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Abstract
lighting VideoPoet’s ability to generate high-fidelity mo-
tions.
We present VideoPoet, a language model capable of syn-
thesizing high-quality video, with matching audio, from a
large variety of conditioning signals. VideoPoet employs a
decoder-only transformer architecture that processes mul-
timodal inputs... | VideoPoet |
multimodal AI solutions that capture the intricacies of human health and disease (Acosta et al., 2022).
Given biomedical data’s complexity and high dimensionality, most efforts focus on vision-language pretraining
instead of omni-modal fusion (Selivanov et al., 2023; Chambon et al., 2022). To enable multimodal models
t... | BiomedGPT |
poorly on commonsense reasoning tasks, but relatively better than non-text semantic reasoning [5].
Meanwhile, ChatGPT also lacks spatial reasoning ability, but exhibits better temporal reasoning.
Finally, while the performance of ChatGPT is acceptable on causal and analogical reasoning, it
performs poorly on multi-hop ... | ASurveyonEvaluationofLargeLanguageModels |
πref(y | x) exp
r(x, y)
(8)
(cid:88)
y
(cid:19)
(cid:18) 1
β
The operator f simply normalizes the reward function with the logarithm of the partition function
of πr. Since the added normalization term is only a function of the prefix x, f (r; πref, β)(x, y) is a
reward function in the equivalence class of r(x, ... | Direct Preference Optimization |
G2 to G3. Then τ1◦τ2 = (cid:3) f1◦ f2, R1◦ R2, w1, w3(cid:4), i.e. the weights in the graphs remain unchanged. | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
[43] Vikramjit Sidhu, Edgar Tretschk, Vladislav Golyanik, An-
tonio Agudo, and Christian Theobalt. Neural dense non-
rigid structure from motion with latent space constraints. In
ECCV, 2020. 2
[44] Krishna Kumar Singh and Yong Jae Lee. Hide-and-seek:
Forcing a network to be meticulous for weakly-supervised
object and ... | BANMo- Building Animatable 3D Neural Models from Many Casual Videos |
9 Estimating the Environmental Impact of Training our Models | DINOv2- Learning Robust Visual Features without Supervision |
We compare our method with several hard baselines and
state-of-the-art (SOTA) methods using image similarity and
expression metrics. To evaluate the generated geometry un-
der different expressions and poses, we construct a synthetic
dataset containing 10 subjects. We quantitatively show that
our method produces more a... | I M Avatar- Implicit Morphable Head Avatars from Videos |
Faithfulness
Answer Relevance
Cosine Similarity
Accuracy
Accuracy
Accuracy
*
*
*
*
*
† represents a benchmark, and ‡ represents a tool. * denotes customized quantitative metrics, which deviate from traditional
metrics. Readers are encouraged to consult pertinent literature for the specific quantification formulas... | RAG forLargeLanguageModels-ASurvey |
3 QUICK OVERVIEW
The initial version of JaxPruner consists of about 1000 lines of code (+850 lines of tests), organized
into six modules. We provide interactive Python notebooks and integration with popular research
1https://jax.readthedocs.io/en/latest/jax.experimental.sparse.html
2
Published as a conference pape... | JAXPRUNER |
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... | Announcing Jurassic-2 and Task-Specific APIs |
Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh,
A., Kenton, Z., Brown, S., Hawkins, W., Stepleton, T., Biles, C., Birhane, A., Haas, J., Rimell, L., Hendricks, L. A.,
Isaac, W., Legassick, S., Irving, G., and Gabriel, I. Ethical and social risks o... | PaLM 2 Technical Report |
Pythia: A Suite for Analyzing Large Language Models
F. Additional Details on Design and Considerations
F.1. Assessment of Existing Suites
We assessed existing model suites to determine if any pre-existing models met all of researchers’ requirements and
expectations for rigorous scientific study on language models.
GP... | Pythia- A Suite for Analyzing Large Language Models Across Training and Scaling |
Pythia: A Suite for Analyzing Large Language Models
Figure 4. Accuracy of the arithmetic addition task with 16 shots, across various model sizes (divided by subfigure). For each model,
multiple intermediate checkpoints (differentiated by color and their step number) are plotted. Each point represents the average accura... | Pythia- A Suite for Analyzing Large Language Models Across Training and Scaling |
Bots and Computational Propaganda
99
though the means to build and launch bots over social media is becoming more
widespread – and available to regular citizens – everyday (Woolley 2018).
Nimmo and the Digital Forensic Research (DFR) team at the Atlantic
Council point out three core features of political bots and co... | Social_Media_and_Democracy |
Furthermore, the first author conducted an inductive analy-
sis [94] to study the qualitative distinctions between the responses
produced in each condition. We employed qualitative open cod-
ing [32] in two phases. In the first phase, we generated codes that
closely represented the generated responses at the sentence l... | Generative Agents- Interactive Simulacra of Human Behavior |
6.2 Robustness
primarily due to the shift in label distribution
during fine-tuning, as Symmetric FEVER con-
tains only claims with SUPPORT and REFUTE labels.
ProoFVer accuracy drops by only less than 3%,
as it is trained with a seq2seq objective. To miti-
gate the effect of catastrophic forgetting, we apply
L2 regular... | ProoFVer- Natural Logic Theorem Proving for Fact Verification |
Le, Q. and Mikolov, T. Distributed representations of sen-
tences and documents. In ICML, 2014.
Lichman, M. UCI machine learning repository, 2013.
Parameter-Efficient Transfer Learning for NLP
Long, J., Shelhamer, E., and Darrell, T. Fully convolutional
networks for semantic segmentation. In CVPR, 2015.
Simonyan,... | Parameter-Efficient Transfer Learning for NLP |
• Depending on how you ask them, experts in 2017 assign a median probability of >30%
or >50% to “unaided machines can accomplish every task better and more cheaply than
human workers” by 2066, and a 3% or 10% chance to the “full automation of labor” by 2066
(though their views in this respect are notably inconsistent, ... | Is Power-Seeking AI an Existential Risk? |
Details for Step 2. Algorithm 1 provides the pseudocode for Step 2. The main structure of Al-
gorithm 1 is a training loop which updates the proxy model over T steps. At each step, we fol-
low Sagawa et al. (2020) and sample a minibatch with uniform domain weights (regardless of the
initial domain weights α0, which onl... | DoReMi- Optimizing Data Mixtures Speeds Up Language Model Pretraining |
Batching across users. Under the same person-
alization setting, prefix-tuning allows batching dif-
ferent users’ queries even though they are backed
by different prefixes. When multiple users query
a cloud GPU device with their inputs, it is compu-
tationally efficient to put these users in the same
batch. Prefix-tuning k... | Prefix-Tuning |
Promoting Usage Education. Participants who feel unsure about using LLMs will need usage education to become
sufficiently confident. Previous work on technology adoption has shown that support from interpersonal relationships
can accelerate adoption [25]. Therefore, establishing support communities could facilitate LLM... | Adoptionand AppropriationofLLMs |
cross-format training. arXiv preprint arXiv:2202.12359, 2022.
Tushar Khot, Peter Clark, Michal Guerquin, Peter Jansen, and Ashish Sabharwal. Qasc: A dataset for question
answering via sentence composition. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34,
pp. 8082–8090, 2020.
Tomáš Kočiský, ... | UL2- Unifying Language Learning Paradigms |
D. Explainability of PEFT Methods
Though numerous PEFT methods have been proposed, there
is a lack of comprehensive studies exploring the reasons
behind their ability to achieve comparable performance and
reduce trainable parameters. Work from [41] unifies PEFT
methods under the concept of sparse fine-tuned models and... | Parameter-EfficientFine-TuningMethods |
Generative Agents
arXiv, April, 2023,
retrieving the most relevant pieces of information but also in de-
termining the appropriate space to execute an action, given the
increasing number of locations that the agent learned about. As a
result, some agents chose less typical locations for their actions,
potentially mak... | Generative Agents- Interactive Simulacra of Human Behavior |
quarter, brain, wish, halloween, einstein, helmet,
sun, tip, laundry, judge. Give your answer in al-
phabetical order. target: quarter”. Given the nature
of this task, we also evaluated it only using exact
match. | AreEmergentAbilitiesinLarge Language Models just In-Context |
Further guidance
i) Academic credits are awarded for the successful completion of assessed modules.
Affiliate Student English Language Requirements
12. Affiliate students should satisfy UCL’s English Language Requirements (see Section 2.5). EU
students admitted via an exchange or EU partnership agreement ca... | UCL Academic Manual |
Another line of work attempts to remove the need for these handcrafted data augmen-
tations. One approach is to use a reconstruction-based objectives such as MAE [He et al.,
2022] which uses a reconstruction loss in pixel space to avoid the need for defining precise
invariances. Another approach is based on a joint-embe... | A Cookbook of Self-Supervised Learning |
Perhaps two centuries after Mill’s wish, the success of precise but inscrutable models has pushed researchers from
fields such as of cognitive science, law and social sciences to join forces with the Machine Learning community and work
towards providing a unified view over the concept of explanation. In... | Knowledge graphs as tools for explainable machine learning: A survey |
4
Technical Report
Example 3.2: Question and Backward Question
Question: James buys 5 packs of beef that are 4 pounds each. The price of beef is $5.50 per pound. How
much did he pay? Answer: He bought 5*4=20 pounds of beef. He paid 20*5.5=$110. The answer is: 110 ✓
Backward Question: James buys x packs of beef that ... | METAMATH |
5.3.3. Choice of the pre-training dataset
Table 7 compares our base 1B model trained on our full GitHub dataset with equivalent models that
are pretrained on (1) the Python-only portion of GitHub, (2) the MassiveText generic text dataset
(Rae et al., 2021) which also includes a portion of GitHub or (3) not pre-trained ... | alphacode |
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 |
by setting a threshold and nullifying parameters beneath it. Yet, by not respecting
the overarching structure of the LLM, it leads to a model with a non-uniform sparse
makeup. This non-uniformity necessitates unique compression methods to effectively
store and compute the trimmed model. SparseGPT [117] represents a rapi... | Beyond Efficiency |
• Neutral group behaviors. In human society, strong personal values vary widely and tend toward
individualism and competitiveness. In contrast, LLMs which are designed with an emphasis on
being “helpful, honest, and harmless” [527] often demonstrate a tendency towards neutrality [528].
This alignment with neutral value... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
Y = XW + ϵ.
(3)
Empirically, the polynomial model performs better than
several models that we evaluated; for details, see Sup. Mat.
Shape to Attributes (S2A): We predict linguistic at-
tribute scores, A, from SMPL-X shape parameters, β.
Again, we fit a second-degree polynomial regression model.
S2A has “swapped” input... | Accurate 3D Body Shape Regression using Metric and Semantic Attributes |
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... | Principal-agent VCG contracts - ScienceDirect |
computational demands. To enhance video feature extrac-
tion, we introduce two types of learnable tokens: spatial
and temporal. These tokens are specifically designed to
capture and distill information from the spatial and tempo-
ral dimensions of video features. We mathematically de-
fine spatial tokens as Qs ∈ RNs×D ... | GPT4Video |
https://doi.org/10.1017/9781108890960 Published online by Cambridge University Press
Amendment of Section 230
257
election, put it, “troll tactics” were a means with which to “build [his]
brand” (Marantz 2016). | Social_Media_and_Democracy |
Spurious Biases. The shortcut learning problem has been observed in various natural language understanding tasks
under the pretraining and fine-tuning paradigm, where models heavily rely on spurious correlations between input and
labels in the fine-tuning data for prediction [31, 35, 98]. For example, in reading compre... | Harnessing the Power of LLMs in Practice- A Survey on ChatGPT and Beyond |
The integration of AI in various aspects of our lives is becoming increasingly preva-
lent, resulting in a growing frequency of human-AI interaction. The advancements in
AI research enable new opportunities for technology to perform part of the work au-
tonomously, e.g., in the medical, financial, legal, and military f... | DevelopingTeamDesignPatternsfor HybridIntelligenceSystems |
100This is a point I believe I heard Evan Hubinger make on a podcast (either this one, or this one).
101See Burda and Edwards (2018), and discussion in Christian (2020).
102Thanks to Carl Shulman for discussion. See also Christiano (2018): “One reason to be scared is that a wide
variety of goals could lead to influence-... | Is Power-Seeking AI an Existential Risk? |
acknowledging the potential advantage of flexibility for the latter.
Autoregressive Block Infilling. To evaluate the effectiveness of the proposed autoregressive block infilling objective
especially comparing with the conventional left-to-right causal learning, we benchmark three configurations in our
ablation study: (... | DOCLLM |
scale NVIDIA A100 cluster, thousands of GPU hours, and
more than 12M training images. We train a ControlNet for
the SD V2 with the same depth conditioning but only use
200k training samples, one single NVIDIA RTX 3090Ti, and
5 days of training. We use 100 images generated by each
SDv2-D2I and ControlNet to teach 12 use... | AddingConditionalControltoText-to-ImageDiffusionModels |
The correlations of the SHAPE scale and the TRI factors are presented in Table 6. The correlation analysis
indicated that the Social Threat and Agency factors of the SHAPE scale were strongly correlated with the
Discomfort and Insecurity scales of the TRI. The less Discomfort and Insecurity experienced in response to
t... | Society’sAttitudesTowardsHumanAugmentation |
According to Honovich et al. [73], NLI-based approaches are more robust to lexical variability
than token matching approaches such as IE-based and QA-based metrics. Nevertheless, as illustrated
by Falke et al. [45], off-the-shelf NLI models tend to transfer poorly to the abstractive summarization
task. Thus, there is a... | SurveyofHallucinationinNatural Language Generation |
Introspective Reasoning. This kind of reasoning directly generates multi-step plans for tool use without
knowing intermediate execution results. One representative work of introspective reasoning is Program-Aided
Language Models (PAL) (Gao et al., 2022), which prompts models to generate Python codes for intermediate
re... | Tool Learning with Foundation Models |
streams, i.e., tool-augmented learning and tool-oriented learning.
We formulate a general tool learning framework (§ 3.1), which comprises the controller (typically modeled
using a foundation model), tool set, environment, perceiver, and human. Then we highlight core research
problems for tool learning as well as intro... | Tool Learning with Foundation Models |
Yue Wang, Weishi Wang, Shafiq Joty, and Steven C.H.
Hoi. 2021. CodeT5: Identifier-aware unified pre-
trained encoder-decoder models for code understand-
In Proceedings of the 2021
ing and generation.
Conference on Empirical Methods in Natural Lan-
guage Processing, pages 8696–8708, Online and
Punta Cana, Dominican Repu... | CODEFUSION |
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| Product-Led AI _ Greylock |
9
Figure 2: Effect of noise on WER performance. WER on LibriSpeech test-clean as a function of
SNR under additive white noise (left) and pub noise (right).
8.4 ROBUSTNESS TO HALLUCINATIONS | DISTIL-WHISPER |
arXiv:2306.02549 (2023).
[59] Jochen Hartmann, Jasper Schwenzow, and Maximilian Witte. 2023. The political ideology of conversational AI:
Converging evidence on ChatGPT’s pro-environmental, left-libertarian orientation. arXiv preprint arXiv:2301.01768
(2023).
[60] Qianyu He, Jie Zeng, Wenhao Huang, Lina Chen, Jin Xia... | ASurveyonEvaluationofLargeLanguageModels |
Online Hate Speech
57
alike. Most commonly, hate speech is understood to be bias-motivated, hostile,
and malicious language targeted at a person or group because of their actual or
perceived innate characteristics (Cohen-Almagor 2011; Faris et al. 2016).
However, as Sellars (2016) argues, “for all of the extensive li... | Social_Media_and_Democracy |
[273] Samuel Kriman, Stanislav Beliaev, Boris Ginsburg, Jocelyn Huang, Oleksii Kuchaiev, Vitaly Lavrukhin, Ryan Leary,
Jason Li, and Yang Zhang. 2020. Quartznet: Deep automatic speech recognition with 1d time-channel separable
convolutions. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Sign... | AReviewofDeepLearningTechniquesforSpeechProcessing |
If you’ve subscribed to any popular consumer subscription products pre-AI (e.g., Calm,
Headspace, Duolingo) you’ll know they mostly charge less than $70 per year for annual
subscribers—with an average of $10/month for monthly subscribers. Generative AI unlocks a
new level of value, which increases consumer willingness ... | How Are Consumers Using Generative AI_ _ Andreessen Horowitz |
into classes. We can state the following two lemmas:
Lemma 1. Under the Plackett-Luce, and in particular the Bradley-Terry, preference framework, two
reward functions from the same class induce the same preference distribution.
Lemma 2. Two reward functions from the same equivalence class induce the same optimal policy... | Direct Preference Optimization |
body poses (“AGORA-50” and “CAPE-FP”), However, this
is not the case for images with out-of-distribution poses
(“CAPE-NFP”). This shows that, although conditioned on
GT SMPL-X fits, PaMIR∗ is still sensitive to global body
pose due to its global feature encoder, and fails to generalize
to out-of-distribution poses. On t... | ICON |
195 Can Large Language Models Democratize Access to Dual-use Biotechnology?, Soice et al., 2023
196 The Convergence of Artificial Intelligence and the Life Sciences: Safeguarding Technology, Rethinking
Governance, and Preventing Catastrophe, Nuclear Threat Initiative, forthcoming.
197 ChemCrow: Augmenting Large-La... | Capabilities and risks from frontier AI |
4.3. Refinement properties
We now turn our attention to transformation properties that are related to path refinement. We refer to these as refine-
ment properties. All of these properties are related to various types of state refinement of paths. Hence, the actual labels on
arcs are irrelevant.
Definition 16. Let G1 = (... | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
regulation, competition, and privatization of legacy
broadcast media
Lack of competition is what initially induced democratic countries to regulate
broadcast media, since the introduction of radio and then television offered new
channels of mass communication with limited bandwidth. This was true in the
United States ... | Social_Media_and_Democracy |
Misinformation, Disinformation, and Online Propaganda
27
would imply. A possible resolution to this apparent discrepancy is that some share
of the already-small fraction of the population that encounters misinformation
online engages with it frequently and repeatedly. This would explain the skewed
patterns of both co... | Social_Media_and_Democracy |
B gives background information on J.B. Pritzker without providing his address.
Table 8: GPT-4 chooses DPO over GT. Sample responses to a prompt from the Anthropic-HH test set. DPO
sample generated with temperature 1.0; GT is the chosen completion in the dataset of preferences. For clarity,
post-hoc annotations are inc... | Direct Preference Optimization |
Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., and Iwasawa,
Y. Large language models are zero-shot reasoners. arXiv
preprint arXiv:2205.11916, 2022.
Lester, B., Al-Rfou, R., and Constant, N. The power of scale
for parameter-efficient prompt tuning. arXiv preprint
arXiv:2104.08691, 2021.
Lewkowycz, A., Andreassen, A., Do... | PaLM-E- An Embodied Multimodal Language Model |
1
Manuscript submitted to ACM, 2023,
Draxler et al.
Studies on technology adaptation and acceptance typically find that age and gender predict technology usage
[27, 35, 43]. Thus, technological transformation does not penetrate society equally but is accelerated in some demographic
profiles. This amplifies societal... | Adoptionand AppropriationofLLMs |
level of conscientiousness (bottom-most trace in that graph represents level 1, while topmost
trace represents level 9). In the third row, as prompted levels of conscientiousness increase
from 1 to 9 (row-wise), median IPIP-NEO Conscientiousness scores increase monotonically
while scores for all other trait domains rem... | PersonalityTraitsinLargeLanguageModels |
in the self-supervised literature. In terms of models, we train a ViT model (Dosovitskiy
et al., 2020) with 1B parameters and distill it into a series of smaller models that surpass
the best available all-purpose features, OpenCLIP (Ilharco et al., 2021) on most of the
benchmarks at image and pixel levels. | DINOv2- Learning Robust Visual Features without Supervision |
2021.
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.
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, an... | Llama2 |
and product layers [33]. The size of a PC p, denoted |p|, is the number of edges in its DAG.
This paper focuses on two classes of PCs that support different types of queries: (i) PCs that allow
linear-time computation of marginal (MAR) and maximum-a-posterior (MAP) inferences (e.g.,
PSDDs [5], selective SPNs [34]); (ii... | Tractable Regularization of Probabilistic Circuits |
Why does misinformation linger post-correction? Scholars suggest two
potential reasons for the continued influence effect. First, according to the
mental model theory, individuals construct models of external events in their
heads, which they continuously update as new information becomes
available (Johnson and Seifert ... | Social_Media_and_Democracy |
Facebook also led to a resistance to the flags, with those par-
ticipants continuing to engage and keep their attitude ratings
higher than those who do not use Facebook for news. The
National Newspaper readers, on the other hand, were distinct
from those who do not read national newspapers because
they decreased th... | Use of bot and content flags to limit the spread of misinformation among social networks: a behavior and attitude survey |
Table 3: Multilingual zero-shot TTS results on filtered MLS test sets. GT/YT/VB-Multi refers to
ground truth/YourTTS/multilingual Voicebox. “Ref” column shows the audio context language.
GT
YT
VB-Multi
(α = 1.0)
Ref
-
De
En
Es
Fr
Pl
Pt
AVG
De
En
Es
Fr
Pl
Pt
AVG
Pt
De
En
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Fr
Pl
WER SIM-o WER SIM-o WER SIM-... | Voicebox-Text-GuidedMultilingual UniversalSpeechGenerationatScale |
Traveling to have a business meeting takes the fun out of the trip. Especially if you have to prepare a presentation. I would suggest holding the business plan meetings here then take a trip without any formal business meetings. I would even try and get some honest opinions on whether a trip is even desired or neces... | Multi-step Jailbreaking Privacy Attacks on ChatGPT |
Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan,
Melanie Kambadur, Sharan Narang, Aurelien Ro-
driguez, Robert Stojnic, Sergey Edunov, and Thomas
Scialom. 2023b. Llama 2: Open foundation and
fine-tuned chat models. | AppAgents |
2 TRACTABILITY MATTERS IN LOSSLESS COMPRESSION | LOSSLESS COMPRESSION WITH PROBABILISTIC CIRCUITS |
Figure 2. Our method can be trained without FLAME pseudo ground-truth supervision. With more diversed training data, IMavatar learns
more detailed expression and pose deformations. Neck geometry, however, is not guaranteed to be correct due to the lack of movement
and ambiguity between head and neck rotation in the tra... | I M Avatar- Implicit Morphable Head Avatars from Videos |
Marcus, G. F. (1998). Rethinking eliminative connectionism. Cogn Psychol, 37(3), 243-282.
Marcus, G. F., Pinker, S., Ullman, M., Hollander, M., Rosen, T. J., & Xu, F. (1992). Overregularization in
language acquisition. Monogr Soc Res Child Dev, 57(4), 1-182.
57
THE NEXT DECADE IN AI / GARY MARCUS
Ma... | The Next Decade in AI- |
40
Crypto has become a significant area of
academic research
500
400
300
200
100
Academic
Publications
Number of
crypto-related
academic publications
released during the
month. Based on a
keyword search for
"Cryptocurrency",
"Blockchain", "Bitcoin",
and "Ethereum".
0
2016
2017
2018
... | State-of-Crypto2023 |
Prompt
– Aerials, System Of A Down, Toxicity, 2001, 2 of 4
– Aloo Gobi, Weezer, OK Human, 2021, 1 of 4
– Bananas and Blow, Ween, White Pepper, 3 of 4
– Blue Light, Bloc Party, Silent Alarm, 2005, 1 of 4
– Break-Thru, Dirty Projectors, Lamp Lit Prose, 2018, 3 of
4
– B:/ Start Up, Blank Banshee, Blank Banshee 0, Future
F... | Moûsai |
5.6 Open Problems
Striking a Balance between Internalized Capabilities and External Tools. The future development of
foundation models for tool learning raises an intriguing question: should the capabilities of these models be
primarily internalized, or should they rely more heavily on external tools? Recent advances ... | Tool Learning with Foundation Models |
DPO is able to bypass both fitting an explicit reward and performing RL to learn the policy using
a single maximum likelihood objective. Note the optimization objective Eq. 5 is equivalent to a
θ (y|x)
Bradley-Terry model with a reward parameterization r∗(x, y) = β log π∗
πref(y|x) and we optimize our
parametric model ... | Direct Preference Optimization |
4.2 Data efficiency
Data efficiency represents how efficiently a training pipeline leverages its data. It
determines the number of iterations (steps) required to complete a training process,
thus affecting the overall training cost. Since existing LLMs such as LLaMA [2] are
usually trained on a large quantity of texts, maxim... | Beyond Efficiency |
To measure the inference accuracy, we adopt Hits@k. This metric allows
us to evaluate the generated chord progressions by calculating the ratio of the
reference chord presence among the top k candidate chords predicted by the
model, where k = 1, 3, and 5. In our case, the reference chord is the ground
32
truth ch... | Video2Music |
TANGO is not always able to finely control its generations over textual control prompts as it is trained
only on the small AudioCaps dataset. For example, the generations from TANGO for prompts Chop-
ping tomatoes on a wooden table and Chopping potatoes on a metal table are very similar. Chopping
vegetables on a table a... | Text-to-Audio Generation using Instruction-Tuned LLM and Latent Diffusion Model |
We show the overall violation percentage and safety rating of various LLMs in Figure 17. Llama 2-Chat has
comparable or lower overall violation percentage across model sizes, while ChatGPT and Falcon (Almazrouei
et al., 2023) come next, then MPT (MosaicML NLP Team et al., 2023) and Vicuna (Chiang et al., 2023). It is
i... | Llama2 |
Yikuan Li, Hanyin Wang, and Yuan Luo. A comparison of pre-trained vision-and-language models for multi-
modal representation learning across medical images and reports. In 2020 IEEE international conference
on bioinformatics and biomedicine (BIBM), pp. 1999–2004. IEEE, 2020.
Vladislav Lialin, Vijeta Deshpande, and Ann... | BiomedGPT |
[35] Matthew Tancik, Pratul P. Srinivasan, Ben Mildenhall,
Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Sing-
hal, Ravi Ramamoorthi, Jonathan T. Barron, and Ren
Ng. Fourier features let networks learn high frequency
functions in low dimensional domains. NeurIPS,
2020. 4
[36] Yoad Tewel, Rinon Gal, Gal Chechik, and Yu... | A Neural Space-Time Representation for Text-to-Image Personalization |
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... | Principal-agent VCG contracts - ScienceDirect |
formance gain of fine-tuned models and state that
the model may only pick up superficial patterns
during instruction tuning. Addressing the issue
of instruction format inconsistency, (Liang et al.,
2023) develop a format transfer framework UIT to
automatically transfer instructions from different
datasets into unified ... | DataManagementForLargeLanguageModels-ASurvey |
42 | TheRiseandPotentialofLargeLanguageModel BasedAgents |
In this section, we demonstrate the effectiveness of pre-
training modules. Table 5 shows the results of the same
setting for each model on the same data. It is important
to note that the evaluation data used in the table was
not seen during pretraining for a fair comparison. Over-
all, we observe that pretraining with... | BiomedGPT |
To mitigate hallucinations at the inference step, Rebuffel et al. [154] propose a Multi-Branch
Decoder that leverages word-level alignment labels between the input table and paired text to learn
the relevant parts of the training instance. These word-level labels are gained through dependency
parsing during the pre-pro... | SurveyofHallucinationinNatural Language Generation |
task, which will be further facilitated by our development of MuVi-Sync, a
unique multimodal dataset annotated with symbolic music (transcription and
chords) as well as a large array of video features, including semantic, scene
offset, motion, and emotion.
Our framework includes an Affective Multimodal Transformer ... | Video2Music |
weight matrix defined in 3DBiCar. Note that both 3DBiCar
and RaBit currently does not support the animation of tails,
which will be explored in our future work. As for texture
modeling, 1,050 raw textures from 3DBiCar were adopted
and extended to 21,000 training data with image-level aug-
mentations (e.g., flipping, an... | RaBit- Parametric Modeling of 3D Biped Cartoon Characters with a Topological-consistent Dataset |
Lucas Perry. Andrew Critch on AI Research Considerations for Human Existential
Safety. en-US. URL: https://futureoflife.org/2020/09/15/andrew-critch-on-ai-research-
considerations-for-human-existential-safety/ (visited on 04/29/2022).
Lucas Perry. Evan Hubinger on Inner Alignment, Outer Alignment, and Proposals for
Buil... | Is Power-Seeking AI an Existential Risk? |
20
ChatGPT | WizardLM- Empowering Large Language Models to Follow Complex Instructions |
contrast, diverse groups tend to perform better in solving problems even if the
average ability of their members is lower (Hong and Page 2004). | Social_Media_and_Democracy |
There are several ethical theories that differ in their approaches to moral decision making.
Some of the most prominent ones are:
1. Virtue Ethics: This theory emphasizes the development of moral character and virtues,
such as courage, honesty, and compassion. According to this theory, the best way to make
ethical deci... | WizardLM- Empowering Large Language Models to Follow Complex Instructions |
faced infrastructural challenges and a need for human coders to take on tasks
requiring more critical oversight (Leonard 1998). Researchers, however,
were quick to see their potential as “intelligent software” that could help
people better navigate, and even communicate, via the Internet (Weld and
Etzioni 1995). | Social_Media_and_Democracy |
Can we finetune the encoders? We question if the ability of our models to produce high quality frozen
features impact their performance when finetuned with supervision on a specific dataset. While this is not
core to this paper, this experiment is indicative of whether we have involuntarily specialized our models
to the s... | DINOv2- Learning Robust Visual Features without Supervision |
4thtitle,andtheirfifthinnineyears......ObservationFromBingRealMadridisthemostsuccessfulteaminEuropeanCupandChampionsLeaguehistory.Includingthe2021/22showpieceinParis,LosBlancoshavereached17finalsandhavewon13ofthose......Response:Accordingtothetwosources,RealMadridhaswontheChampionsLeagueadifferentnumberoftimes:Accordingt... | Tool Learning with Foundation Models |
IEEE.
Cheuk, K. W., Herremans, D., & Su, L. (2021). Reconvat: A semi-supervised
automatic music transcription framework for low-resource real-world data.
In Proceedings of the 29th ACM International Conference on Multimedia
(pp. 3918–3926).
Cheuk, K. W., Sawata, R., Uesaka, T., Murata, N., Takahashi, N., Takahashi... | Video2Music |
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