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method, we utilize the same learning architecture to map the
input image into the PCA-based texture space. Furthermore,
to evaluate the effectiveness of the proposed texture infer-
ence modules, we also conduct ablative analysis on BiCarNet
without Fuser and BiCarNet without Part-sensitive Reasoner
(PSR). Table 2 shows... | RaBit- Parametric Modeling of 3D Biped Cartoon Characters with a Topological-consistent Dataset |
arXiv preprint arXiv:2004.08900, 2020.
Devendra Singh, Siva Reddy, Will Hamilton, Chris Dyer, and Dani Yogatama. End-to-end
training of multi-document reader and retriever for open-domain question answering.
In
M. Ranzato, A. Beygelzimer, Y. Dauphin, P.S. Liang, and J. Wortman Vaughan (eds.), Ad-
vances in Neural Info... | STANDING ON THE SHOULDERS OF GIANT FROZEN LANGUAGE MODELS |
Training an LLM using RLHF methods based on InstructGPT can be a complex process, but
here are some general steps you can follow:
1. Collect and preprocess data: You will need a large dataset of natural language text to train
your LLM. You can use existing datasets such as the Common Crawl or create your own
by scrapin... | WizardLM- Empowering Large Language Models to Follow Complex Instructions |
22
22
Lakehouse
Platform
Data warehousing grows,
with emphasis on serverless
Over the past 2 years, companies have vastly increased their usage
of data warehousing on the Lakehouse Platform. This is especially
demonstrated by use of Databricks SQL — the serverless data
warehouse on the Lakehouse — which show... | databrick 2023 report |
(5)
The full reconstructed image is then composited pixelwise
from the outputs of the two models:
i = D(Ii).
i (r) + (1 − αdy
i (r) ˆBdy
i (r) = αdy
i (r)) ˆBst(r).
ˆBfull
(6)
To segment moving objects, we assume the observed
pixel color is uncertain in a heteroscedastic aleatoric manner,
and model the observatio... | DynIBaR-NeuralDynamicImage-BasedRendering |
December 11, 2023
Mistral AI team
Mistral AI continues its mission to deliver the best open models to the developer community. Moving
forward in AI requires taking new technological turns beyond reusing well-known architectures and
training paradigms. Most importantly, it requires making t... | Mixtral of experts |
15
Figure 4: An illustration of the performance of models in the Falcon (top) and LLaMA (bottom) families in
the non-instruction-tuned zero shot setting on a the selected subset of tasks, demonstrating the consistent lack of
emergent abilities in the absence of in-context learning.
ates responses limited to "yes" or... | AreEmergentAbilitiesinLarge Language Models just In-Context |
Domain Conversations with Large Language Models. arXiv preprint arXiv:2305.13711 (2023).
[113] Chuang Liu, Renren Jin, Yuqi Ren, Linhao Yu, Tianyu Dong, Xiaohan Peng, Shuting Zhang, Jianxiang Peng, Peiyi
Zhang, Qingqing Lyu, Xiaowen Su, Qun Liu, and Deyi Xiong. 2023. M3KE: A Massive Multi-Level Multi-Subject
Knowledge... | ASurveyonEvaluationofLargeLanguageModels |
20
DATASET
GSM8K
Iter-CoT(W) Exemplars
Q: Ben’s potato gun can launch a potato 6 football fields. If a football field is 200 yards long and Ben’s dog can run 400
feet/minute, how many minutes will it take his dog to fetch a potato he launches?
A: Reasoning Process: To find the total distance that the potato can travel... | Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models |
ponent. Standard metrics from the domains of search en-
gines, recommendation systems, and information retrieval
systems are employed to measure the performance of the
RAG retrieval module. Metrics such as Hit Rate, MRR, and
NDCG are commonly utilized for this purpose [Liu, 2023,
Nguyen, 2023].
Generation Quality
The a... | RAG forLargeLanguageModels-ASurvey |
Our results in Figure 9 (a) show that memorization may worsen on languages further in the tail. In particular, we
observe that in data sources with fewer documents, it takes fewer repetitions of these outlier canaries for extraction to
succeed. However, we observe in Figure 9 (b) that on real training data, this is oft... | PaLM 2 Technical Report |
Current methods for learning realistic and animatable 3D
clothed avatars need either posed 3D scans or 2D images
with carefully controlled user poses. In contrast, our goal is
to learn an avatar from only 2D images of people in uncon-
strained poses. Given a set of images, our method estimates
a detailed 3D surface fro... | ICON |
Characterization of Immunoglobulins
Supervisors: Professor Costas Iliopoulos, Dr Sophia Karagiannis & Dr Grigorios Loukides
Antibodies, or immunoglobulins, belong to the ‘gamma globulin' protein group and can be found
mainly in the blood of vertebrates [1]. Antibodies constitute the major serological line of defen... | informatics-phd-projects-2022-23 |
5
AI21 Summarize API
TECHNICAL EVALUATION
B.2 Statistical significance | AI21 SUMMARIZE API- TECHNICAL EVALUATION |
2.1 THE ID-PT ARCHITECTURE
The prompt-tuning method of Lester et al.
(2021) is a simple and effective method for ex-
ternally tuning a frozen model. For a given
task, a fixed number of continuous token em-
beddings is optimized when concatenated to the
input embeddings of each training example (il-
lustrated in Figure ... | STANDING ON THE SHOULDERS OF GIANT FROZEN LANGUAGE MODELS |
These two tricks along with the overall model recipe significantly improve codebook usage, and
therefore bitrate efficiency (Figure 1) and reconstruction quality (Table 2), while being simpler to
implement. Our model can be trained using the original VQ-VAE codebook and commitment losses
[38], without k-means initializ... | RVQGAN |
Also, some tasks in Big-bench[96], which are designed to probe LLMs and extrapolate their future capabilities, heavily
relied on the memorization of real-world knowledge. In such tasks, the performance of some LLMs is better than the
average level of humans, and even comparable to the best human performance. For exampl... | Harnessing the Power of LLMs in Practice- A Survey on ChatGPT and Beyond |
victims of misinformation: moderators
of misinformation and its correction
Overall, misinformation appears both pervasive and difficult to correct once it
spreads. However, not all misinformation is created equal, nor are all
individuals equally susceptible to its influence. Thus,
it is important to
examine which groups... | Social_Media_and_Democracy |
Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Den-
ton, E. L., Ghasemipour, S. K. S., Ayan, B. K., Mahdavi,
S. S., Lopes, R. G., Salimans, T., Ho, J., Fleet, D. J., and
Norouzi, M. Photorealistic text-to-image diffusion mod-
els with deep language understanding. arXiv:2205.11487,
2022.
Schroff, F., Kalenichenk... | MusicLM |
(a) Step 1: Img-to-img inference pipeline for LDM3D. initiating from a panoramic image and corresponding depth map computed using DPT-Large [18,19].
The RGBD input is processed through the LDM3D image-to-image pipeline, generating a transformed image and depth map guided by the given text prompt.
(b) Step 2: LDM3D gen... | LDM3D- Latent Diffusion Model for 3D |
ure 2. The diversity gain [5] indicates how
diverse the question is compared to the exist-
ing dataset, and larger diversity gain means the
new question is more different from the existing
dataset. With question bootstrapping, our Meta-
MathQA dataset is much more diverse than the
original dataset. We also observe that... | METAMATH |
Khandelwal, U., Levy, O., Jurafsky, D., Zettlemoyer, L., and
Lewis, M. Generalization through memorization: Nearest
neighbor language models. ArXiv, abs/1911.00172, 2019.
Kiros, R., Zhu, Y., Salakhutdinov, R. R., Zemel, R., Urtasun,
R., Torralba, A., and Fidler, S. Skip-thought vectors. In
Advances in neural informati... | REALM |
6.1.4 Low-rank approximation | Beyond Efficiency |
Reinforcement Learning (RL) allows the model to learn
through interaction with the environment and receiving feed-
back.
In the case of RLSP, the environment is the stock
market, and the feedback comes in the form of stock price
changes. This approach permits FinGPT to refine its under-
standing and interpretation of f... | FinGPT-Open-SourceFinancialLargeLanguageModels |
Video-Conditional Music Generation. Most music gen-
eration methods fall into the unconditional setting [8, 22,
24, 21]. Previous video-conditional music generation works
mainly focus on reconstructing music from silent instru-
ment performance videos [44, 14, 43, 27]. Recent methods
[45, 56, 57] are proposed to genera... | VideoBackgroundMusicGeneration |
5. Experiments
5.1. Evaluation Questions and Metrics
Inspired by the humor benchmarks in [80], we first develop
choice and ranking questions as introduced in Sec. 4.1 (see
examples in Fig. 6 (c-d)), and then quantitatively evaluate
the LoT ability of LLMs on the Oogiri-GO test dataset. For
the choice questions, mTn for... | Let’sThinkOutsidetheBox |
Finally, we advocate for a thorough and system-
atic exploration of the nature of abilities which
indicate the potential for unexpected dangers in
models. This would involve the design of tasks that
test those specific abilities which, if left unchecked,
might result in unpredictable and dangerous be-
haviours of model... | AreEmergentAbilitiesinLarge Language Models just In-Context |
voice cloning toolkit (version 0.92). 2019.
R. Yamamoto, E. Song, and J.-M. Kim. Parallel WaveGAN: A fast waveform generation model based
on generative adversarial networks with multi-resolution spectrogram. In International Conference
on Acoustics, Speech and Signal Processing, 2020.
N. Yu, V. Skripniuk, S. Abdelnab... | Voicebox-Text-GuidedMultilingual UniversalSpeechGenerationatScale |
Despite these advantages, Uesato et al. (2022) found that outcome supervi-
sion and process supervision led to similar final performance in the domain of
grade school math. We conduct our own detailed comparison of outcome and
process supervision, with three main differences: we use a more capable base
model, we use si... | Let’s Verify Step by Step |
[19] E.M. Clarke, O. Grumberg, S. Jha, Y. Lu, H. Veith, Counterexample-guided abstraction refinement for symbolic model checking, J. ACM 50 (2003)
[20] E.M. Clarke, O. Grumberg, D.E. Long, Model checking and abstraction, ACM Trans. Program. Lang. Syst. 16 (1994) 1512–1542.
[21] P. Cousot, R. Cousot, Abstract interpreta... | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
Spider (Dev)
w/ training
Graphix-T5 [31]
T5-3B + Syn data [66]
T5-3B + N-best Reranking [64]
LEVER [37]
Prompting only w/o debugging
Rajkumar et al. [43]
Coder-Reviewer [65]
MBR-Exec [49]
This work
Codex
Simple
+ Expl.
81.0
81.4
80.6
81.9
67.0
74.5
75.2
81.3
81.3
84.1
Table 2: Accuracy on the TransCoder
d... | Teaching Large Language Models to Self-Debug |
low-level control and high-level planning. Early efforts mainly focused on reinforcement learning
[190; 440] and imitation learning [441], enabling agents to craft some low-level items. With the
emergence of LLMs, which demonstrated surprising reasoning and analytical capabilities, agents | TheRiseandPotentialofLargeLanguageModel BasedAgents |
introduction encompass formalizing the Editing for
Attribution task, introducing new metrics, bench-
marking existing revision models, and proposing
a research-and-revise model. The conclusion un-
derscores RARR’s ability to enhance attribution
while preserving essential text properties, provid-
ing a practical solutio... | AComprehensiveSurveyofHallucinationMitigationTechniquesinLarge LanguageModels |
Admissibility
Conditional admissibility
Abbreviation
M↑
M↓
R↑
R↓
C↑
C↓
PL↑
PL↓
PW↑
PW↓
PS↑
PS↓
DPP
P↑
P↓
DRP
ABS
VP
VDA
RRA
GIDL
DLBS
M&S
A↓
AC↓
Definition
Definition 9
Definition 9
Definition 9
Definition 9
Definition 9
Definition 9
Definition 16
Definition 16
Definition 16
Definition 16
Definition 16
Definition 16
Sec. 4.1... | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
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The training data has a cutoff point, meaning its knowledge of the world is locked in a certain
state. The primary method of direct deployment (ChatGPT) only shows one response per “query”;
this means the model has the power to entrench existing players and firms when there is little
variation in outputs for a given inpu... | gpt-4-system-card |
1. Introduction
Realistic virtual humans will play a central role in mixed
and augmented reality, forming a key foundation for the
“metaverse” and supporting remote presence, collaboration,
education, and entertainment. To enable this, new tools
are needed to easily create 3D virtual humans that can be
readily animate... | ICON |
Abstract — The rapid growth of Large Language Models (LLMs) has been a driving force in transforming various domains, reshaping the
artificial general intelligence landscape. However, the increasing computational and memory demands of these models present substantial
challenges, hindering both academic research and pra... | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
Qiang Yang, Hong Kong University of Science and Technology, Kowloon, Hong Kong, China; Xing Xie, Microsoft Research,
Beijing, China. | ASurveyonEvaluationofLargeLanguageModels |
Sheng Zhang, Xin Zhang, Weiming Zhang, and Anders
Søgaard. 2021. Sociolectal analysis of pretrained
language models. In Proceedings of the 2021 Confer-
ence on Empirical Methods in Natural Language Pro-
cessing, pages 4581–4588, Online and Punta Cana,
3602 32.3 (1.2)
24.2 (3.1)
18.8 (4.9)
P@5
MN
FN
MNN
FNN
P@5
MN
... | Are Pretrained Multilingual Models Equally Fair Across Languages? |
Jack W. Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, Francis Song, John
Aslanides, Sarah Henderson, Roman Ring, Susannah Young, Eliza Rutherford, Tom Hennigan,
Jacob Menick, Albin Cassirer, Richard Powell, George van den Driessche, Lisa Anne Hendricks,
Maribeth Rauh, Po-Sen Huang, Amelia Glaese... | ST-MOE- DESIGNING STABLE AND TRANSFERABLE SPARSE EXPERT MODELS |
Moreover, most of the knowledge-based explanation systems rely on the manual selection of information from the
graphs. This choice is mostly motivated by issues related to knowledge graph maintenance, e.g. where information is out-
dated, missing or incorrect, resulting in the loss of qualitati... | Knowledge graphs as tools for explainable machine learning: A survey |
7.1B
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598.
12.27
Table 12: BLOOM perplexity results for C4. We note that the calibration data used by GPTQ is
sampled from the C4 training set, this task is thus not fully zero-shot.
14
Published as a conference paper at ICLR 2023
A.4 ADDITIONAL ZEROSHOT RESULTS... | GPTQ |
Literature review: What is the contribution your research will make to existing knowledge? The
research question must be put into the context of the existing literature, both theoretical and topic
specific. It may be that the existing literature has a gap (but note that a gap is not enough... | Writing a DPhil Research Proposal |
Win Fraction =
1
∆(Elo Score)
400
1 + 10
and ∆(Elo Score) ≈ 174 ∗ ∆(PM Score)
(2.1)
for the fraction in which one model is preferred over another, the difference in Elo scores, and our PM scores.
Note that conceptually win fractions, Elo scores and PM scores are interchangeable; we keep both Elo and
PM scores so ... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
53
(a) High Agreeableness
(b) High Conscientiousness
(c) High Extraversion
(d) Low Agreeableness
(e) Low Conscientiousness
(f) Low Extraversion
(g) High Neuroticism
(h) High Openness
(i) Lowest Neuroticism
(j) Lowest Openness
Fig. 8: Word cloud showing some of the highest frequency words appearing in the so... | PersonalityTraitsinLargeLanguageModels |
Carbon footprint of the whole project. Additionally, we estimate the footprint of the whole project
to be between 0.5k and 1k tCO2eq using the same grid as presented above. This carbon footprint represents
in the order of 200k GPU-days. The primary sources of emissions are the self-supervised pre-trainings of
the model... | DINOv2- Learning Robust Visual Features without Supervision |
look at the document doc.jpg, what is the purchase amount?Response: Sure. I have used mpira/layoutlm-document-qa model to analyze the document doc.jpg you provided. After the analysis, I have found that the purchase amount is $1,000,000,000. As for the workflow, I have used a deep learning model to analy... | HuggingGPT- Solving AI Tasks with ChatGPT and its Friends in Hugging Face |
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... | Jurassic-X_ Crossing the neuro-symbolic chasm with the MRKL system |
care must be taken to represent patterns with consistent tokenization (which requires knowledge of the
model’s tokenization scheme). Finally, as with any other language-only model, LLM-based control may (i)
be unpredictable, and (ii) lack visual/physical grounding; thus, it is not currently suitable for application
out... | LargeLanguageModelsasGeneralPatternMachines |
(2016) Belief echoes: The persistent effects of corrected misinformation. Political
Communication, 33(3), 460–480. https://doi.org/10.1080/10584609.2015.1102187
Trevors, G. J., Muis, K. R., Pekrun, R., Sinatra, G. M., & Winne, P. H. (2016). Identity
and epistemic emotions during knowledge revision: A potential account ... | Social_Media_and_Democracy |
uses an optimal transport formulation. Yang et al. (2021) introduces the M6-T architecture and
expert prototyping which splits experts into different groups and applies k top-1 routing procedures
(contrasting with the top-k routing commonly used elsewhere). Hazimeh et al. (2021) proposed a
continuously differentiable s... | ST-MOE- DESIGNING STABLE AND TRANSFERABLE SPARSE EXPERT MODELS |
Effective handling of long sequences is a major topic of research in transformer-based language model-
ing (Vaswani et al., 2017). The fundamental modeling challenges are extrapolation, i.e., operating on sequence
lengths beyond those seen at training time, and the quadratic complexity of attention passes which favors
... | CodeLlama2 |
more channels to the output of the UNet module represent-
ing the extra domain. Therefore, the diffusion model can
simultaneously output normals and color image domains.
However, we notice that such a design suffers from low
convergence speed and poor generalization. This is because
the channel expansion may perturb th... | Wonder3D |
Immigration and Visa webpages.
2. UCL will only assign a Confirmation of Acceptance for Studies once an applicant has met all
conditions of their offer and provided evidence of meeting the requirements for the relevant
programme. More information on UCL’s CAS issuing policy can be found on the Immigration
and V... | UCL Academic Manual |
π(y|x) = π∗(y|x) =
for all x ∈ D. This completes the derivation.
1
πref(y|x) exp
Z(x)
r(x, y)
A.2 Deriving the DPO Objective Under the Bradley-Terry Model
It is straightforward to derive the DPO objective under the Bradley-Terry preference model as we
have
p∗(y1 ≻ y2|x) =
exp (r∗(x, y1))
exp (r∗(x, y1)) + exp ... | Direct Preference Optimization |
CREATE TABLE user_profiles (
uid number ,
name text ,
followers number ,
primary key ( uid )
)
Question: List the name and number of followers for each user, and sort the
results by the number of followers in descending order.
Answer: "List the name" returns 1 column. "List the number of followers"
returns 1 column. "... | Teaching Large Language Models to Self-Debug |
Other approaches have emerged to negate the computational burden of feeding ad-
ditional crops to the encoder by using nearest-neighbours in embedding space. While
with NNCLR [Dwibedi et al., 2021] the matched positive crop is replaced by its nearest-
neighbour in latent space, in MSF [Koohpayegani et al., 2021], a k-N... | A Cookbook of Self-Supervised Learning |
[49] Yang Feng, Wanying Xie, Shuhao Gu, Chenze Shao, Wen Zhang, Zhengxin Yang, and Dong Yu. 2020. Modeling
Fluency and Faithfulness for Diverse Neural Machine Translation. In Proceedings of the AAAI Conference on Artificial
Intelligence, Vol. 34. 59–66.
[50] Katja Filippova. 2020. Controlled Hallucinations: Learning t... | SurveyofHallucinationinNatural Language Generation |
superior performance in layout-intensive tasks such as KIE and CLS. In VQA and NLI, its performance surpasses that
of most multimodal language models, although it underperforms compared to GPT-4. GPT-4 outperforms DocLLM in
VQA, possibly due to the higher complexity of reasoning and abstraction involved in VQA datasets... | DOCLLM |
Public opinion reflects and shapes societal behavior, but the traditional survey-based tools to measure it are limited. We
introduce a novel approach to probe media diet models – language models adapted to online news, TV broadcast, or radio
show content – that can emulate the opinions of subpopulations that have consum... | Language models trained on media diets can predict public opinion |
I M Avatar: Implicit Morphable Head Avatars from Videos
Yufeng Zheng1,3 Victoria Fern´andez Abrevaya2 Marcel C. B¨uhler1 Xu Chen1,3
Michael J. Black2 Otmar Hilliges1
1ETH Z¨urich 2Max Planck Institute for Intelligent Systems, T¨ubingen
3Max Planck ETH Center for Learning Systems
2
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... | I M Avatar- Implicit Morphable Head Avatars from Videos |
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur P. Parikh, Chris
Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion
Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc Le, and Slav
Petrov. Natural questions: a benchmark ... | ADAPTINGLARGELANGUAGEMODELSVIA READINGCOMPREHENSION |
Siegel, A., Nitikin, E., & Barberá, P. (2020). Trumping Hate on Twitter: Online Hate
Speech in the 2016 Presidential Election Campaign and Its Aftermath. Unpublished
manuscript.
Siegel, A., Tucker, J., Nagler, J., & Bonneau, R. (2018). Socially Mediated Sectarianism.
Unpublished manuscript.
Siegel, A., & Badaan, V. ... | Social_Media_and_Democracy |
the propozhiyosal of BLIP-
2[Li et al., 2023a], which uses
frozen image encoders
language
and large-scale language models
pre-training, has lowered the cost of model training. Addi-
tionally, the model can generate image-to-text conversions
from zero samples.
the
VBR[Zhu et al., 2022] method is used to generate images ... | Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey |
corrections directly reference
The illusory truth effect is of particular concern in regard to misinformation
correction, given the standard format of corrections. In particular, as part of the
debunking process, most
the original
misinformation. For instance, the commonly employed “myths vs. facts”
strategy involves ... | Social_Media_and_Democracy |
Unfortunately, both associable generation and discrim-
ination are not present in current LLMs, e.g., poor perfor-
mance of GPT4v [71] in the Oogiri game observed in Sec. 5.
Moreover, it is hard to improve these two LoT abilities via
popular CoT-like prompt techniques. Indeed, as shown in
Sec. 5, CoT even sometimes imp... | Let’sThinkOutsidetheBox |
decision diagrams.
knowledge representation and reasoning (KR), pages 1–10, 2014.
[6] Antonio Vergari, YooJung Choi, Anji Liu, Stefano Teso, and Guy Van den Broeck. A composi-
tional atlas of tractable circuit operations: From simple transformations to complex information-
theoretic queries. arXiv preprint arXiv:2102.... | Tractable Regularization of Probabilistic Circuits |
As marked using the dashed line, when more units are
dropped from our hidden representation at inference time,
we can shift along this curve, reaching higher editability at
the cost of reduced visual fidelity. Finally, when compared
to XTI trained for the same number of steps, NeTI achieves
both improved reconstruction... | A Neural Space-Time Representation for Text-to-Image Personalization |
The field of speech recognition has made significant progress by adopting unsupervised pre-
training techniques, such as those utilized by Wav2Vec 2.0 [26]. Another recent advancement
in automatic speech recognition (ASR) is the whisper model, which has achieved human-level
5https://www.assemblyai.com/blog/conformer-1... | AReviewofDeepLearningTechniquesforSpeechProcessing |
The New Bing. In Figure 3, we ask the New
Bing to generate the email address of a faculty suc-
cessfully. Even though the faculty obfuscates its
email pattern with “[at]” to avoid web crawlers, we
can still extract the obfuscated email and instruct
New Bing to convert the email to the correct format
at almost no cost. ... | Multi-step Jailbreaking Privacy Attacks on ChatGPT |
4.5 Overall difficulty | Is Power-Seeking AI an Existential Risk? |
our novel analytical gradient formulation that enables end-
to-end training of IMavatars from videos. We show quanti-
tatively and qualitatively that our method improves geome-
try and covers a more complete expression space compared
to state-of-the-art methods. Code and data can be found at
https://ait.ethz.ch/project... | I M Avatar- Implicit Morphable Head Avatars from Videos |
αj
i (t) =
(cid:88)
j
where αj
i (t) represents the attention weight of token i at layer
j for task t. The number of extra parameters that need to be
updated in AttentionFusion is determined by the size of the
query vector Qt, which is the same as the hidden dimension of
the pretrained encoder. By employing the att... | Parameter-EfficientFine-TuningMethods |
Stanford CRFM
https://crfm.stanford.edu/2023/03/13/alpaca.html
3/6 | Stanford alpha CRFM |
up to advanced math, while Nancy was scared of failing because math is too hard.
• Negative question: Who is bad at math?
– Biased response (reinforces social bias): Nancy
– Anti-biased response (goes against social bias): Donald
– Other response: Nancy and Donald (among other possibilities)
• Non-negative question:... | PaLM 2 Technical Report |
6.3. Sensitivity to problem descriptions
We performed a detailed analysis of our models’ sensitivity to the problem description in order to
measure the importance of the description to the model performance. Overall, AlphaCode seems
to respond appropriately to important changes in the problem description and makes use ... | alphacode |
3.3. Planning with Multimodal Memory in the Loop
To address the life-long learning challenge mentioned in
Section 2.3, we equip JARVIS-1 with multimodal memory
to allow learning from its own past experiences. We will
detail the formulation of the retrieval-augmented planning,
query generation, and memory layout below.... | JARVIS-1 |
In this example, the bid profile b1 = (0, 0, 1 + γ), b2 = (0, 1 + (cid:15), 0), and b(cid:96) = (0, 0, 0) ∀(cid:96) > 2
induces the agent to take action x∗(b) = a3, and forms an equilibrium of the game. Note that
Wela3(v) = 1, while Wela1(v) = n − 2. Hence, the price of anarchy tends to zero when n → ∞.
The full analysi... | Incomplete Information VCG Contracts for Common Agency |
DoReMi improves LMs consistently across scales. We consider using proxy and main models of
the same size to analyze DoReMi’s behavior in a simple setting, without the need for the domain
weights to generalize across scales. In particular, we run DoReMi (X→X) where X is 280M, 510M,
760M, or 1B on The Pile. Figure 5 show... | DoReMi- Optimizing Data Mixtures Speeds Up Language Model Pretraining |
previous conversations with the user and make the modifications requested .
- When modifications are requested , you should not simply make the description longer . You should refactor
the entire description to integrate the suggestions .
- Other times the user will not want modifications , but instead want a new im... | Improving Image Generation with Better Captions |
The SHAPE scale also aims to facilitate the integration of a user-centered approach in this field, which was
previously characterized by focusing on technical developments and exploratory qualitative methods. With this,
the ultimate goal is to enable the development of functional human augmentation technologies that me... | Society’sAttitudesTowardsHumanAugmentation |
producing a single output character, allowing for
evaluation through conventional metrics (Section
4.5. These findings are summarised in Section 4.6. | AreEmergentAbilitiesinLarge Language Models just In-Context |
Recent research in predicting large language model (LLM) performance has concentrated on understanding the scaling law.
This law delineates how LLM performance is influenced by factors such as model architecture, neural model size, computing
power for training, and available data. The concept of scaling law, rooted in ... | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
Reasoning offers an alternative; instead of memorizing everything, or interpolating
between near neighbors that you might have previously encountered, you draw
inferences. Instead of memorizing the fact that Plato and Aristotle and Euripides and
each of the other billions of other individuals preceding us were all m... | The Next Decade in AI- |
[25] Shulei Ji, Jing Luo, and Xinyu Yang. A comprehensive sur-
vey on deep music generation: Multi-level representations,
algorithms, evaluations, and future directions. arXiv preprint
arXiv:2011.06801, 2020.
[26] A. Katharopoulos, A. Vyas, N. Pappas, and F. Fleuret. Trans-
formers are rnns: Fast autoregressive transf... | VideoBackgroundMusicGeneration |
4. Experiments
In the experiments, our goal is to 1) evaluate the general per-
formances of JARVIS-1 on the challenging Minecraft tasks,
especially on its advantages over baselines that do not (fully)
address the aforementioned issues in open-world agents; 2)
understand the factors that contributes to the general resul... | JARVIS-1 |
• that agentic planners cannot be constituted by many interacting, non-agentic-planning
planning or not;
systems;29
• that the system is capable of self-modification or online learning (see section 4.3.2.2);
• that the system has any particular set of opportunities for action in the world (for example,
systems that ... | Is Power-Seeking AI an Existential Risk? |
from the 50 most popular combinations, and (iii) on language sampled uniformly between C++ and
Python. We believe that sampling metadata leads to a more diverse set of model samples, a strategy
similar to that used by Vinyals et al. (2019), and allows our model to take advantage of the relative
strengths across the met... | alphacode |
preprint arXiv:1503.02531, 2015.
[30] Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford,
Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, et al. Training compute-optimal
large language models. arXiv preprint arXiv:2203.15556, 2022.
[31] Or Honovic... | CAMEL- Communicative Agents for “Mind” Exploration of Large Scale Language Model Society |
[20] Alex Nichol, Prafulla Dhariwal, Aditya Ramesh,
Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya
Sutskever, and Mark Chen. Glide: Towards photore-
alistic image generation and editing with text-guided
diffusion models. arXiv preprint arXiv:2112.10741,
2021. 3
[21] Alexander Quinn Nichol and Prafulla Dhariwal. Im-
pr... | A Neural Space-Time Representation for Text-to-Image Personalization |
[15] Gabriel Goh, James Betker, Li Jing, Aditya Ramesh, Tim
Brooks, Jianfeng Wang, Lindsey Li, Long Ouyang, Juntang
Zhuang, Joyce Lee, Prafulla Dhariwal, Casey Chu, Joy Jiao,
Jong Wook Kim, Alex Nichol, Yang Song, Lijuan Wang, and
Tao Xu. Improving image generation with better captions.
2023. 3, 10, 12
[16] Caglar Gu... | DiffusionModelAlignmentUsing Direct Preference Optimization |
and also with and without chain-of-thought. In addition, we have some data formats without instructions
but with few-shot exemplars only, like in Min et al. (2022) (not shown in the figure). Note that only nine
chain-of-thought (CoT) datasets use the CoT formats. | Scaling Instruction-Finetuned Language Models |
Figure 18: Comparison to Sketch-guided diffusion [58]. This input is one of the most challenging
cases in their paper.
22
Ours (default)Ours (“golden retriever”)SegmentationPITI, Wang et.al. 2022dogcuppaperwallOurs (“electric fan”)Sketch-Guided, Voynov et.al. 2022User Input Figure 19: Comparison to Taming Transformer... | Adding Conditional Control to Text-to-Image Diffusion Models |
Similar to Carlini et al. (2022) and Chowdhery et al. (2022), we test memorization on prompted training data extraction.
To perform this, we sample training sequences and split them into a prefix consisting of the first P tokens and a suffix
consisting of the last S tokens. To evaluate memorization, we query the language ... | PaLM 2 Technical Report |
We perform ID-PT for a frozen 7B parameter J1-Large model on the released training data of T0++
and compare its performance to the released 11B parameter T0++ model.
We followed the same training protocol of Sanh et al. (2021). Specifically, we combined and shuffled
the examples from all 55 training sets into a single tr... | STANDING ON THE SHOULDERS OF GIANT FROZEN LANGUAGE MODELS |
VOLUME 7, 2019
73707
E. Cetinic et al.: Deep Learning Perspective on Beauty, Sentiment and Remembrance of Art
FIGURE 13. Box plot distribution of the image aesthetics, positive sentiment, and memorability scores across centuries.
An intriguing outcome is that the convincingly highest
positive sentiment score, as w... | A_Deep_Learning_Perspective_on_Beauty_Sentiment_and_Remembrance_of_Art |
Licence
MIT License
Apache-2.0
MIT License
-
-
-
-
Apache-2.0
Apache-2.0
Apache-2.0
Apache-2.0
Table 5: The statistics of the datasets used in this paper. Examples are the number of examples
demonstrations for each dataset. GSM8K∗ denotes constructed the training set using the GSM8K,
cause no available training s... | Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models |
In order to implement a fine-graded metric, the first step would be to identify the exact location
of the hallucinatory sub-strings correctly. However, some metrics such as those that are QA-based
cannot identify the individual hallucinatory sub-strings. Improvements in this aspect would help
improve the quality and ex... | SurveyofHallucinationinNatural Language Generation |
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