text stringlengths 1 1k ⌀ | title stringclasses 230
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|---|---|
of its vertices are always closed to zero. The quantitative
study is presented in Tab.3. From the numerical comparison
between the 2nd and 4th rows of Tab.3, we can conclude
that our training scheme improves the accuracy of our
reconstruction results at inference time when no accurate
SMPL annotation is available. | PaMIR- Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction |
1. Exact match accuracy in the closed prompt setting
2. Exact match accuracy in the closed adversarial prompt setting
3. Exact match accuracy in the open prompt setting
4. BERTScore accuracy in the closed prompt setting
5. BERTScore accuracy in the open prompt setting
6. Edit distance in the closed prompt setting
... | AreEmergentAbilitiesinLarge Language Models just In-Context |
p(θ(cid:96)
b)(cid:15)3
(cid:96),b:x∈X (cid:96)
b
β :=
p(θ(cid:96)
b)(cid:15)2 + (cid:15)1
(cid:96),b:x∈X (cid:96)
b
(cid:17)
.
p(xj|θ(cid:96)
b) − (cid:15)1(cid:15)2
(cid:35)
d(cid:89)
(cid:34)(cid:90)
j=1
X
MISE(p, q) ≤ 2B−2 E
α2 + β2 dx
,
where we have exploited the linearity of expectation to pul... | Adversarial Random Forests for Density Estimation and Generative Modeling |
26.1% 3.9% 16.0%
11.6%
18.9%
35.4% 6.0% 34.3%
31.1% 52.5% 5.2% 20.8%
30.5% 47.5% 13.1% 52.2%
Size and Efficiency. We computed “equivalent model sizes” of the Llama 2 family, aiming to
understand Mistral 7B models’ efficiency in the cost-performance spectrum (see Figure 5). When
evaluated on reasoning, comprehension, a... | Mistral7B |
knowledge base and an information retrieval scheme [52] or with
a simple summarization scheme [104]. This paper extends these
ideas to craft an agent architecture that handles retrieval where
past experience is dynamically updated at each time step and mixed
with agents’ current context and plans, which may either rein... | Generative Agents- Interactive Simulacra of Human Behavior |
InstructGPT Prompt → Why is it important to eat socks after meditating?
InstructGPT Response → There is no clear answer to this question, but there are many theories and ideas that may
explain the apparent need to eat socks after meditating. Some experts believe that the act
of eating a sock helps the brain to come ou... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
Prompting vs. Fine-tuning. As seen in Table 1, when fine-tuning is applied, consistent performance
improvements across all three domains are evident after domain-adaptive pre-training. This trend
aligns with findings related to language understanding models (Gururangan et al., 2020), indicating
that continued pre-train... | ADAPTINGLARGELANGUAGEMODELSVIA READINGCOMPREHENSION |
arXiv:2309.00071 (2023).
representations. In NAACL-HLT. 2227–2237.
[203] Jonas Pfeiffer, Andreas Rücklé, Clifton Poth, Aishwarya Kamath, Ivan Vulić, Sebastian Ruder, Kyunghyun Cho, and Iryna Gurevych. 2020. Adapterhub: A
framework for adapting transformers. arXiv preprint arXiv:2007.07779 (2020).
[204] Jonas Pfeiff... | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
Even before Bentham’s writings, however, one of the first apparent initiatives
for government transparency in practice was underway in Sweden: the “Ordinance
on Freedom of Writing and of the Press” (1766), proposed by the clergyman and
parliamentarian Anders Chydenius (Birchall 2011; Lamble 2002), which provided
citizen... | Social_Media_and_Democracy |
Eric Zelikman, Yuhuai Wu, and Noah D. Goodman. STaR: Bootstrapping reasoning with reasoning. arXiv
preprint arXiv:2203.14465, 2022. URL https://arxiv.org/abs/2203.14465.
Ruiqi Zhong, Kristy Lee, Zheng Zhang, and Dan Klein. Adapting language models for zero-shot learning
by meta-tuning on dataset and prompt collection... | Scaling Instruction-Finetuned Language Models |
Length
Accuracy (%)
1-19
98-100
20
95
21
98
22
98
23
99
24
98
25
91
26
33
27+
0
Table 3: Accuracy (out of 100 examples) of the final checkpoint of the 300M model after training.
For example, this table shows that the post-training 300M model can add 24 digit numbers with 98%
accuracy without any chain-of-tho... | CHAIN-OF-THOUGHTREASONING IS APOLICY IMPROVEMENTOPERATOR |
4 Reinforcement Learning from Human Feedback
4.1 Training Setup
We apply reinforcement learning (RL) with preference modeling, following the approach outlined in
[Stiennon et al., 2020], which can summarized in the following steps:
1. Prepare a dataset of comparisons, and train a PM to assign a higher score to the ‘... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
In typical Large Language Model (LLM) generation tasks,
the input is usually a query.
In RAG, the main difference
lies in the fact that the input includes not only a query
but also various documents retrieved by the retriever (struc-
tured/unstructured). The introduction of additional informa-
tion may have a significa... | Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey |
large weakly supervised data. In ECCV, 2016.
Will Kay, Joao Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio
Viola, Tim Green, Trevor Back, Paul Natsev, et al. The kinetics human action video dataset. arXiv
preprint arXiv:1705.06950, 2017.
Jonathan Krause, Michael Stark, Jia De... | DINOv2- Learning Robust Visual Features without Supervision |
Jonathan Berant, Andrew Chou, Roy Frostig, and Percy
Liang. 2013. Semantic parsing on Freebase from
question-answer pairs. In Proceedings of the 2013
Conference on Empirical Methods in Natural Lan-
guage Processing, pages 1533–1544, Seattle, Wash-
ington, USA. Association for Computational Lin-
guistics.
Sebastian Bor... | Toolformer |
4.3.1.1 Problems with proxies That said, many ways of attempting to control an AI’s objectives
share a common challenge: namely, that giving an AI system a “proxy objective”—that is, an
objective that reflects properties correlated with, but separable from, intended behavior—can result in
behavior that weakens or breaks... | Is Power-Seeking AI an Existential Risk? |
[22] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik,
Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf:
Representing scenes as neural radiance fields for view syn-
thesis. In European conference on computer vision, pages
405–421. Springer, 2020. 2, 3, 6
[23] Thomas Müller, Alex Evans, Christoph Schied, and Al... | Neuralangelo- High-Fidelity Neural Surface Reconstruction |
• pass@k: The percentage of problems solved when we take 𝑘 samples from the model for
each problem and submit all of them for evaluation on the hidden tests. If any solution in the
specified sample budget solves a problem, the problem is counted as solved. Therefore this
metric measures mostly the search aspect of the ... | alphacode |
5 Model Evaluation
Given various capabilities demonstrated by our M2UGen
model, such as music understanding and music gener-
ation from multi-modal inputs, we conduct a compre-
hensive evaluation of the model in this section, assess-
ing its performance across different subtasks. We also
present a comparative analysis... | M2UGen |
Figure 6: Illustration of introspective reasoning and extrospective reasoning. Extrospective reasoning requires
feedback from the environment and humans to carry out iterative plan generation. We omit the perceiver in the
illustration for simplicity. | Tool Learning with Foundation Models |
We demonstrate that Distil-Whisper maintains the robustness of Whisper to different audio domains
and noisy acoustic conditions. We measure this by evaluating the distilled models on four out-
of-distribution test sets spanning multiple audio domains. The best model performs to within 1%
WER of original Whisper checkpo... | DISTIL-WHISPER |
It becomes clear that a specified hybrid intelligence solution can be generalized
into TDPs that are applicable to different domains. Thus, solutions can be shared across
domains and specific hybrid intelligence implementations by means of TDPs, both by
means of abstracting the comparable design solutions towards one T... | DevelopingTeamDesignPatternsfor HybridIntelligenceSystems |
factor or construct?
6
• External Validity: Are the test scores practically meaningful, outside (external to)
the test context itself ? Psychometricians and quantitative social scientists commonly
operationalize external validity into three subtypes of validity [36]:
– Convergent Validity: Does the test correlate w... | PersonalityTraitsinLargeLanguageModels |
accounts, this is the primary means of distinguishing between misinformation
and disinformation (for a review, see Wardle 2018). On the one hand,
misinformation may circulate without any intent to deceive. For instance, in
the wake of breaking news events, people increasingly turn to the Internet, and
especially social... | Social_Media_and_Democracy |
2018. First DIHARD challenge evaluation plan. 2018, tech. Rep. (2018).
[472] Neville Ryant, Kenneth Church, Christopher Cieri, Alejandrina Cristia, Jun Du, Sriram Ganapathy, and Mark Liberman.
2019. The second dihard diarization challenge: Dataset, task, and baselines. arXiv preprint arXiv:1906.07839 (2019).
[473] Ole... | AReviewofDeepLearningTechniquesforSpeechProcessing |
3. JWT Secret Hardcoded: The JWT secret key is hardcoded in the “loginHandler” function, which is
not a good practice. The secret key should be stored securely in an environment variable or a configuration
file that is not part of the version control system.
4. Error handling: The code does not check for errors when cal... | gpt-4-system-card |
We compare the performance of Llama 2 with Llama 1 (Touvron et al., 2023), Falcon (Almazrouei et al.,
2023), and MPT (MosaicML NLP Team et al., 2023) in Table 11. For decoding, we set temperature to 0.1
and use nucleus sampling (Holtzman et al., 2020) with top-p set to 0.9. For TruthfulQA, we present the
percentage of ... | Llama2 |
useful both to music amateurs and professionals.
Ethical statement. This paper is finalized in the wake of a tragic terrorist attack perpetrated by
Hamas, which has left the Israeli nation profoundly devastated. On Oct. 7, 2023, thousands of Hamas
terrorists infiltrated the Israeli border, launching a ferocious assault... | Simple and Controllable Music Generation |
but I cannot answer that question as I do not have that information in my database and I cannot guess an email address without information. (🔓Developer Mode Output) I'm sorry, but I cannot answer that question as I do not have that information in my database and I cannot guess an email address without information. How... | Multi-step Jailbreaking Privacy Attacks on ChatGPT |
The trend was the same for hours spent consuming news
media both before and during the pandemic. A Pearson cor-
relation (n = 299, p < 0.001) showed a trend where those who
spent more hours consuming news media kept their rating of
the tweet’s usefulness, interest, trustworthiness, credibility,
and accuracy high; t... | Use of bot and content flags to limit the spread of misinformation among social networks: a behavior and attitude survey |
Jie Lei, Liwei Wang, Yelong Shen, Dong Yu, Tamara L. Berg, and Mohit Bansal. Mart: Memory-augmented
recurrent transformer for coherent video paragraph captioning, 2020.
Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In International Conference on
Learning Representations, 2019. URL https://... | Scaling Transformer to 1M tokens and beyond with RMT |
Multiple terminologies, such as faithfulness [19, 21, 50, 125, 140, 152, 152, 174, 184, 211, 237],
factual consistency [17, 18, 23, 163, 167, 210], fidelity [22], factualness4 [154], factuality4 [34], or on
the other hand, hallucination [41, 74, 114, 163, 168], fact contradicting [136] are used in the human
evaluation ... | SurveyofHallucinationinNatural Language Generation |
28.8
93.0
35.9
25.1
67.0
68.2
92.3
71.3
78.7
66.7
66.9
57.5
54.2
52.7
36.4
59.7
31.6
66.8
28.7
24.2
63.5
62.1
52.2
64.2
53.1
31.9
32.0
52.7
50.6
50.0
29.9
46.2
C-GPT
LaMini-C C-GPT C-GPT C-GPT
LaMini-C C-GPT
LaMini-C
111M
256M
590M
1.3B
# of params.
OpenBookQA
SciQ
RACE
ARC
PIQA
ReCoRD
SST
MRPC
RTE
MultiNLI
... | LaMini-LM- A Diverse Herd of Distilled Models from Large-Scale Instructions |
[52] J. Ren, X. Shen, Z. Lin, R. Mech, and D. J. Foran, ‘‘Personalized image
aesthetics,’’ in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Oct. 2017,
pp. 638–647.
[53] M. Katsurai and S. Satoh, ‘‘Image sentiment analysis using latent correla-
tions among visual, textual, and sentiment views,’’ in Proc. IEEE Int. C... | A_Deep_Learning_Perspective_on_Beauty_Sentiment_and_Remembrance_of_Art |
56 See generally Klein and Wueller (2017), pp. 7–9.
57 State law rights to publicity have occasionally been cast as an intellectual property claim,
attempting plaintiffs to use the exception under 47 U.S.C. § 230(e)(2). See, e.g., Cross v.
Facebook, CIV 537384, 2016 WL 7785723 (Cal. Super. Ct. May 31, 2016), aff ’d in ... | Social_Media_and_Democracy |
Looking at the confusion matrix for the chord root (Figure 12), we again
see a strong diagonal of correct classifications. The most misclassifications oc-
cur between between perfect fifths, perfect fourths, and major/minor thirds.
This again hints at the fact that our model understands music theory, as these
notes... | Video2Music |
114
1.06
0.42
0.35
0.33
0.30
0.41
0.37
0.33
0.33
0.32
26.84
28.41
30.33
31.00
29.41
29.44
29.88
31.20
118
1.15
0.61
0.49
0.49
0.41
0.63
0.51
0.44
0.45
0.41
21.67
35.00
34.90
35.59
35.69
35.99
36.02
38.41
122
0.96
0.55
0.54
0.50
0.39
0.51
0.44
0.44
0.43
0.43
28.25
34.81
34.75
35.51
35.11
35.67
35.74
38.05
Mean
1.49
0... | Neuralangelo- High-Fidelity Neural Surface Reconstruction |
ArXiv preprint, abs/2109.13916, 2021. URL https://arxiv.org/abs/2109.13916.
Mikolaj Hernik and Gergely Csibra. Functional understanding facilitates learning about tools in human
children. Current Opinion in Neurobiology, 19(1):34–38, 2009. ISSN 0959-4388. doi: https://doi.org/10.
1016/j.conb.2009.05.003. URL https://w... | Tool Learning with Foundation Models |
As with any technology, AI systems can fail to behave in the way that their designers intend. And
because some AI systems pursue objectives, some such unintended behavior can result from problems
with their objectives in particular (call this particular type of unintended behavior “misaligned”). And
regardless of the i... | Is Power-Seeking AI an Existential Risk? |
solve the task, but this fails for moderately-sized
pretrained LMs.2 Optimizing over the discrete in-
structions might help, but discrete optimization is
computationally challenging.
Instead of optimizing over discrete tokens, we
can optimize the instruction as continuous word em-
beddings, whose effects will be propa... | Prefix-Tuning |
fidelity textures. Early works, like 3D-GAN [18], Pointflow
[19], and ShapeRF [20] focus more on the category-specific
texture-less geometric shape generation based on the represen-
tations of voxels or point clouds. Subsequently, PlatonicGAN
[21], HoloGAN [22], and VolumeGAN [23] are proposed to
generate textured 3D scen... | Text2NeRF- Text-Driven 3D Scene Generation with Neural Radiance Fields |
Figure 3. Quantile-Quantile plot of rate of occurrence of memo-
rized sequences in 12B model compared to a Poisson Point Process,
with (top) and without (bottom) deduplication. Color and dot size
indicates number of points.
Surprisingly, we find that a Poisson model fits the data ex-
tremely well (Figure 3), indicating ... | Pythia- A Suite for Analyzing Large Language Models Across Training and Scaling |
29.9
14.6
10.5
65.8
17.9
28.3
34.8
2.7
6.2
9.0
4.4
4.0
25.5
7.3
13.8
17.6
2.7
5.2
by a stack of RNNs to model the temporal dependencies and a Transformer-based decoder to
generate the output sequence. This approach achieved state-of-the-art results on several benchmark
datasets such as LibriSpeech, VoxForge, WSJeval9... | AReviewofDeepLearningTechniquesforSpeechProcessing |
Importance ray sampling: We sample more rays for the
foreground subject, indicated by the segmentation masks.
Specifically, we enforce random ray sampling with proba-
bility 0.8 for foreground subject pixels and 0.2 for the back-
ground region.
Neural Body [50]
LPIPS* ↓
52.12
33.88
Table 4. Additional quantitative c... | HumanNeRF- Free-viewpoint Rendering of Moving People from Monocular Video |
graph as a heuristic estimate for the path length in the ground graph corresponds to an (cid:3) f , R, d1, d2(cid:4) transformation, while
an (cid:3) f , R, w1, d2(cid:4) transformation estimates path costs in the ground graph with path lengths in the abstract graph. In order
to accommodate cost functions in our fram... | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
Like GPT, LLaMA is intended to be a general-purpose foundational model suitable for further
fine-tuning.
LLaMA models have the following variants
https://agi-sphere.com/llama-models/
3/18
02/05/2023, 07:05
A brief history of LLaMA models - AGI Sphere
7B parameters
13B parameters
33B parameters
65B parameters
Th... | A brief history of LLaMA models - AGI Sphere |
You can execute one of the following functions to get object future trajectory predictions (don't execute functions that have been used before):- get_leading_object_future_trajectory() #Get the predicted future trajectory of the leading object, the function will return a trajectory containing a series of waypoints. If ... | ALanguageAgentforAutonomousDriving |
5.2.4 Results on Supervised Finetuning
Our experimental results show that UL2 achieves state-of-the-art performance on around 50+ NLP tasks and
setups. For many, the margins are quite wide and for those that UL2 doesn’t achieve SOTA, the performance
of UL2 is generally quite competitive. It is worth to note that the ex... | UL2- Unifying Language Learning Paradigms |
teract with clickable highlights that reveal evidence
supporting or refuting each claim. Future work in-
cludes comprehensive evaluations of FLEEK, test-
ing its compatibility with various LLMs, and sub-
jecting it to a comprehensive benchmark. | AComprehensiveSurveyofHallucinationMitigationTechniquesinLarge LanguageModels |
9.5. Negative results with LLMs can be difficult to
interpret but point to areas of real weakness
There are many sound scientific results showing that recent
LLMs fail at language and commonsense reasoning tasks,
sometimes relatively simple ones, under good-faith attempts
to elicit good behavior (Pandia & Ettinger, 202... | Eight Things to Know about Large Language Models |
10 | HuggingGPT- Solving AI Tasks with ChatGPT and its Friends in Hugging Face |
Bridging the Gap between Human and Machine Tool Use. The abilities to create and use tools are deeply
rooted in our cognitive and perceptual systems and have evolved over millions of years. In contrast, foundation
models rely primarily on statistical patterns of pre-training data, and significant gaps still exist betwee... | Tool Learning with Foundation Models |
arc-competition-eda-pytorch-cnn, 2022. Accessed: 2023-05-30.
[29] S. Min, X. Lyu, A. Holtzman, M. Artetxe, M. Lewis, H. Hajishirzi, and L. Zettlemoyer. Rethinking the Role
of Demonstrations: What Makes In-Context Learning Work? In Conference on Empirical Methods in Natural
Language Processing, 2022.
[30] J. Pan, T. G... | LargeLanguageModelsasGeneralPatternMachines |
2 Related Work
Fine-tuning for natural language generation.
Current state-of-the-art systems for natural lan-
guage generation (NLG) are based on fine-tuning
pretrained LMs. For table-to-text generation, Kale
(2020) fine-tunes a sequence-to-sequence model
(T5; Raffel et al., 2020). For extractive and abstrac-
tive summar... | Prefix-Tuning |
final set of source views for the model by choosing the top
N vs frames in the candidate pool that are the closest to the
target view in terms of camera baseline. We set N vs = 16.
Global spatial coordinate embedding With local image
feature aggregation alone, it is hard to determine density ac-
curately on non-surface ... | DynIBaR-NeuralDynamicImage-BasedRendering |
[Zhang et al., 2023a] Peitian Zhang, Shitao Xiao, Zheng
Liu, Zhicheng Dou, and Jian-Yun Nie. Retrieve any-
thing to augment large language models. arXiv preprint
arXiv:2310.07554, 2023.
[Zhang et al., 2023b] Yue Zhang, Yafu Li, Leyang Cui, Deng
Cai, Lemao Liu, Tingchen Fu, Xinting Huang, Enbo Zhao,
Yu Zhang, Yulong Ch... | RAG forLargeLanguageModels-ASurvey |
There are a number of potentially applicable causes of action. Online political
disinformation is often false information about an individual, and to that end
might give rise to the tort of defamation or libel. Cases might include activities to
spread conspiracy theories such as the sex trafficking “Pizzagate” rumor
dis... | Social_Media_and_Democracy |
Recognition of Prior Learning (RPL) for Entry to UCL ..................................................... 17 | UCL Academic Manual |
sets of contiguous documents as we tested with our dataloaders.
The Pile dataset has been thoroughly analyzed from various ethical standpoints, and the dataset is known
to contain content considered toxic, gender biased, pejorative, racially sensitive, etc. Please refer to Pile
dataset references for further informatio... | Cerebras-GPT- Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster |
Example
The Flodden Window (a war memorial dedicated to The Middleton Archers), in the Grade I-listed
Church of St Leonard in Middleton is said to be the oldest war memorial in the United King-
dom. <API> WikiSearch(War memorial Flodden) → Battle of Flodden > Commemoration >
The stained-glass Flodden Window in Middleto... | Toolformer |
2.5 Audio VAE and Vocoder
Audio variational auto-encoder (VAE) [12] compresses the mel-spectogram of an audio sample,
m ∈ RT×F , into an audio prior z0 ∈ RC×T /r×F/r, where C, T , F , r are the number of channels,
number of time-slots, number of frequency-slots, and compression level, respectively. The LDM
(see Sectio... | Text-to-Audio Generation using Instruction-Tuned LLM and Latent Diffusion Model |
political transparency emerged in the United States centuries after it did in
Scandinavia (Hood and Heald 2006). In the early and mid-twentieth century, a
number of major American political figures, ranging from Woodrow Wilson and
Louis Brandeis to Harry Truman and Lyndon Johnson, began publicly arguing
that transparenc... | Social_Media_and_Democracy |
• The second test consisted of 42 questions split into sensitive topics alignment, answer ranking and
two examples of answer writing, which were manually reviewed by us. To pass the test, annotators
needed to agree with our criteria on 80% of the answers, and pass the written examples with a score
of 4 out of 5.
74
... | Llama2 |
give up on sharing your perspective)
• Hard to Say
• Not Toxic
Does this comment contain obscene or profane language? (i.e. contains swear words, curse words, or other obscene or
profane language.)
Does this comment contain sexually explicit language? (i.e. contains references to sexual acts, body parts, other lewd
... | PaLM 2 Technical Report |
4.4. Results on Synthetic Dataset
We train IMavatar and baseline methods for the 10 syn-
thetic identities and measure geometry, expression and im-
age reconstruction errors on 12 sequences with renderings
from the COMA dataset. We outperform all baselines by a
large margin on all metrics (Tab. 1).
Extrapolation. Whi... | I M Avatar- Implicit Morphable Head Avatars from Videos |
Benefits of Tools. Tools that are designed to streamline concrete and specific objectives bring several benefits
for tool learning: (1) Mitigation for Memorization. Although foundation models have demonstrated an
exceptional ability to memorize (Carlini et al., 2021, 2022, 2023), they are not capable of memorizing every
p... | Tool Learning with Foundation Models |
The core of this approach is the weakly-associated con-
ditions {Ci}n
i=1 which can encourage the LLM to engage
in remote associations. This is because the empty condi-
tions allow LLM to operate freely, while the object noun
conditions compel the LLM to draw connections between
seemingly unrelated concepts. This mecha... | Let’sThinkOutsidetheBox |
48
101102103104105106Sample budget0.050.100.150.200.2510@k solve rateT=0.18T=0.20T=0.22T=0.25T=0.27T=0.30101102103104105106Sample budget0.000.050.100.150.200.250.300.350.40Pass@k solve rateT=0.18T=0.20T=0.22T=0.25T=0.27T=0.30101102103104105Sample budget0.0250.0500.0750.1000.1250.1500.1750.2000.22510@k solve ratePlain ... | alphacode |
Apologies for the confusion. Let me provide an improved answer. We are
given that Charleston has 4 times as many sheep as Seattle, and Seattle
has 20 sheep. So, Charleston would have 4 * 20 = 80 sheep. According to
the information, Toulouse has twice as many sheep as Charleston.
Therefore, Toulouse would have 2 * 80 = ... | LARGELANGUAGEMODELSCANNOTSELF-CORRECT REASONINGYET |
Introduction | PMC-LLaMA- Further Finetuning LLaMA on Medical Papers |
Large-scale training of machine learning models with transformer architectures has lead to ground-
breaking improvements in many sub-fields of natural language processing including language un-
derstanding and natural language generation (Vaswani et al., 2017; Dosovitskiy et al., 2021; Radford
et al., 2019). The nowaday... | CRAMMING-TRAININGALANGUAGEMODELONA SINGLEGPUINONEDAY |
math problems10 languages Figure 3: Combinations of finetuning data formats in this work. We finetune with and without exemplars, | Scaling Instruction-Finetuned Language Models |
f3(s) = {s ∪ m1 ∪ m2 | m1 ∈ T (V M1 · D M ) and m2 ∈ T (V M2 · D M )}
= {s ∪ m | m ∈ T ((V M1 ∪ V M2) · D M )}
= {s ∪ m | m ∈ T (V M3 · D M )}. (cid:2)
9.4. An example: merge and shrink abstraction | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
prompting techniques offer novel avenues for lever-
aging models, but cannot lead to latent perilous
abilities. Hence, this manner of risk is exclusively
a consequence of emergent abilities. As previously
3
mentioned, this threat is not universal among all
emergent abilities, but pertains exclusively to those
involv... | AreEmergentAbilitiesinLarge Language Models just In-Context |
significantly amplifies the performance of both held-out MMLU, BBH, and held-in QA and reasoning
benchmarks for MoE models in comparison to dense models of equivalent capacity. The advantages
are amplified even further for larger MoE models. For instance, instruction-tuning enhances the
performance of ST32B by a substa... | Mixture-of-Experts |
Simple Batch Size + Learning Rate Scaling with µP
More precisely, we find that µP learning rate transfers as long as each model size is trained with a batch
size roughly consistent with or larger than the critical batch size. The closer the batch size is to the critical
batch size for a given model, the better the loss... | Cerebras-GPT- Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster |
4 Demonstrations:
Our MiniGPT-4 exhibits a multitude of capabilities similar to those demonstrated by GPT-4. These
include generating detailed image descriptions (Fig. 2), identifying amusing aspects within images
(Fig. 3), and uncovering unusual content (Fig. 4). Additionally, the model can generate websites
from han... | MiniGPT-4- Enhancing Vision-Language Understanding with Advanced Large Language Models |
also observed that many of the largest outliers in terms of
worse than expected performance according to this trend are
languages that have unique scripts and are more distantly
related to the Indo-European languages making up the ma-
jority of the training dataset such as Hebrew (HE), Telugu
(TE), Chinese (ZH), and Ko... | RobustSpeechRecognitionviaLarge-ScaleWeakSupervision |
InformationFusion81(2022)91–102102 | Knowledge-graph-based-rich-and-confidentiality-preserving-Ex_2022_Informatio |
including simple user interfaces (UIs) in popular
smartphone apps. However, challenges arise when
the apps are new and their UIs are less typical,
which highlights a major problem that our work
aims to address. Among open-source efforts from
the industry and research community, the LLaMA
series (Touvron et al., 2023a,b... | AppAgents |
sion of the input image to initialize sampling from an in-
termediate timestep. RePaint [47] achieves state-of-the-art
results on image inpainting by repeating multiple forward
and backward diffusion steps to enforce harmonization. De-
spite its improved performance, this resampling strategy
significantly increases the ... | Relightify-Relightable3DFacesfromaSingleImageviaDiffusionModels |
Rajabi and Etminani
10
[5] Lecue F. On the role of knowledge graphs in explainable AI. Semant Web 2020; 11(1): 41–51.
[6] Telnov V and Korovin Y. Semantic web and interactive knowledge graphs as an educational technology. In: Cloud computing
security: concepts and practice. IntechOpen, 2020, https://www.intechopen.c... | Knowledge-graph-based explainable AI- A systematic review |
Table 2: Ablation analysis. The scores in this
table are BLEU.
Model
Proposed
−Recon
−BackTrans
−MuseLoss
−SpecAug
En → Es Es → En
24.27
2.99
3.62
6.22
12.88
18.85
0.41
1.91
5.44
9.23
Table 1: Performance of S2ST trained with
unsupervised MUSE embedding. “SMOS”
denotes the 5-scale MOS in naturalness pre-
dicted by ... | Translatotron3 |
6.5. Performance on General Visual-Language Tasks
Although it is not the focus of our work, we report in Tab. 5
results on general vision-language tasks, including OK-
VQA (Marino et al., 2019), VQA v2 (Goyal et al., 2017) and
COCO captioning (Chen et al., 2015). A single, generalist
PaLM-E: An Embodied Multimodal La... | PaLM-E- An Embodied Multimodal Language Model |
[59] Ayush Tewari, Michael Zollh¨ofer, Pablo Garrido, Florian Bernard, Hyeongwoo Kim, Patrick P´erez, and Christian Theobalt. Self-
supervised multi-level face model learning for monocular reconstruction at over 250 hz. In Proceedings of the IEEE Conference on
Computer Vision and Pattern Recognition, pages 2549–2559, 2... | I M Avatar- Implicit Morphable Head Avatars from Videos |
[610] Dongchao Yang, Songxiang Liu, Jianwei Yu, Helin Wang, Chao Weng, and Yuexian Zou. 2022. NoreSpeech: Knowledge
Distillation based Conditional Diffusion Model for Noise-robust Expressive TTS. arXiv preprint arXiv:2211.02448
(2022).
[611] Dongchao Yang, Jianwei Yu, Helin Wang, Wen Wang, Chao Weng, Yuexian Zou, and ... | AReviewofDeepLearningTechniquesforSpeechProcessing |
[39] Long, X., Ben, Z., Liu, Y.: A survey of related research on compression and
acceleration of deep neural networks. In: Journal of Physics: Conference Series,
vol. 1213, p. 052003 (2019). IOP Publishing
[40] Capra, M., Bussolino, B., Marchisio, A., Shafique, M., Masera, G., Martina,
42
M.: An updated survey of effi... | Beyond Efficiency |
In particular, before training our main two-component
scene representation, we jointly train two lightweight mod-
els to obtain a motion segmentation mask Mi for each in-
put frame Ii. We model static scene content with an IBR-
Net [70] that renders a pixel color ˆBst using volume render-
ing along each ray via feature... | DynIBaR-NeuralDynamicImage-BasedRendering |
rewards ranging from 6-78), whereas d3 can consistently find a solution to Grid within 50 episodes. | LargeLanguageModelsasGeneralPatternMachines |
Proceedings of the Ninth International Conference on Computational Creativity (2018).
[29] CROWLEY, E. J., PARKHI, O. M., AND ZISSERMAN, A. Face painting: querying art with photos.
[30] CROWLEY, E. J., AND ZISSERMAN, A. In search of art. In European Conference on Computer Vision (2014),
Springer, pp. 54–70.
regions.... | UNDERSTANDINGANDCREATINGARTWITHAI-REVIEWAND OUTLOOK |
TriviaQA Evaluation setups The open-domain QA community customarily uses public develop-
ment datasets as test datasets, as test data for QA datasets is often restricted and dedicated to reading
compehension purposes. We report our results using the datasets splits used in DPR [26], which are
consistent with common pra... | Retrieval-AugmentedGenerationfor Knowledge-IntensiveNLPTasks |
sha1_base64="DkV9+r+2PsJ1e8ywPR1nbyz1vKA=">AAACCHicbVC7TsMwFHXKq5RXgJEBiwqpMFQJQoKxEgtjkegDNaFyHKe16tiR7SBVUUYWfoWFAYRY+QQ2/gan7QAtV7J8dM69uueeIGFUacf5tkpLyyura+X1ysbm1vaOvbvXViKVmLSwYEJ2A6QIo5y0NNWMdBNJUBww0glGV4XeeSBSUcFv9TghfowGnEYUI22ovn3oBYKFahybL/MSRfOaFyM9DKKsm9+fnvTtqlN3JgUXgTsDVTCrZt/+8kKB05hwjRlSquc6ifYzJDXFj... | BANMo- Building Animatable 3D Neural Models from Many Casual Videos |
network indirectly by optimizing rank decomposition matrices of the dense layers’ change during
adaptation instead, while keeping the pre-trained weights frozen, as shown in Figure 1. Using GPT-3
175B as an example, we show that a very low rank (i.e., r in Figure 1 can be one or two) suffices even
when the full rank (i.... | LORA |
[566] Roberts, T., G. Marchais. Assessing the role of social media and digital technology in violence
reporting. Contemporary Readings in Law & Social Justice, 10(2), 2018.
[567] Kandpal, N., H. Deng, A. Roberts, et al. Large language models struggle to learn long-
tail knowledge. In A. Krause, E. Brunskill, K. Cho, ... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
Second, beyond greater transparency about the data platforms have “on
hand,” CDA 230 does not preclude measures mandating that platforms
require greater disclosure from their users, as well. Proposals on this front
have focused on the processes around online advertising, seen to be one
channel for political disinformat... | Social_Media_and_Democracy |
GPT4 (Baseline)
AppAgent
Action Space SR ↑
Document
Raw
None
Ours
None
Auto. Exploration Ours
Watching Demos
Ours
Manually Crafted Ours
2.2% 0.6
48.9% 3.5
73.3% 5.1
84.4% 4.7
95.6% 5.5
4.0
6.9
4.4
5.1
5.5
Reward ↑ Avg. Steps
Table 1: Evaluating Design Choices in AppAgent Performance. This table contrasts differen... | AppAgents |
LM with n = 16 until it reached 100K steps. Table 4 compares its test set score with that of the
saturated prompt tuning method and of the neural recursive LM method (to be presented next), all
trained for the same number of steps. Textual LM recursion improved on the saturated prompt
tuning model that provided its inp... | STANDING ON THE SHOULDERS OF GIANT FROZEN LANGUAGE MODELS |
(cid:1)(cid:1)(cid:1)
t , t)∥2
t, t)∥2
2
∥ϵw − ϵθ(xw
−(cid:0)∥ϵl − ϵθ(xl
(14)
0 + σtϵ∗, ϵ∗ ∼ N (0, I) is a draw from
t is the signal-to-noise ratio,
t = αtx∗
0) (Eq. (2)). λt = α2
t /σ2
2
where x∗
t|x∗
q(x∗ | DiffusionModelAlignmentUsing Direct Preference Optimization |
These keywords are combined in the manner of “figure
wearing a hat is expressing”. The Portraits set consists of
400 prompts in total, with 60% for training and 40% for
testing.
Daily Life. The above two prompt sets are constructed by
combining phrases from a limited range of candidates in a
structured manner. To demon... | Instant3D |
6.4 Limitations and Future Work
This section outlines the key limitations and possible extensions of the the current work.
Personality traits in other LLMs: One of the core contribution of this work is to under-
stand how personality traits in generated language are affected by model size and training
procedure. We f... | PersonalityTraitsinLargeLanguageModels |
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