure 5: Jointly finetuning on non-CoT and CoT data improves performance on both evaluations, compared
to finetuning on just one or the other. | Scaling Instruction-Finetuned Language Models |
A single model mastering all these aspects would
make a strong contribution to the music industry,
as it can enable the broader public to be part of
the creative process by allowing them to compose
music using an accessible text-based interface, as-
sist creators in finding inspiration, and provide an
unlimited supply ... | Moûsai |
MODEL ANSWER (INCORRECT): Nazem Kadri is a hockey player. Being out at home is part of hockey.
So the answer is yes. (cid:55) (Ground truth is “no”, being out at home is part of baseball.) | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models |
ship using Sinkhorn to infer the cluster membership probabilities
• Grill et al. [2020, BYOL] introduces BYOL removing the clustering step, introducing a predictor and
projector network, defining the continuous targets as the output of a momentum network, renormalize
each sample representation by its (cid:96)2-norm and... | A Cookbook of Self-Supervised Learning |
Outcomes of the proposed research will be grounded in theoretical foundations, validated and verified
through empirical evaluation. Methods to achieve the project goals will include and not limited to:
symbolic modelling to map user vs system knowledge, sentiment analysis and topic modelling,
information visualizati... | informatics-phd-projects-2022-23 |
a variety of downstream tasks, often competi-
tive with much larger models, without sacrific-
ing its core language modeling abilities. | Toolformer |
!"Speech synthesis has been around for a while (hello Siri!) but consumer and enterprise applications are just getting good. For high-end applications like film and podcasts the bar is quite high for one-shot human quality speech that doesn’t sound mechanical. But just like with images, today’s
!"Video and 3D models are... | Generative AI A Creative New World Sequoia Capital |
layers in the pre-trained Whisper model, and that the final 3 layers are necessary for ensuring high
transcription accuracy. We leave finding effective early exit schemes for Seq2Seq ASR models as
future work. | DISTIL-WHISPER |
2. pre(g(a)) = pre(a)[V C] and post(g(a)) = post(a)[V C], for all a ∈ A1.
3.
4. R = {(cid:3)a, g(a)(cid:4) | a ∈ A}.
We note that this is a homomorphic abstraction where |E2| ≤ |E1| holds. | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
[349] Yen-Ju Lu, Zhong-Qiu Wang, Shinji Watanabe, Alexander Richard, Cheng Yu, and Yu Tsao. 2022. Conditional diffusion
probabilistic model for speech enhancement. In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech
and Signal Processing (ICASSP). IEEE, 7402–7406.
[350] Loren Lugosch, Mirco Ravanell... | AReviewofDeepLearningTechniquesforSpeechProcessing |
offloading. arXiv preprint arXiv:2312.17238, 2023.
[12] William Fedus, Jeff Dean, and Barret Zoph. A review of sparse expert models in deep learning.
arXiv preprint arXiv:2209.01667, 2022.
[13] Trevor Gale, Deepak Narayanan, Cliff Young, and Matei Zaharia. Megablocks: Efficient sparse
training with mixture-of-exper... | Mixtral of Experts paper |
llms. MosaicML, 2023.
Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze
Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, et al. Lamda: Language models for dialog
applications. arXiv preprint arXiv:2201.08239, 2022.
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Mart... | ChatGPT’sOne-yearAnniversary-AreOpen-Source LargeLanguageModelsCatchingup |
So far, research in the area of social attitudes toward augmented humans has been limited due to the lack
of assessment tools. Work that considered attitudes towards augmented humans was mainly conducted using
qualitative methods [77, 82]. Quantitative studies in the domain have adapted conventional scales ,e.g. from t... | Society’sAttitudesTowardsHumanAugmentation |
sion tasks. To assess the effectiveness of each comprehension task type, we employ general LLM
benchmarks to evaluate zero-shot prompting performance. Specifically, we evaluate at least three
general tasks for each comprehension task type, following the task clustering settings in FLAN (Wei
et al., 2022). Besides, we a... | ADAPTINGLARGELANGUAGEMODELSVIA READINGCOMPREHENSION |
3688 Hongyu Ren, Weihua Hu, and Jure Leskovec. 2020.
Query2box: Reasoning over knowledge graphs in
International
vector space using box embeddings.
Conference on Learning Representations.
Sebastian Riedel, Limin Yao, Andrew McCallum, and
Benjamin M Marlin. 2013. Relation extraction with
matrix factorization and univer... | Adaptable and Interpretable Neural Memory Over Symbolic Knowledge |
https://www.paradigm.xyz/2023/09/casino-on-mars
5/9
21/09/2023, 08:13
The Casino on Mars
are not topics that we are accustomed to reconsidering, and it is natural that
some people | The Casino on Mars |
For the fine-tuning of our modified autoencoder, we used
a KL-autoencoder architecture with a downsampling factor
of 8 time the pixel space image resolution. This downsam-
pling factor was found to be optimal in terms of fast training
process and high-quality image synthesis [20].
During the fine-tuning process, we used t... | LDM3D- Latent Diffusion Model for 3D |
4.1 HIDDEN CHOW-LIU TREES
Hidden Chow-Liu Trees (HCLTs) are smooth and structured-decomposable PCs that combine the
ability of Chow-Liu Trees (CLTs) (Chow & Liu, 1968) to capture feature correlations and the extra
6HCLT will be introduced in Sec. 4.1; all algorithms use a CPU implementation of rANS as codec. See
App... | LOSSLESS COMPRESSION WITH PROBABILISTIC CIRCUITS |
pη(z|x) ∝ exp(cid:0)d(z)(cid:62)q(x)(cid:1)
d(z) = BERTd(z), q(x) = BERTq(x)
where d(z) is a dense representation of a document produced by a BERTBASE document encoder [8],
and q(x) a query representation produced by a query encoder, also based on BERTBASE. Calculating
top-k(pη(·|x)), the list of k documents z with h... | Retrieval-AugmentedGenerationfor Knowledge-IntensiveNLPTasks |
5.2. Variational Autoencoders
VAEs (Kingma & Welling, 2014) are one of the most widely
used likelihood-based deep generative models. We adopt a
conditional VAE to a TTS system. A conditional VAE is a
conditional generative model where the observed conditions
modulate the prior distribution of latent variables used to
... | ConditionalVariationalAutoencoderwithAdversarialLearningfor End-to-EndText-to-Speech |
(cid:88)
(cid:96),b:x∈X (cid:96)
b
q(x) =
1
B
d(cid:89)
j=1
q(θ(cid:96)
b)
q(xj; ψ(cid:96)
b,j).
(1)
Compare this with the true density:
p(x) =
1
B
p(θ(cid:96)
b) p(x|θ(cid:96)
b).
(2)
In both cases, the density evaluated at a given point is just a
coverage-weighted average of its density in all leaves... | Adversarial Random Forests for Density Estimation and Generative Modeling |
19We view simplicity as essential, as an ad hoc, case-by-case treatment of AI failure modes will likely only treat visible
symptoms and create a false sense of security.
36 | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
(cid:123)(cid:122)
(cid:125)
(cid:124)
(cid:123)(cid:122)
(cid:125)
retrieval
generation
(1)
However, this marginal probability involves an intractable
summation over all size-K subsets of the memory corpora
˜M. We approximate this instead by using the top-K entries
in memory with the highest probability under ... | REVEAL-Retrieval-AugmentedVisual-LanguagePre-Trainingwith Multi-SourceMultimodalKnowledgeMemory |
256 | A Cookbook of Self-Supervised Learning |
74
Alexandra A. Siegel
speech intended to influence discourse and behavior (Benesch 2014a, 2014b).
Counter-speech campaigns have long been used to combat the public expression
of hate speech and discrimination through traditional media channels.
Examples of this in the US context include the use of anti-KKK billboards... | Social_Media_and_Democracy |
Since neither the seed data nor the augmented data intentionally include “red teaming" demonstration
examples nor does the finetuning stage optimize for detecting and reducing potential harm, we
evaluate the model on 30 potentially sensitive prompts to understand our model’s safety implications.
We found that for these... | Self-AlignmentwithInstructionBacktranslation |
2.3 Self-Curation (selecting high-quality examples)
We select high quality examples using the language model itself. We start with a seed instruction
model M0 finetuned on (instruction, output) seed examples only. We then use M0 to score each
augmented example {(ˆxi, yi)} to derive a quality score ai. This is done usi... | Self-AlignmentwithInstructionBacktranslation |
Overall, my current best guess is that ensuring the full PS-alignment of APS systems is going to be
very difficult, especially if we build them by searching over systems that satisfy external criteria, but
which we don’t understand deeply, and whose objectives we don’t directly control.
It’s harder to reason in the abst... | Is Power-Seeking AI an Existential Risk? |
4.2 Reflection
Challenge: Generative agents, when equipped with only raw ob-
servational memory, struggle to generalize or make inferences.
Consider a scenario in which Klaus Mueller is asked by the user:
“If you had to choose one person of those you know to spend an
hour with, who would it be?" With access to only obs... | Generative Agents- Interactive Simulacra of Human Behavior |
without sacrificing performance on non-code related benchmarks.
Mistral 7B leverages grouped-query attention (GQA) [1], and sliding window attention (SWA) [6, 3].
GQA significantly accelerates the inference speed, and also reduces the memory requirement during
decoding, allowing for higher batch sizes hence higher thro... | Mistral7B |
understanding. arXiv preprint arXiv:1910.14599, 2019.
[75] OpenAI. Gpt-4 system card.
[76] OpenAI. Gpt-4 technical report, 2023.
[77] Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama,
Alex Ray, et al. Training language models to follow ... | Harnessing the Power of LLMs in Practice- A Survey on ChatGPT and Beyond |
about the generally problematic system of privacy-invasive advertising that funds
contemporary platforms without actually making these systems more just, the
efforts will fail to achieve a democratically desirable outcome (Taylor 2017).
Furthermore, certain forms of transparency can “intentionally occlude” (Ananny
and ... | Social_Media_and_Democracy |
Xiang Yue, Xingwei Qu, Ge Zhang, Yao Fu, Wen-
hao Huang, Huan Sun, Yu Su, and Wenhu Chen.
2023. Mammoth: Building math generalist models
through hybrid instruction tuning. arXiv preprint
arXiv:2309.05653.
Renrui Zhang, Jiaming Han, Aojun Zhou, Xiangfei Hu,
Shilin Yan, Pan Lu, Hongsheng Li, Peng Gao, and
Yu Qiao. 2023a... | DataManagementForLargeLanguageModels-ASurvey |
Figure 3 illustrates the evaluation process of AI models, including LLMs. Some evaluation
protocols may not be feasible to evaluate deep learning models due to the extensive training size.
Thus, evaluation on a static validation set has long been the standard choice for deep learning
models. For instance, computer visi... | ASurveyonEvaluationofLargeLanguageModels |
Page 7 of 15 32
4 Results
4.1 Association between belief in COVID‑19
numbers and preventative behaviors | Use of bot and content flags to limit the spread of misinformation among social networks: a behavior and attitude survey |
combining science, engineering and design. It delivers world class results in education,
research and innovation to address challenges in the areas of energy, climate, mobility,
health and digital society. For generations, our engineers have proven to be
entrepreneurial problem-solvers, both in business and in a social... | Job details - TU |
Desplanques et al. [108] propose a state-of-the-art architecture for speaker verification utilizing
a Time Delay Neural Network (TDNN) called ECAPA-TDNN. The paper presents a range of
enhancements to the existing 𝑥-vector architecture that leverages recent developments in face
56
Mehrish et al.
verification and co... | AReviewofDeepLearningTechniquesforSpeechProcessing |
data can be learned more effectively by the model
is preferable and data lacking informative content
or excessively demanding for the model should be
avoided. They also propose LoBaSS method to se-
lect SFT data using learnability as the principal cri-
terion measured by the loss difference between fine-
tuned and pre-... | DataManagementForLargeLanguageModels-ASurvey |
organization of the chapter
In this chapter, we synthesize recent work on misinformation and its correction.
Knowledge of this subject is still rapidly developing, and many questions remain
unanswered and unresolved.1 Here, we pay particular attention to one of these
important questions: Why does misinformation persis... | Social_Media_and_Democracy |
PROMPT FOR SAYCAN ROBOT PLANNING
Locations = [counter, table, user, trash, bowl].
Objects = [7up, apple, kettle chips, tea, multigrain chips, coke, lime soda, jalapeno chips, rice chips, orange,
grapefruit soda, pepsi, redbull, energy bar, sponge, water].
The robot can pick up items with pick(object) and put down items... | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models |
speech law can be easier to research, in part simply because the rules come from
public law rather than platforms’ discretionary Community Guidelines. There
is also somewhat more information available about how platforms apply legal
rules and what specific content they take down. 1 This has allowed independent
experts t... | Social_Media_and_Democracy |
To empirically evaluate the performance of the proposed approach, we conducted experiments on the
English and Spanish languages using various datasets, including the Common Voice 11 dataset Ardila
et al. [2020], as well as two synthesized datasets derived from the Conversational Jia et al. [2019a]
and Common Voice 11 d... | Translatotron3 |
First, we prove that w ∈ Laj . To do so, we must show that aj ∈ arg maxa(cid:48)∈A Eo∼F|a(cid:48) [w(o)] −
[w(o)] − ψ(aj) ≥ Eo∼F|aj(cid:48) [w(o)] − ψ(aj(cid:48)) ∀j(cid:48) ∈ [q]. That is,
)(w2 − w1) ≥ ψ(aj) − ψ(aj(cid:48)) ∀j(cid:48) ∈ [q]. Replacing w and ψ, the last inequality holds if
[w(o)]. We show that one w w... | Incomplete Information VCG Contracts for Common Agency |
Biomedical Literature. arXiv preprint arXiv:2306.04926 (2023).
[87] Douwe Kiela, Max Bartolo, Yixin Nie, Divyansh Kaushik, Atticus Geiger, Zhengxuan Wu, Bertie Vidgen, Grusha
Prasad, Amanpreet Singh, Pratik Ringshia, et al. 2021. Dynabench: Rethinking benchmarking in NLP. arXiv preprint
arXiv:2104.14337 (2021).
[88] ... | ASurveyonEvaluationofLargeLanguageModels |
Current limitations to solve
We’re early in the hype cycle as it relates to AIs impact on fintech.
Accuracy and data security are the two main concerns fintech buyers share
when debating adopting
Right now companies that are popping up are more geared toward content
summarization (e.g. earnings call summaries) vs. v... | Fintech x AI_ The Lightspeed View _ by Lightspeed _ Lightspeed Venture Partners _ Jun, 2023 _ Medium |
To steer our models at a more fine-grained level, we relied heavily on our models themselves
as tools. One of our main tools for steering the model towards appropriate refusals is rule-based
reward models (RBRMs).[98, 99] This technique uses a GPT-4 classifier (the RBRM) to provide an
additional reward signal to the GPT-... | gpt-4-system-card |
main definition language (PDDL), then leveraging classical planners to quickly
find a solution, and then translating the found solution back into natural language.
Along with LLM+P, we define a diverse set of different benchmark problems
taken from common planning scenarios. Via a comprehensive set of experiments
on these... | LLM+P- Empowering Large Language Models with Optimal Planning Proficiency |
In brief, for most traditional NLU tasks, a fine-tuned model is a better choice in terms of the performance on benchmark
datasets and the computational cost. The scale of LLMs is usually 10× or even 100× larger than fine-tuned models.
One possible cause for the inferior performance of LLMs on certain tasks can be the d... | Harnessing the Power of LLMs in Practice- A Survey on ChatGPT and Beyond |
A natural question is how important the curriculum learning where the model is required to success-
fully learning 1 through N digit addition before N + 1 digit examples are added in the training step.
We run an ablation where we train a 582M parameter ByT5 model on 1 through 6 digit addition
in a single supervised fin... | CHAIN-OF-THOUGHTREASONING IS APOLICY IMPROVEMENTOPERATOR |
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... | Language models can explain neurons in language models |
forms of democratically important information (Flyverbom 2016). As some
hoped that information and communication technologies could start laying the
foundations for “a culture of transparency” in countries without a long-
standing history of democratic governance (Bertot, Jaeger, and Grimes 2010,
p. 267), Heemsbergen (... | Social_Media_and_Democracy |
e
| The a16z Investment Thesis on AI in Bio + Health _ Andreessen Horowitz |
6 ABLATIONS ON TRAINING DATA | ADAPTINGLARGELANGUAGEMODELSVIA READINGCOMPREHENSION |
(12)
i=1,
is the special
mi = e(vp), i is the p-th special token; otherwise, mi = 0,
token adaptation. PASTA enables a re-
mi
markable reduction in trainable parameters by training only
the trainable vector e(vp) to update the representations of
special tokens. The reasons for using [CLS] and [SEP] as
special tokens... | Parameter-EfficientFine-TuningMethods |
Image&Text to Text (JP) > Woman: You've been waiting fora while, my apologies. Please, takeyour time and enjoy! > Man: ________________ ？Image&Text to Text (JP) > Woman: ________________ ？> GPT4v: この紳士はおそらく「サプライズセット」を注文したようで、彼の前には疑問符のついた枠が現れました。彼自身も考えているように、これは新しいトレンドの見えない美食なのかしら？@ The gentleman seems to have ordered a... | Let’sThinkOutsidetheBox |
The following section shifts the focus from task-specific
evaluation methods and metrics to provide a synthesis of the
existing literature based on their unique attributes. This ex-
ploration covers the objectives of RAG evaluation, the aspects
along which these models are assessed, and the benchmarks
and tools availab... | RAG forLargeLanguageModels-ASurvey |
Tokenization: In real-time applications, tokenization has
to be performed on the fly. This involves breaking down the
stream of text into smaller units or tokens.
Stop word removal and stemming/lemmatization: For
real-time processing, a predefined list of stop words can be
used to filter out these common words from th... | FinGPT-Open-SourceFinancialLargeLanguageModels |
emotions with several unique features like color and ten-
sion, e.g., major chords bring a feeling of brightness, while | VideoBackgroundMusicGeneration |
[43] Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder,
and Li Deng. MS MARCO: A human generated machine reading comprehension dataset. In
Tarek Richard Besold, Antoine Bordes, Artur S. d’Avila Garcez, and Greg Wayne, editors,
Proceedings of the Workshop on Cognitive Computation: Integr... | Retrieval-AugmentedGenerationfor Knowledge-IntensiveNLPTasks |
In practice there will usually be some reason for composing two transformations, for instance, to let F2 and F3 represent
different abstraction levels for F1. It is then reasonable that M2 does not contain any landmarks on the variables in M1,
since this is redundant; we note in the example above that both the land... | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding,
Yujie Qian, Zhilin Yang, and Jie Tang. 2021. Gpt
understands, too. arXiv preprint arXiv:2103.10385.
Yang Liu and Mirella Lapata. 2019. Text summariza-
In Proceedings of
tion with pretrained encoders.
the 2019 Conference on Empirical Methods in Nat-
ural Language Processi... | Prefix-Tuning |
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this paper demonstrate the importance of diverse in-
structiondata,andourlargesyntheticdatasetcanbe
the first step toward higher-quality data for building
better instruction-following models.
6.3 Limitations of SELF-INSTRUCT
Here, we discuss some limitations of this work to
inspire future research in this direction.
Tai... | SELF-INSTRUCT- Aligning Language Model with Self Generated Instructions |
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Translatotron 3: Speech to Speech Translation with
Monolingual Data
Eliya Nachmani1∗ Alon Levkovitch1 Yifan Ding2 Chulayuth Asawaroengchai1
Heiga Zen 2 Michelle Tadmor Ramanovich1
1Google Research, 2Google DeepMind
Abstract | Translatotron3 |
–
84.96±7.16
Table 7: Ablation if discretization in canonicalization.
Knowledge
(cid:51)
(cid:51)
(cid:55)
Post-validation HPO-B ↑ HyperBO ↑ HyperFD ↓
59.74±1.89
62.41±3.66
63.20±3.55
81.59±0.94
78.34±0.71
76.21±0.16
1.48±0.06
1.44±0.05
1.36±0.09
(cid:51)
(cid:55)
–
Table 8: Ablation on knowledge utilization i... | MLCopilot- Unleashing the Power of Large Language Models in Solving Machine Learning Tasks |
An Example PDDL Problem File Written by GPT-4 with Context
Context (shaded):
An example planning problem is: You have 5 blocks. b2 is on top of b5. b5 is on top of b1.
b1 is on top of b4. b3 is on top of b2. b4 is on the table. b3 is clear. Your arm is empty. Your
goal is to move the blocks. b4 should be on top of b3.... | LLM+P- Empowering Large Language Models with Optimal Planning Proficiency |
mode switching, wherein downstream fine-tuning is associated with specific pre-training schemes. We
conduct extensive ablative experiments to compare multiple pre-training objectives and find that our method
pushes the Pareto-frontier by outperforming T5 and/or GPT-like models across multiple diverse setups.
Finally, by s... | UL2- Unifying Language Learning Paradigms |
System Type
System Description
Upstream Dependencies
Downstream Dependencies
PaLM 2 is a language model.
Section 3 and Appendix E.1
See Section E.2 for discussion on anticipated priority uses.
Hardware & Software
Hardware: TPU v4 (Jouppi et al., 2020).
Implementation Frameworks
Software: Pathways (Barham et al., ... | PaLM 2 Technical Report |
sequences detected as approximate matches with matching
score less than 0.5. We include a sample of approximate
matches obtained with T = 0 in the accompanying material.
Note that acoustic modeling carried out by the second stage
introduces further diversity in the generated samples, also
when the semantic tokens match... | MusicLM |
All models are fine-tuned using the Adam optimizer (Kingma & Ba, 2015) and the DeepSpeed
library (Aminabadi et al., 2022) with a constant learning rate of 10−4. We used a batch size of
2048 for our experiments with the 300M parameter model and 1024 for the 582M parameter model,
which were the maximum possible sizes tha... | CHAIN-OF-THOUGHTREASONING IS APOLICY IMPROVEMENTOPERATOR |
Topic #14
game
team
season
year
said
cells
study
data
time
group
said
like
new
people
man
flight
caption
aircraft
add
water
let
phys
order
model
case
int
struct
return
case
static
court
plaintiff
state
case
district
string
return
public
new
class
optical
surface
device
invention
system
method
artery
surface
energy
optic... | The Pile- An 800GB Dataset of Diverse Text for Language Modeling |
Ouyang et al., 2022; Thoppilan et al., 2022; Work-
shop et al., 2023; Muennighoff et al., 2022). The
strong foundational capability of LLMs, achieved
through large-scale pre-training on massive text
corpora (e.g., In-Context Learning (Brown et al.,
2020a), Chain-of-Thoughts (Wei et al., 2022c,b),
among others), is a co... | Unleashing Infinite-Length Input Capacity for Large-scale Language Models with Self-Controlled Memory System |
outside the building and onto the other side of
the small road the hearses drive down. Now the
road is important because obviously if there’s a
road, there’s a curb onto the sidewalk, so most
of us are on the other side of the road, besides
a few older people walking a lot slower.
As one of the old woman goes to walk u... | Direct Preference Optimization |
Figure 4: Percentages of mined examples of each task type in all the comprehension task ex-
amples.
In the biomedicine domain, we conduct ablations on each comprehension task type by systemat-
ically removing each task type from the reading comprehension texts. We then use the resulting
modified reading comprehension ... | ADAPTINGLARGELANGUAGEMODELSVIA READINGCOMPREHENSION |
Karan Goel, Albert Gu, Chris Donahue, and Christopher
Ré. 2022. It’s raw! audio generation with state-space
models. In International Conference on Machine Learn-
ing, ICML 2022, 17-23 July 2022, Baltimore, Maryland,
USA, volume 162 of Proceedings of Machine Learning
Research, pages 7616–7633. PMLR.
Gal Greshler, Tama... | Moûsai |
165Note that this could be compatible with Bostrom’s (2014) formulation of the “orthogonality thesis”—e.g.,
“Intelligence and final goals are orthogonal: more or less any level of intelligence could in principle be combined
with any final goal.” That is, Bostrom’s formulation only applies to the “in principle” possibilit... | Is Power-Seeking AI an Existential Risk? |
To obtain valid proofs during prediction, we
need to lexically constrain the inference process
by switching between three different search spaces
depending on which element of the triple is being
predicted. To achieve this, we use dynamically
constrained markup decoding (De Cao et al.,
2021), a modified form of lexical... | ProoFVer- Natural Logic Theorem Proving for Fact Verification |
of Justice, Federal Trade Commission, and State Attorneys General into the
potential viewpoint-based content moderation policies of the platforms. It
ended by calling for legislation to implement the positions expressed in the
order. | Social_Media_and_Democracy |
The simplest approach involves human guidance throughout the process: humans provide clear and
specific instructions directly, while the agents’ role is to understand natural language commands from
humans and translate them into corresponding actions [459; 460; 461]. In §4.1, we have presented
the scenario where agents... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.4
0.8
0.0
3.2
0.0
Direct CoT Direct CoT Direct CoT Direct CoT Direct
28.0
47.2 52.8 10.4 10.8 49.6 47.6 24.4
42.0 32.0 52.4 58.4 35.2 56.0 67.2 72.4 31.6
14.8 40.0 58.0 55.2 36.8 60.4 60.8 53.2 44.0
46.8 56.8 52.4 50.4 32.0 54.4 60.4 66.4 32.4
8.4
52.8
0.0
52.0
2.4
17.2
53.2 46.8
... | Scaling Instruction-Finetuned Language Models |
G MEASURING SIMILARITY BETWEEN SUBSPACES
A, U j
to measure the subspace
In this paper we use the measure φ(A, B, i, j) = ψ(U i
B ∈ Rd×j, obtained by
similarity between two column orthonormal matrices U i
taking columns of the left singular matrices of A and B. We point out that this similarity is simply
a reverse of t... | LORA |
Brian Lester, Rami Al-Rfou, and Noah Constant. The power of scale for parameter-efficient prompt
In Proceedings of the 2021 Conference on Empirical Methods in Natural Language
tuning.
Processing, pp. 3045–3059, Online and Punta Cana, Dominican Republic, November 2021.
Association for Computational Linguistics. doi: 10.1... | STANDING ON THE SHOULDERS OF GIANT FROZEN LANGUAGE MODELS |
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