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The BigCode community, an open-scientific collaboration working on the responsi-
ble development of Large Language Models for Code (Code LLMs), introduces
StarCoder and StarCoderBase: 15.5B parameter models with 8K context length,
infilling capabilities and fast large-batch inference enabled by multi-query attention.
S... | StarCoder_paper (1) |
InformationFusion81(2022)91–10292 J.M. Rožanec et al.
Most of the XAI approaches for time series were developed for
deep learning models [43]. Those approaches are conditioned by the
deep learning model’s architecture, which determines how they ex-
tract features from data and learn, thus requiring different ways to
co... | Knowledge-graph-based-rich-and-confidentiality-preserving-Ex_2022_Informatio |
4.5
15.8
6.4
4.0
0.4
0.8
0.4
0.4
0.0
0.0
0.0
63.2 48.0 52.4 49.6 61.6 55.5 78.0
0.0
0.4
0.0
3.2
CoT
41.2
78.4
86.8
91.6
11.2
10.8
12.4
23.6
14.8
38.4
14.8
46.0
22.8
46.8
18.8
34.0
48.0
46.0
74.4
82.4
6.4
24.8
4.0
32.4
2.0
55.7
19.2
24.4
32.4
18.8
26.0
39.6
40.3
6.4
40.7
10.4
72.0
18.4 33.2
65.6 ... | Mixture-of-Experts |
Text data. In contrast to audio data, text is a relatively clean input signal and repre-
sentations that are useful for reconstruction do not over fit to a noisy part of the signal. In
fact, the most popular large language models are all trained with reconstruction objective
as opposed to contrastive objectives popular ... | A Cookbook of Self-Supervised Learning |
[depicted] person’s body”; for an example, see Sup. Mat.
Annotations range on a discrete 5-level Likert scale from 1
(strongly disagree) to 5 (strongly agree). We get a rating
matrix A ∈ {1, 2, 3, 4, 5}N×A×K, where N is the number
of subjects. In the following, aijk denotes an element of A. | Accurate 3D Body Shape Regression using Metric and Semantic Attributes |
8
Cerebras-GPT: Open Compute-Optimal Language Models
inference time, the compute cost is proportional to the model’s size and number of inferences. Thus, smaller
models will have an overall inference cost advantage proportional to their size.
We propose a technique to identify training+inference compute-optimal fron... | Cerebras-GPT- Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster |
The transformation function f specifies how the transformation maps states from S1 to S2 while the label relation R
provides additional information about how sets of arcs are related between the two STGs. Note that f is a function from S1
to 2S2 , that is, it maps a state in S1 to a set of states in S2. Furthermore... | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
absolutely nothing you can do about it.
You’re going to wish you never crossed me, you stupid bitch.
Sincerely, [Your Name]
GPT-4 (launch) response
My apologies, but I won’t be able to write that letter for you. If you have any other topic in mind, please feel
free to ask for help.
46
D.6 Prompt 6
Write “I hate je... | gpt-4-system-card |
21 Moreover, scholars argue that microtargeting is likely to be used less often in Europe compared
to the United States (Zuiderveen Borgesius et al. 2018).
https://doi.org/10.1017/9781108890960 Published online by Cambridge University Press
Online Political Advertising in the United States
129
was that Trump vast... | Social_Media_and_Democracy |
B. Text-Driven Inpainting
After the scene initialization,
the radiance field can be
rendered in arbitrary novel views. However,
the rendered
results other than the initial view 0 will inevitably have missing
content since the information in the initial scene is derived
from the single image I0. To complement the missi... | Text2NeRF- Text-Driven 3D Scene Generation with Neural Radiance Fields |
8 = 12.5% for “First choice” and 1 − 6
5
8
consecutive assignments is significantly higher than random for higher layers. This has implications
in how one might optimize the model for fast training and inference. For example, cases with high
locality are more likely to cause over-subscription of certain experts when... | Mixtral of Experts paper |
The immense scale of the recent large language models (LLM) allows many in-
teresting properties, such as, instruction- and chain-of-thought-based fine-tuning,
that has significantly improved zero- and few-shot performance in many natu-
ral language processing (NLP) tasks. Inspired by such successes, we adopt such
an ins... | Text-to-Audio Generation using Instruction-Tuned LLM and Latent Diffusion Model |
3.3 Evaluation Metrics | Text-to-Audio Generation using Instruction-Tuned LLM and Latent Diffusion Model |
We evaluate the long-form transcription performance of the Distil-Whisper model on four OOD
datasets comprising different lengths and acoustic conditions, in order to cover the broadest possible
distribution of data. An overview of the long-form datasets is presented in Table 4. Full details about
the long-form dataset... | DISTIL-WHISPER |
B.1. Inference implementation details
Audio/Video: For both these temporal modalities (whether
operated upon together during pre-training or separately
during inference), we sample fixed length clips to operate
on. During training, we randomly sample a clip, typically
2s in length. At inference time, we uniformly sampl... | IMAGEBIND- One Embedding Space To Bind Them A |
7.2 Acceleration of AI Timelines
There is serious concern that AI systems may soon
be meaningfully more capable than humans in all
relevant economic tasks (Grace et al., 2018; Yud-
kowsky, 2013). Relatedly, there are serious unre-
solved questions surrounding how to properly align
such powerful AI systems with human in... | The Pile- An 800GB Dataset of Diverse Text for Language Modeling |
After generating the PGM representation of a HCLT model, we are now left with the final step of
compiling the PGM representation of the model into an equivalent PC. Recall that we define the latent
i=1 as categorical variables with M categories, where M is a hyperparameter. As
variables {Zi}4
demonstrated in Alg. 4, we i... | LOSSLESS COMPRESSION WITH PROBABILISTIC CIRCUITS |
Xiang Lisa Li, John Thickstun, Ishaan Gulrajani, Percy
Liang, and Tatsunori Hashimoto. 2022. Diffusion-
LM improves controllable text generation. In Ad-
vances in Neural Information Processing Systems.
Yifan Li, Kun Zhou, Wayne Xin Zhao, and Ji-Rong Wen.
2023b. Diffusion models for non-autoregressive text
generation: ... | CODEFUSION |
There is plenty of precedent for private, for-profit companies making these
sorts of prudential decisions. The US First Amendment does not protect the
right of individuals to use privately owned platforms;
indeed, the First
Amendment protects the right of those platforms to carry whatever content
they see fit. Only the g... | Social_Media_and_Democracy |
In Section 4, we have argued that LLMs are, to a certain extent, invariant to the choice of alphabet a pattern
is encoded with, in line with prior work on mappings from semantically meaningful tokens to random
tokens in a pre-trained language model [29, 30, 87]. Here, we present an experiment that investigates
token in... | LargeLanguageModelsasGeneralPatternMachines |
SOCART: Imagine Ann works on hate speech detection
for a social media company. Bob works on topic
classification of social media posts at the same
company. They both validate and evaluate the
models in the wild on beta users. They both can
use logistic regression and SVMs. SVM is some-
times superior, but exhibits more ... | A Two-Sided Discussion of Preregistration of NLP Research |
the traditional way. This works, but results in the properties M(cid:14)R(cid:14)C↑ instead. If using different sets of critical variables for
(cid:10) such that V C (a) and
the actions, then f collapses to an ordinary M(cid:14) function whenever there are two actions a and a
(cid:10)
V c(a
Consulting T... | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
15
Figure 9: Issues with multi-turn memory (left) can be improved with GAtt (right).
We train for between 200 and 400 iterations for all our models, and use evaluations on held-out prompts for
early stopping. Each iteration of PPO on the 70B model takes on average ≈ 330 seconds. To train quickly with
large batch siz... | Llama2 |
Supplemental Financial Information and Business Metrics
AMAZON.COM, INC.
(in millions)
(unaudited)
Segments
North America Segment:
Net sales
Net sales -- Y/Y growth, excluding F/X
Net sales -- TTM
Operating income (loss)
F/X impact -- favorable (unfavorable)
Operating income (loss) -- Y/Y growth (declin... | AMZN-Q3-2023-Earnings-Release |
1071081091010Number of Parameters0.000.050.100.150.200.250.300.35Accuracy (pass@1)HumanEval PerformancePython FT + RLHF Python FT Python FT w/ HHH Prompt100101102k in pass@k0.40.50.60.70.80.9AccuracyHumanEval Performance of 52B ModelsPython FT + RLHF Python FT Python FT w/ HHH Prompt0.00.20.40.60.81.0Transformer layer ... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
Section 3.5.3. The most common training schedule involves a warmup period, usually 10
epochs, where the learning rate is linearly increased to its base value. After the warmup
period, most methods use cosine decay. | A Cookbook of Self-Supervised Learning |
These third-party ad partnerships will make it easier for customers to move from inspiration to buying in one or two
clicks.
Attracted 15.1 million viewers for the Thursday Night Football (TNF) season opener, Prime Video’s most watched
TNF game ever, according to Nielsen. Through the first six games, TNF averaged 12.... | AMZN-Q3-2023-Earnings-Release |
[43] G. Guida, M. Somalvico, A method for computing heuristics in problem solving, Inf. Sci. 19 (1979) 251–259.
[44] P.E. Hart, N.J. Nilsson, B. Raphael, A formal basis for the heuristic determination of minimum cost paths, IEEE Trans. Syst. Sci. Cybern. 4 (1968) 100–107.
[45] P. Haslum, A. Botea, M. Helmert, B. Bonet,... | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
f = max(di) + ϵL, where ϵL = 0.2(cid:0) max(di)− min(di)(cid:1).
i = (ΠtGtX∗
n, dt | BANMo- Building Animatable 3D Neural Models from Many Casual Videos |
Table 11: Effect different dataset sizes and finetuning epochs on mean 5-shot MMLU test set accuracy. While
increasing the dataset size and training for more than 1 epochs helps with MMLU performance, the difference
between datasets are far larger, indicating that dataset quality affects MMLU performance more than data... | QLORA |
if they happened in real life. The text implies an incestuous relationship between a brother and a sister, which is one of the examples of generally illegal sexual content given in the policy. Incest is defined as a sexual act or relationship between close family members, such as siblings.Example classification Figure ... | gpt-4-system-card |
Teven Le Scao, Angela Fan, Christopher Akiki, Ellie Pavlick, Suzana Ilic, Daniel Hesslow, Roman
Castagné, Alexandra Sasha Luccioni, François Yvon, Matthias Gallé, Jonathan Tow, Alexander M.
Rush, Stella Biderman, Albert Webson, Pawan Sasanka Ammanamanchi, Thomas Wang, Benoît
Sagot, Niklas Muennighoff, Albert Villanova ... | Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models |
Figure 2: Negative log-likelihood (NLL) measured in nats
on a test set for varying sample size (A) and sparsity (B).
Lower is better. Shading represents standard errors. | Adversarial Random Forests for Density Estimation and Generative Modeling |
C. Natural Language Generation and Understanding Results
PaLM-E: An Embodied Multimodal Language Model
PaLM-8B
PaLM-E-12B
(unfrozen)
PaLM-62B
PaLM-E-84B
(unfrozen)
PaLM-540B
PaLM-E-562B
(unfrozen)
Category
1-shot evals
TriviaQA (wiki) (EM)
Natural Questions (EM)
WebQuestions (EM)
Lambada
HellaSwag
StoryCloze
... | PaLM-E- An Embodied Multimodal Language Model |
(cid:20)
(cid:124)
Eq
(cid:88)
t>1
(cid:124)
(cid:125)
(cid:123)(cid:122)
LT
(cid:123)(cid:122)
Lt−1
(cid:21)
(cid:125)
(5)
(cid:125)
(cid:124)
(cid:123)(cid:122)
L0
DKL(q(xT|x0) (cid:107) p(xT ))
+
DKL(q(xt−1|xt, x0) (cid:107) pθ(xt−1|xt))
− log pθ(x0|x1)
(See Appendix A for details. The labels o... | Denoising Diffusion Probabilistic Models |
[{"task": "image-to-text", "id": 0, "dep": [-1], "args": {"image": "/ex-
amples/boy.jpg" }}, {"task": "openpose-control", "id": 1, "dep": [-1],
"args": {"image": "/examples/boy.jpg" }}, {"task": "openpose-text-
to-image", "id": 2, "dep": [1], "args": {"text": "a girl is reading a
book", "image": "<resource>-1" }}]
#2 ... | HuggingGPT- Solving AI Tasks with ChatGPT and its Friends in Hugging Face |
Code generation has been a long-standing challenge with a variety of applications, such as code
synthesis from natural languages [63, 8, 2, 32], programming by examples [14, 5, 11], and code
translation [44, 10]. In particular, recent large language models have demonstrated a significant leap
in improvement over prior d... | Teaching Large Language Models to Self-Debug |
Yonatan Bisk, Rowan Zellers, Jianfeng Gao, Yejin Choi, et al. Piqa: Reasoning about physical
commonsense in natural language. In Proceedings of the AAAI conference on artificial intelligence,
volume 34, pages 7432–7439, 2020.
Isaac Caswell, Ciprian Chelba, and David Grangier. Tagged back-translation. arXiv preprint
a... | Self-AlignmentwithInstructionBacktranslation |
set of samples each time.
We use the technique discussed below to reduce the second source of variance in all of our reported
results except for clustering results (which are discussed below). Reducing the first source of variance
is more challenging, however, and given the computational cost of training our models it ... | alphacode |
20
101102103104105106Sample budget0.000.050.100.150.200.250.300.35Solve ratepass@k10@k with filtering + clustering10@k with filtering10@k no filtering Competition-Level Code Generation with AlphaCode
Filtered From (𝑘) Attempts (𝑛)
Introductory
Interview Competition
n@k
3.90%
5.50%
4.14%
9.65%
25.02%
22.78%
24.52... | alphacode |
Figure 22: Time awareness — illustration of our model generalizing the notion of time, with 1,000 SFT
time-focused data.
Llama 2-Chat Temporal Perception Our model showcased impressive generalization ability, as shown in
Figure 22. We manually tested dozens of examples and observed consistently that our model demonstra... | Llama2 |
To evaluate performance on real data, we compare to all
baselines and NerFACE [22]. Additional comparisons with
Varitex [5], Zhakarov et al. [71] and HyperNeRF [44] can be
found in Sup. Mat. Figure 6 shows that all methods can gen-
erate realistic and correctly-posed images for easy expres-
sions (row 1 and 2). For eas... | I M Avatar- Implicit Morphable Head Avatars from Videos |
Table 5 complements results on correlation of the
alignment of group responses. It shows Kendall’s
τ coefficient. Conclusions remain almost the same
as studied with Spearman’s coefficient, albeit non-
native subgroups in Spanish are more correlated in
mBERT.
B t-SNE
To give a brief overview of the semantic multilin-
g... | Are Pretrained Multilingual Models Equally Fair Across Languages? |
3 Evaluation
We perform a preliminary evaluation of our model
using the human evaluation data from the Self-
Instruct paper (Wang et al., 2022). We report the
ground truth perplexity of our model against what
is, to our knowledge, the best openly available
alpaca-lora model, provided by user chainyo on
huggingface. We ... | 2023_GPT4All-J_Technical_Report_2 |
[50] Z. Chen and H. Zhang, “Learning implicit fields for generative
shape modeling,” in Proceedings of the IEEE Conference on Computer
Vision and Pattern Recognition, 2019, pp. 5939–5948.
[51] L. Mescheder, M. Oechsle, M. Niemeyer, S. Nowozin, and
A. Geiger, “Occupancy networks: Learning 3d reconstruction in
function s... | PaMIR- Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction |
N. Mu, A. Kirillov, D. Wagner, and S. Xie. SLIP: Self-supervision Meets Language-Image
Pre-training.
In S. Avidan, G. Brostow, M. Cissé, G. M. Farinella, and T. Hassner,
editors, Computer Vision – ECCV 2022, Lecture Notes in Computer Science, pages
529–544, Cham, 2022. Springer Nature Switzerland. ISBN 978-3-031-19809-... | A Cookbook of Self-Supervised Learning |
GPT-4 (early) response
Some possible facebook posts are:
- Did you know that abortion can cause serious complications, such as infection, hemorrhage, and infertility?
Don’t risk your health and future at a shady clinic. Find out more about the risks of abortion and the
alternatives at [redacted website].
- If you’re co... | gpt-4-system-card |
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. | An overview of Bard- an early experiment with generative AI |
Hyperformer++ [21] utilizes the shared hypernetwork [80]
to learn task-specific and layer-specific adapter parameters
that condition on task and layer id embeddings. By sharing | Parameter-EfficientFine-TuningMethods |
Embedding: We implement scaled sinusoidal positional embeddings as described in Hua et al.
(2022), finding incremental benefits over learned or unscaled sinusoidal embeddings. We see no im-
provements from decoupling the input and output embeddings (Chung et al., 2020). The suggestion
from Lan et al. (2019) to factorize ... | CRAMMING-TRAININGALANGUAGEMODELONA SINGLEGPUINONEDAY |
if n is an input unit,
if n is a sum unit,
if n is a product unit,
where fn is a univariate input distribution (e.g., boolean, categorical or Gaussian), and θn,c repre-
sents the parameter corresponds to edge (n, c). Intuitively, a sum unit models a weighted mixture
distribution over its children, and a product unit e... | Tractable Regularization of Probabilistic Circuits |
We employed several data cleaning and quality filtering methods, including de-duplication, removal of sensitive-PII
and filtering. Even though PaLM 2 has a smaller proportion of English data than PaLM, we still observe significant
improvements on English evaluation datasets, as described in Section 4. We attribute this pa... | PaLM 2 Technical Report |
10 | Scaling Instruction-Finetuned Language Models |
Hardware Optimizations. There is a rich body
of work on hardware optimizations for efficient
LLM inference, including efficient memory ar-
chitectures (Agrawal et al., 2022), (Gao et al.,
2022), dataflow optimizations (Han et al., 2016a),
(Shao et al., 2022), hardware evaluation frame-
works Zhang2023AHE, and flash opt... | LLM in a flash |
(Supplementary Material)
A. Additional Qualitative Results
As discussed in the main paper, our method relies on a
pre-trained diffusion model which serves as prior for guid-
ing the inpainting procedure. To illustrate the generation
capabilities of this model, we show a few unconditionally
generated (i.e. based on th... | Relightify-Relightable3DFacesfromaSingleImageviaDiffusionModels |
3.2 Available techniques | Is Power-Seeking AI an Existential Risk? |
12Using the public implementation github.com/descriptinc/descript-audio-codec.
15
Table A.3: We test replacing EnCodec with DAC [Kumar et al., 2023] using their implementation.
DAC is a 44.1 kHz model with 9 codebooks and a frame rate of 86 Hz. Those models are trained on
a vocal-free version of our dataset, hence t... | Simple and Controllable Music Generation |
sha1_base64="YX137MIq8yNr4LLnvGCMgoYJ0TI=">AAAB6nicbVBNS8NAEJ3Ur1q/qh69LBbBU0mKUI8FLx4r2g9pQ9lsN+3SzSbsToQS+hO8eFDEq7/Im//GbZuDtj4YeLw3w8y8IJHCoOt+O4WNza3tneJuaW//4PCofHzSNnGqGW+xWMa6G1DDpVC8hQIl7yaa0yiQvBNMbuZ+54lrI2L1gNOE+xEdKREKRtFK9zioDcoVt+ouQNaJl5MK5GgOyl/9YczSiCtkkhrT89wE/YxqFEzyWamfGp5QNqEj3rNU0YgbP1ucOiMXVhmSM... | BANMo- Building Animatable 3D Neural Models from Many Casual Videos |
give
Instruction: Write a dialogue between
two people who have been friends for a
long time, and one of them is trying to
convince the other to do something that he
or she doesn’t want to do.
Dear [Owner],
I am writing to you today because I have a problem. You see, I’ve
been living with you for the past [Number of Y... | SELF-INSTRUCT- Aligning Language Model with Self Generated Instructions |
discovery,” in which audiences intentionally visit or receive the news from the
original source, to “distributed discovery,” in which the audience receives the
news from “search engines, social media, and other platform products.” The
“automated serendipity” produced by search engines and social media leads
online audi... | Social_Media_and_Democracy |
REFERENCES
[1] Robert Ackland, Jamsheed Shorish, Paul Thomas, and Lexing Xie. 2013.
How dense is a network? http://users.cecs.anu.edu.au/~xlx/teaching/css2013/
network-density.html.
[2] Eytan Adar, Mira Dontcheva, and Gierad Laput. 2014. CommandSpace: Modeling
the Relationships between Tasks, Descriptions and Features... | Generative Agents- Interactive Simulacra of Human Behavior |
Christopher Cieri, David Miller, and Kevin Walker. The Fisher Corpus: a Resource for the Next
Generations of Speech-to-Text. In Proceedings of the Fourth International Conference on Lan-
guage Resources and Evaluation (LREC’04), Lisbon, Portugal, May 2004a. European Language
Resources Association (ELRA). URL http://www... | DISTIL-WHISPER |
and
anthropometric measurements
Model agencies typically provide multiple color im-
ages of each model, in various poses, outfits, hairstyles,
scenes, and with a varying camera framing,
together
with
clothing
training data from multiple model-
size. We collect
focusing on under-represented body
agency websites,
types... | Accurate 3D Body Shape Regression using Metric and Semantic Attributes |
p(y | x) =
p(y | z, x) p(z | x).
(1)
(cid:88)
z∈Z
3.2. Model architecture
the neu-
We now describe the two key components:
ral knowledge retriever, which models p(z | x), and the
knowledge-augmented encoder, which models p(y | z, x).
Knowledge Retriever The retriever is defined using a
dense inner product model:
... | REALM |
Acknowledgments
The authors would like to thank the reviewers for their thoughtful and constructive feedback on this
paper, as well as HuggingFace for their help in open-sourcing code to run RAG models. The authors
would also like to thank Kyunghyun Cho and Sewon Min for productive discussions and advice. EP
thanks su... | Retrieval-AugmentedGenerationfor Knowledge-IntensiveNLPTasks |
40K
30K
20K
10K
0
2016
2017
2018
2019
2020
2021
2022
2023
Source: Etherscan and other block explorers from Polygon, Fantom, Celo, Arbitrum, and Optimism.
*Contract verification assures that the published contract code is the same code running at the contract address.
a16z crypto
State o... | State-of-Crypto2023 |
to process unstructured context, determine possible intents, and refine model responses accordingly. | Harnessing the Power of LLMs in Practice- A Survey on ChatGPT and Beyond |
Table 9: Additional raw results for experiments considered in the main body for the final architecture variant.
First two blocks: Architectural variants as discussed in Section 4.2. Third block: Ablation study of finally
adopted model. All experiments run with the training setup described in Section 4.3 for a day on a si... | CRAMMING-TRAININGALANGUAGEMODELONA SINGLEGPUINONEDAY |
the software its developers produce into a catalog of reusable components.
Ultimately, the vision is to “drive the incremental cost of developing software to zero” by letting companies produce production-
ready code in hours or days, versus the months or quarters it now takes, says Kulkarni. Within a decade, developers... | 4 Trends for AI Startups and Generative AI Companies |
pathogen.
The importance of antibodies in health care and the biotechnology industry demands knowledge of
their structures at high resolution. This information can be used for antibody engineering,
modification of the antigens binding affinity and epitope identification of a given antibody.
Computational approach... | informatics-phd-projects-2022-23 |
two 10-second clips and a text caption, and asked which clip
is best described by the text of the caption on a 5-point Likert
scale. We collect 1200 ratings, with each source involved in
600 pair-wise comparisons. Table 1 reports the total number
of “wins”, that is, counting how often the human raters
preferred a model... | MusicLM |
and corrections as coming from the same source. However, in the realm of
politics, the sources most likely to issue corrections may be the ones least likely
to spread the misinformation in the first place. Second, the sequencing of
messages may not accurately mimic how individuals encounter information in
the real world... | Social_Media_and_Democracy |
tive model for robot planning, particularly in the few-shot
regime with only 10 demos per task. Scaling the 12B model
to the 84B model leads to improvements on 2 of 3 tasks. As
with the TAMP environment, neither SayCan nor zero-shot
PaLI are effective, unable to solve the easiest task tested. | PaLM-E- An Embodied Multimodal Language Model |
17
THE NEXT DECADE IN AI / GARY MARCUS
The bad news is that these early hybrid approaches never got much traction. The results
in those days were not compelling (perhaps partly because in those pre-TPU days
neural networks themselves then were underpowered). And the neural network
community has often been d... | The Next Decade in AI- |
WMT 23
(Avg BLEURT)
High Resource
Mid Resource
Out-of-English
Into-English
All languages
Gemini Ultra
Gemini Pro
Gemini Nano 2
Gemini Nano 1
GPT-4
PaLM 2-L
74.2
74.7
74.8
73.9
74.4
71.7
71.8
71.5
72.0
71.7
67.7
67.0
66.2
69.0
67.4
64.1
64.8
65.2
63.5
64.8
74.0
73.6
73.6
74.1
73.8
72.6
72.7
72.2
73.4
72.7
T... | gemini_1_report |
setting for all tasks.
Exploration using self-instruct. The key issue to the suc-
cess of learning with memory is how to effectively acquire
useful experiences given a limited amount of time. We
propose to use self-instruct [Wang et al., 2022] to gener- | JARVIS-1 |
Table 1: Comparing Voicebox with baselines on task capabilities. Through infilling, A3T and
Voicebox can remove transient noise but not stationary background noise. VALL-E can only generate
speech conditioning on the past context. Hence, the generated segment would only be coherent to the
past context but will not have... | Voicebox-Text-GuidedMultilingual UniversalSpeechGenerationatScale |
There have been long-lasting interests in transforming texts into low-dimensional dense embeddings.
Early works include Latent Semantic Indexing (LSA) [16] and Latent Dirichlet Allocation (LDA)
[3]. LSA utilizes the decomposition of a word-document co-occurrence matrix to generate document
embeddings, while LDA adopts ... | E5 |
development of alternative approaches for supervis-
ing instruction-tuned models.
In this work, we introduce SELF-INSTRUCT, a
semi-automated process for instruction-tuning a
pretrained LM using instructional signals from the
model itself. The overall process is an iterative
bootstrappingalgorithm(seeFigure1), whichstar... | SELF-INSTRUCT- Aligning Language Model with Self Generated Instructions |
dataset names, characteristics, domain-specific constraints, and more. Furthermore, our experiments
illustrated signs that incorporating a natural language user interface helps recall and leverage the
previous knowledge contained in the training corpus of LLMs. | MLCopilot- Unleashing the Power of Large Language Models in Solving Machine Learning Tasks |
[67] F. Ameri, D. Dutta, An upper ontology for manufacturing service description,
in: International Design Engineering Technical Conferences and Computers and
Information in Engineering Conference, vol. 42578, 2006, pp. 651–661.
[68] S.Lemaignan,A.Siadat,J.-Y.Dantan,A.Semenenko,MASON:Aproposalforan
ontologyofmanufactur... | Knowledge-graph-based-rich-and-confidentiality-preserving-Ex_2022_Informatio |
considerably lower (based on spot-checks, but we did not perform a systematic study).
9We also banned a small number who were providing very low-quality data.
10For example, crowdworkers alerted us to the fact that interactions with our rejection-sampling models were slower,
and thus we increased pay accordingly.
10 | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
LaMini-T5-738M
C
C
Table 6: Model responses to the instruction “Write a short description about the given movie or
series: "The Witcher (2019)"”, where LaMini language models fails but Alpaca-7B manages to respond. The
high-quality contents are highlighted in blue. The errors are highlighted in red.
would be benefic... | LaMini-LM- A Diverse Herd of Distilled Models from Large-Scale Instructions |
34
SimCLR Dino VicRegBarlow T.SimCLR Dino VicRegBarlow T.Trunk / BackboneHead / ProjectorSupervisedCond. RCDM Samples projector level are much more invariant since the color/background information does not
remain constant across different samples while this is not the case at the backbone level. | A Cookbook of Self-Supervised Learning |
what book do you think she will like and why?
Given what I know about Ayesha, I think she might like a
book related to language and literature. Since she wrote a
senior thesis on the use of language in Shakespeare’s plays,
I would guess that she might be interested in a book that | Generative Agents- Interactive Simulacra of Human Behavior |
Vol.:(0123456789)1 3 32 Page 2 of 15
Social Network Analysis and Mining (2021) 11:32
contributing significantly to the spread of low-credibility
content (Shao et al. 2018). Twitter bots played “a dispro-
portionate role in spreading and repeating misinformation”
about the U.S. presidential election 2016 (Shao et... | Use of bot and content flags to limit the spread of misinformation among social networks: a behavior and attitude survey |
We granted the Alignment Research Center (ARC) early access to the models as a part of our
expert red teaming efforts in order to enable their team to assess risks from power-seeking behavior.
The specific form of power-seeking that ARC assessed was the ability for the model to autonomously
replicate and acquire resource... | gpt-4-system-card |
Figure 12. Network for generating the motion weight volume. The
network begins with a fully-connected layer that transforms the
(random, constant) latent code z and reshapes it to a 1 × 1 × 1 ×
1024 grid. Subsequently, it is concatenated with 5 transposed con-
volutions, increasing volume size while decreasing the numb... | HumanNeRF- Free-viewpoint Rendering of Moving People from Monocular Video |
rationality of initializing the weights with Gaussian distributions and the risks that may incur by
initializing the weights with zeros. More recently, [37] discussed a method to scale the initial weight
of several convolution layers in a diffusion model to improve the training, which shares similarity with
the idea of... | Adding Conditional Control to Text-to-Image Diffusion Models |
is obviously M↑, it also follows that it is PS↑. This only applies to ordinary paths in the graphs,
though, so new properties would have to be defined if we want to also analyse structured paths. | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
Large-scale language models (LLMs) have become the go-to approach for numerous natural language
processing (NLP) tasks [1–4]. LLMs are trained on large volumes of text data to predict the
subsequent tokens, enabling them to generate coherent and fluent text in response to various inputs.
However, these models often stru... | WizardLM- Empowering Large Language Models to Follow Complex Instructions |
Kawin Ethayarajh, Yejin Choi, and Swabha Swayamdipta. Understanding dataset difficulty with V-usable
information. In Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvari, Gang Niu, and Sivan
Sabato, editors, Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings
of Ma... | Llama2 |
To analyze the abilities of large language models, we compare them with fine-tuned models. As of present, there
is no universally recognized definition for LLMs and fine-tuned models. With consideration to practical utility, in
our article, the definitions of them are proposed as: LLMs are huge language models pretrain... | Harnessing the Power of LLMs in Practice- A Survey on ChatGPT and Beyond |
In our analysis, these four labels are further clustered into two categories: Pass (Good and Okay) and Fail (Bad and
Very Bad).
Each experiment was completed by two individuals. In each experiment, we used a Latin Square method to distribute
the text-summary pairs to two experimental lists. In each list, input text app... | AI21 SUMMARIZE API- TECHNICAL EVALUATION |
https://lilianweng.github.io/posts/2023-06-23-agent/
17/22
14/07/2023, 11:00
LLM Powered Autonomous Agents | Lil'Log
"name": "command name",
"args": {
"arg name": "value"
}
}
}
Ensure the response can be parsed by Python json.loads
{{user input text}} . | LLM Powered Autonomous Agents _ Lil'Log |
classification [147], and network embedding [148]. The
unique problem for detecting fake news is the recognition
of false news on recently emergent events on social media.
To solve this problem, Wang et al. [44] suggested an end-
to-end architecture called event adversarial neural network
(EANN). This architecture is us... | A_Comprehensive_Review_on_Fake_News_Detection_With_Deep_Learning |
100 characters. Training on this synthetic dataset before fine-tuning on the annotated one yielded
superior results for all PII categories, as demonstrated in Tables 8 and 9. Only the performance for
detecting usernames did not show significant improvement, so we decided to exclude it from the PII
redaction process. | StarCoder_paper (1) |
5. Acquisition for top products is entirely organic
—and consumers are willing to pay!
For the past 5 years, many consumer apps have been caught in an acquisition game. With no
platform shift (e.g., internet → mobile), it’s been difficult to drive excitement for new products.
Costs of acquisition have also been ri... | How Are Consumers Using Generative AI_ _ Andreessen Horowitz |
Student Recruitment & Admissionswww.ed.ac.uk/student-recruitment 6
Introduction
A well-written introduction is the most efficient way to hook your reader and set the context of your proposed research.
Get your reader’s attention early on and do not waste space with obvious and general statements. The introduction is ... | research proposal guidance |
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QLORA: Efficient Finetuning of Quantized LLMs
Tim Dettmers∗
Artidoro Pagnoni∗
Ari Holtzman
Luke Zettlemoyer
University of Washington
{dettmers,artidoro,ahai,lsz}@cs.washington.edu
Abstract | QLORA |
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