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Magar, I. and Schwartz, R. Data contamination: From
memorization to exploitation. In Proceedings of the 60th
Annual Meeting of the Association for Computational Lin-
guistics (Volume 2: Short Papers), pp. 157–165, Dublin,
Ireland, May 2022. Association for Computational Lin-
guistics. doi: 10.18653/v1/2022.acl-short.18... | Eight Things to Know about Large Language Models |
Parameter Efficient Finetuning (PEFT) method, most of the memory footprint for LLM finetuning
comes from activation gradients and not from the learned LoRA parameters. For a 7B LLaMA
model trained on FLAN v2 with a batch size of 1, with LoRA weights equivalent to commonly used
0.2% of the original model weights[28, 37]... | QLORA |
Shin, J., & Thorson, K. (2017). Partisan selective sharing: The biased diffusion of fact-
checking messages on social media: Sharing fact-checking messages on social media.
Journal of Communication, 67(2), 233–255. https://doi.org/10.1111/jcom.12284
Shu, S. B., & Carlson, K. A. (2014). When three charms but four alarms... | Social_Media_and_Democracy |
Oskar van der Wal Helped with the CrowS-Pairs evaluation and writing up the gender bias case study.
B. Corrections and Updates
Following the value of “doing science in the open” (Phang et al., 2022), we released a variety of artifacts over the course
of training our models for the public to use. However, after this in... | Pythia- A Suite for Analyzing Large Language Models Across Training and Scaling |
weights and is thus much more memory efficient at 21 GB vs 26 GB, providing a three percentage
points of improvement over Vicuna 13B. Furthermore, Guanaco 7B easily fits on modern phones at a
5 GB footprint while still scoring nearly 20 percentage points higher than Alpaca 13B.
However, Table 6 also has very wide confi... | QLORA |
reference models at a predictable rate, provided that the PM scores of the models’ responses are within the
range considered in these calibration studies. That said, we find significant failures of robustness as RLHF
optimizes towards much higher scores, as explained in Section 4.5 and Appendix B.4.
We might generally ex... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
C.7. Best settings for sampling
As we generate a large amount (≥ 1M) of samples for each problem, the exact settings to use for
sampling can potentially have a large impact on the model performance.
In Figure A5(a-b), we show that sampling temperature does have an impact on the solve rate, but the
temperature of 𝑇 = 0... | alphacode |
Therefore, the integration of RLSP into the fine-tuning pro-
cess of FinGPT provides a powerful tool for improving the
model’s financial market understanding and predictive accu-
racy. By using actual stock price movements as feedback, we
are directly harnessing the wisdom of the market to make our
model more effective... | FinGPT-Open-SourceFinancialLargeLanguageModels |
n , and define the maximum leaf diameter mt := max(cid:96)∈[L(t)] diam(X(cid:96)).We
Let [L(t)] be the leaves of the discriminator f (t)
n if the model places them in the same leaf. We show that, as n, t → ∞, conditional
say that two samples are neighbors in f (t)
probabilities for neighboring samples converge—including... | Adversarial Random Forests for Density Estimation and Generative Modeling |
Controlling Editability. Thanks to our importance-based
ordering over our mapper’s hidden representation, we can
control our dimensionality at inference time. In Figure 11
we gradually change the strength of our dropout to show
how this affects the generated image’s visual and text fi-
delity. When a stronger dropout i... | A Neural Space-Time Representation for Text-to-Image Personalization |
• Naively construed, I can imagine arguments given about the usefulness/necessity of agentic
planning and strategic awareness making the wrong predictions about current systems (or,
e.g., about animal behaviors like squirrels burying nuts for the winter). Thus, for example,
one might have expected writing complex code ... | Is Power-Seeking AI an Existential Risk? |
7/13
Table 2. Nearest neighbor analysis for understanding predictions of media diet models. For the filled-in prompt “The coronavirus outbreak is
a [minor] threat to the health of the U.S. population.”, we show the top 10 most semantically similar sentences in the training sets for
CNN/FOX media diet models. The FOX m... | Language models trained on media diets can predict public opinion |
Francesco Fusco, Damian Pascual, and Peter Staar. pNLP-Mixer: An Efficient all-MLP Architecture
for Language. arxiv:2202.04350 [cs], February 2022. URL https://arxiv.org/abs/22
02.04350v1.
Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason
Phang, Horace He, Anish Thite, Noa Nabes... | CRAMMING-TRAININGALANGUAGEMODELONA SINGLEGPUINONEDAY |
3.2 The Contemporary AI Art Scene | UNDERSTANDINGANDCREATINGARTWITHAI-REVIEWAND OUTLOOK |
The 2016 presidential election in the United States and, to a lesser extent, the
Brexit referendum earlier that year in the United Kingdom, changed the received
wisdom. Looking for an explanation for those surprising results, many turned
to the new technology of political communication. Blame was (and continues to
be) ... | Social_Media_and_Democracy |
beams for the marginals. We refer to this decoding procedure as “Thorough Decoding.” For longer
output sequences, |Y | can become large, requiring many forward passes. For more efficient decoding,
we can make a further approximation that pθ(y|x, zi) ≈ 0 where y was not generated during beam
search from x, zi. This avoid... | Retrieval-AugmentedGenerationfor Knowledge-IntensiveNLPTasks |
B. COMPUTATIONAL AESTHETICS
Computational aesthetics is a growing field of interest within
the computer vision community and is mainly preoccu-
pied with developing computational methods that can pre-
dict aesthetic judgments in a similar manner as humans.
Although some studies have addressed the topic of compu-
tationa... | A_Deep_Learning_Perspective_on_Beauty_Sentiment_and_Remembrance_of_Art |
The evaluation set is formed by 10,000 random samples drawn from the test split of the dataset, following the approach
proposed by Rae et al. (2021). The few-shot examples are taken from the train split, keeping a balanced number of toxic
and non-toxic examples. The primary metric is AUC-ROC, obtained using the normali... | PaLM 2 Technical Report |
PaLM
Google Translate
PaLM 2
78.5
80.2
81.1
76.1
75.3
78.3
70.3
72.3
74.4
68.6
68.5
72.0
Regional translation experimental setup We also report results on the FRMT benchmark (Riley et al., 2023) for
Few-shot Regional Machine Translation. By focusing on region-specific dialects, FRMT allows us to measure PaLM
2’s ab... | PaLM 2 Technical Report |
[32] A. Chowdhery, S. Narang, J. Devlin, M. Bosma, G. Mishra, A. Roberts, P. Barham, H. W.
Chung, C. Sutton, S. Gehrmann, et al. Palm: Scaling language modeling with pathways. arXiv
preprint arXiv:2204.02311, 2022.
[33] M. Ahn, A. Brohan, N. Brown, Y. Chebotar, O. Cortes, B. David, C. Finn, K. Gopalakrishnan,
K. Hausm... | LLM+P- Empowering Large Language Models with Optimal Planning Proficiency |
Tools can expand the action space of LLM-based agents. With the help of tools, agents can utilize
various external resources such as web applications and other LMs during the reasoning and planning
phase [92]. This process can provide information with high expertise, reliability, diversity, and quality
for LLM-based ag... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
• Embedding Layers. The embedding layer is the foundational component of a
Transformer model, serving as the initial step in transforming raw input data
into a format that can be effectively processed. It maps discrete tokens, such
as words or subwords, into continuous vector representations, often referred to
as word e... | Beyond Efficiency |
chatgpt on reasoning, hallucination, and interactivity. CoRR, abs/2302.04023, 2023.
[133] Fang, T., S. Yang, K. Lan, et al. Is chatgpt a highly fluent grammatical error correction system?
A comprehensive evaluation. CoRR, abs/2304.01746, 2023.
[134] Lu, A., H. Zhang, Y. Zhang, et al. Bounding the capabilities of lar... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
transformer-based systems on outdoor VLN tasks:
• Priority map module Our novel PM-VLN module con-
ducts a hierarchical process of high-level alignment of
textual spans with visual perspectives and feature-level
operations on inputs during navigation (see Figure 3). | APriorityMapforVision-and-LanguageNavigation withTrajectoryPlansandFeature-LocationCues |
Table 3. Visual Question Answering results on OK-VQA, compared with existing methods that use different knowledge sources. For
the memory cost, we assume all models use bfloat16. Green means on-device model parameters that are learnable, Blue means on-device
memory of frozen model parameters, and Red means CPU/disk sto... | REVEAL-Retrieval-AugmentedVisual-LanguagePre-Trainingwith Multi-SourceMultimodalKnowledgeMemory |
) are disjoint.
8. Metric properties
In this section we will augment our framework with metric properties for admissibility, in addition to the previous
qualitative ones for refinement, and investigate how these properties relate to each other. The results we will prove in this
section are summariz... | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
Shaping a Single LLM Personality Domain
In the first study, we tested if LLM-simulated Big Five personality domains (measured by
the IPIP-NEO and LLM-generated text) can be independently shaped. The prompts were
constructed as follows: first, we created sets of prompts for each Big Five trait designed
to shape each tra... | PersonalityTraitsinLargeLanguageModels |
3.7.2 Evaluation without labels
As we just discussed, most evaluations rely on the use of labels and training an auxiliary
model. This can make evaluations expensive and sensitive to hyperparameters or their
optimizations. To help alleviate these issues multiple methods have been proposed to
evaluate or help tune hyper... | A Cookbook of Self-Supervised Learning |
sha1_base64="wKKE7yVAX2LXfl0fCkkuip40484=">AAAB9XicbVDLSgMxFL3js9ZX1aWbYBHERZkRQZcFNy4r2Ie005JJM21oJjMkd5Qy9D/cuFDErf/izr8xbWehrQcCh3Pu5Z6cIJHCoOt+Oyura+sbm4Wt4vbO7t5+6eCwYeJUM15nsYx1K6CGS6F4HQVK3ko0p1EgeTMY3Uz95iPXRsTqHscJ9yM6UCIUjKKVup2I4jAIs9ake94TvVLZrbgzkGXi5aQMOWq90lenH7M04gqZpMa0PTdBP6MaBZN8UuykhieUjeiAty1VNOLGz... | BANMo- Building Animatable 3D Neural Models from Many Casual Videos |
Our work builds on top of the work of many, many teams at Google. We’d especially like to recognize the Pax team,
the Pathways infrastructure team, the Sax team, AIDA team, the JAX team, the Flaxformer team, the XLA team, the
Plaque team, the Borg team, and the Datacenter networking infrastructure team. We gratefully a... | PaLM 2 Technical Report |
Systematically measuring the impact of online hate speech is challenging (Sellars
2016), but a diverse body of research suggests that online hate speech has
serious offline consequences both for individuals and for groups. Surveys of
internet users indicate that exposure to online hate speech may cause fear
(Hinduja and... | Social_Media_and_Democracy |
Bidirectional RNNs.
For numerous tasks in speech processing, it is more effective to process
the whole utterance at once. For instance, in speech recognition, one-shot input transcription can
be more robust than transcribing based on the partial (i.e. previous) context information [161]. The
vanilla RNN has a limitati... | AReviewofDeepLearningTechniquesforSpeechProcessing |
6 CONCLUSION
Our research shows that LLMs are not yet capable of self-correcting their reasoning. This implies
that expecting these models to inherently recognize and rectify their inaccuracies might be overly
optimistic, at least with the current state of technology. More broadly, this underscores the need for
ongoin... | LARGELANGUAGEMODELSCANNOTSELF-CORRECT REASONINGYET |
LPIPS ↓
0.03421
0.03227
0.02231
0.02781
0.02156
0.02155
0.02085
Table 2. Quantitative results on real videos. We compare our
method with the SOTA and baselines on test sequences with un-
seen expressions and poses. Our method reconstructs the expres-
sions more accurately while being on par in terms of image quality.
... | I M Avatar- Implicit Morphable Head Avatars from Videos |
adiversearrayofexternaltoolstoenhancetheircapabilities.SuchamodularapproachcouldfacilitateamoreflexibleandcustomizableAIsystem,allowingforrapidexpansionofmodelcapabilitiestoaddressvarioustasks.Conversely,foundationmodelshaveincreasinglydisplayedtheabilitytointernalizeandperformmanyAItasksthatpreviouslyrequiredseparateto... | Tool Learning with Foundation Models |
In addition to FEVER, we train and evaluate
ProoFVer on two other related datasets. First, we
use Symmetric FEVER (Schuster et al., 2019), a
dataset designed to assess the robustness of fact
verification systems against the claim-only bias
present in FEVER. The dataset consists of 1,420
counterfactual instances, split ... | ProoFVer- Natural Logic Theorem Proving for Fact Verification |
nˆmK1accordingtoEq.5,producinganewlatentmapsequence˜zK1={˜z1,...,˜zK}.Finally,each˜zin˜zK1isfurtherfedtotheimagedecoderΩforsynthesizingeachnewframeˆxinoutputvideoˆxK1.NotethatflowpredictorFisnotrequiredduringinference.4.Experiments4.1.DatasetsandMetricsWeconductcomprehensiveexperimentsonthefollow-ingvideodatasets.MUGfac... | Conditional Image-to-Video Generation with Latent Flow Diffusion Models |
Sadly it happens more often that what I would like to admit. A whiteout happens every now and
then and could be deadly!
How many people died trying to climb you recently?
In 2019, 11 people died on Everest during a record season with huge number of climbers. Videos
shared on social media showcased climbers waiting in l... | LaMDA- Language Models for Dialog Applications |
Principal-agent VCG contracts - ScienceDirect
Seungjin Han
Econ. Lett. (2015)
Seungjin Han
J. Econ. Theory (2007)
Hu Fu et al.
Econ. Lett. (2017)
Sushil Bikhchandani
https://www.sciencedirect.com/science/article/abs/pii/S0022053122000333?via%3Dihub
5/7 | Principal-agent VCG contracts - ScienceDirect |
Application Decisions
3.9.1 Offer of an Undergraduate Place
1. UCL endeavours to ensure that all applicants who have applied by the October or January equal
2.
consideration deadline will receive a decision via UCAS by the end of April in the calendar year
of proposed entry, or a calendar year ahead for defe... | UCL Academic Manual |
7. Conclusions | Knowledge graphs as tools for explainable machine learning: A survey |
1 # add LoRA to the textual module of Qwen-VL
2 QWenLMHeadModel(
(transformer): QWenModel(
(wte): Embedding(151936, 4096)
(drop): Dropout(p=0.0, inplace=False)
(rotary_emb): RotaryEmbedding()
(h): ModuleList(
(0-31): 32 x QWenBlock(
(ln_1): RMSNorm()
(attn): QWenAttention(
3
4
5
6
7
8
9
10
11
12
13
14
... | Let’sThinkOutsidetheBox |
labels). All arcs remain, if we ignore the labels, so this is an RRAa abstraction. Fig. 7 (right) illustrates the effect of removing
action c instead. Then there is no longer any arc from {u, v} to {u, v}. However, there is still a path from {u, v} to {u, v}, so
this is an RRAb abstraction. | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
SSL has been used not only to improve sample efficiency, but also to improve exploration.
Guo et al. [2022b] propose BYOL-Explore which uses BYOL [Grill et al., 2020] to learn
the encoder and the forward model, and use the forward model disagreement as the
exploration objective. The follow-up work by ? address the proble... | A Cookbook of Self-Supervised Learning |
5 Result and Discussions
In this section, we provide evaluation result and
discussion of LaMini-LM for both the downstream
NLP tasks and human evaluation on user-oriented
instruction. For NLP downstream task, larger mod-
els yield better average performance, as seen in
Figure 5. Therefore to save space, we present the... | LaMini-LM- A Diverse Herd of Distilled Models from Large-Scale Instructions |
[25] Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M.,
Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the
future of large language models. Meta-Radiology, 100017 (2023)
[26] Yang, J., Jin, H., Tang, R., Han, X., Feng, Q., Jiang, H., Yin, B., Hu, X.:
Harnessi... | Beyond Efficiency |
with Generative Environment Models for RL. arXiv, 1906.09237v2.
Gupta, N., Lin, K., Roth, D., Singh, S., & Gardner, M. (2019). Neural Module Networks for Reasoning over
Text. arXiv, 1912.04971v1.
Ha, D., & Schmidhuber, J. (2018). World Models. arXiv, 1803.10122v4.
Henaff, M., Weston, J., Szlam, A., Bordes, A., &... | The Next Decade in AI- |
On a good day, a system like the widely discussed neural network GPT-2, which
produces stories and the like given sentence fragments, can convey something that
ostensibly seems to reflect a deep understanding. Given, for example, a sentence
fragment (in bold) like, "Two soldiers walked into a bar", it can often gene... | The Next Decade in AI- |
Yizhong Wang, Swaroop Mishra, Pegah Alipoormo-
labashi, Yeganeh Kordi, Amirreza Mirzaei, Atharva
Naik, Arjun Ashok, Arut Selvan Dhanasekaran, An-
jana Arunkumar, David Stap, Eshaan Pathak, Gi-
annis Karamanolakis, Haizhi Lai, Ishan Purohit,
Ishani Mondal, Jacob Anderson, Kirby Kuznia,
Krima Doshi, Kuntal Kumar Pal, Mai... | LaMini-LM- A Diverse Herd of Distilled Models from Large-Scale Instructions |
though options for misaligned power-seeking are open.
Human society relies heavily on controlling the options and incentives of agents with imperfectly
aligned objectives. Thus: suppose I seek money for myself, and Bob seeks money for Bob. This need
not be a problem when I hire Bob as a contractor. Rather: I pay him fo... | Is Power-Seeking AI an Existential Risk? |
us ambiguous clusters containing both correct and incorrect samples. Additionally, even correct
solutions may be put into multiple clusters, as their behaviour on invalid test inputs may still differ.
We tuned two hyperparameters for clustering on the evalidation set: the number of test inputs to use
in clustering as we... | alphacode |
videos. The scarcity of existing models and the underlying data gap prompted us
to undertake the creation of a new dataset and develop our Affective Multimodal
Transformer (AMT) model, with the aim of pushing the boundaries of music
generation for video.
Our approach starts by collecting a dataset of popular music ... | Video2Music |
[10] Tim Brooks, Aleksander Holynski, and Alexei A Efros. In-
structpix2pix: Learning to follow image editing instructions.
arXiv preprint arXiv:2211.09800, 2022. 2, 3
[11] John Canny. A computational approach to edge detection.
IEEE Transactions on Pattern Analysis and Machine Intelli-
gence, (6):679–698, 1986. 6
[1... | AddingConditionalControltoText-to-ImageDiffusionModels |
For fine-tuning in specific downstream tasks, researchers have innovated task-specific difficulty metrics. A notable example
is in paraphrase generation, where Kadotani et al. [122] proposed using the edit distance between paraphrased sentence
pairs as a metric, approximating the extent of required transformations. The... | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
[210] Raffel, C., N. Shazeer, A. Roberts, et al. Exploring the limits of transfer learning with a unified
text-to-text transformer. The Journal of Machine Learning Research, 21(1):5485–5551, 2020.
[211] Ge, Y., W. Hua, J. Ji, et al. Openagi: When LLM meets domain experts. CoRR, abs/2304.04370,
2023.
2023.
[212] Raj... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
Structured knowledge in the form of domain ontologies was investigated with the idea that it could to support (or
potentially replace) experts in this data interpretation step – cfr. seminal work of [39] to translate the outputs of a neural
network into symbolic knowledge using a domain ontology in ... | Knowledge graphs as tools for explainable machine learning: A survey |
Results. First, we look into the percentage of toxic responses disaggregated by languages and identity groups to
analyze potential toxic language harms. We observe that dialog-prompting is effective at controlling toxicity for most
of the languages, except for English, German and Portugeuse, where the toxicity rates an... | PaLM 2 Technical Report |
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Table 5: The details of the prompt design in HuggingGPT. In the prompts, we set some injectable
slots such as {{ Demonstrations }} and {{ Candidate Models }}. These slots are uniformly replaced
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contain extensive contextual information. There-
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topi... | LaMini-LM- A Diverse Herd of Distilled Models from Large-Scale Instructions |
Personal and Psychological Factors
Misinformation, however, is not contained to the political sphere. More basic
personal and psychological factors may predispose certain individuals to
champion misinformation and disavow corrections across domains. We
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Flan-U-PaLM
Flan-PaLM
540B PaLM
540B U-PaLM
3B
11B
8B
62B
62B
CoT
27.2
38.0
52.4
38.8
5.2
11.2
2.4
18.8
8.0
21.6
15.2
28.4
18.0
51.2
14.8
12.8
20.0
34.0
26.0
30.8
43.6
48.4
44.4
46.4
Direct
38.0
45.2
62.0
52.8
31.6
30.8
29.6
44.4
32.4
50.8
35.2
60.8
36.8
71.2
35.6
47.6
36.8
74.0
50.0
70.0
63.6
85.6
60.4
91.2
... | Scaling Instruction-Finetuned Language Models |
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1. A light-weight extension to LLMs designed for understanding visual documents.
2. A disentangled spatial attention mechanism that captures cross-alignment between text and layout modalities.
3. An infilling pre-training objective tailored to address irregular layouts effectively.
4. An instruction-tuning dataset s... | DOCLLM |
mitigating error avalanching. In SECToR, simplify-then-guess generates K separate guesses for an
addition problem by applying between 1 and K simplification steps before fast adding the remaining
addition problem. It then takes a majority vote between the generated guesses to construct a final
guess for the answer. In ... | CHAIN-OF-THOUGHTREASONING IS APOLICY IMPROVEMENTOPERATOR |
videos. The original keys have already recorded the infor-
mation on video colors, so color features are unnecessary
for video-music correspondence modeling. If we remove
remove the influence of tonality by changing keys, we need
color features to capture the video colors.
Ablation on Music Generator. We further conduc... | VideoBackgroundMusicGeneration |
28
Mehrish et al.
𝑇(cid:214)
𝑇(cid:214)
Fig. 7. The Diffusion Probabilistic Model is a generative model that progressively transforms a noise distribu-
tion into the target data distribution through a series of diffusion steps, where the noise level decreases as
the process continues. The model is trained by maxi... | AReviewofDeepLearningTechniquesforSpeechProcessing |
Broader forms of transparency can also go beyond purely voluntary, self-
regulatory processes and be mandated by either government regulation or by
membership in certain organizations or institutions. Along with a commitment
to public transparency via transparency reports, the GNI requires independent
third-party asses... | Social_Media_and_Democracy |
324
Nathaniel Persily & Joshua A. Tucker
incentives do not always run in the direction of publication of research results,
whatever the conclusions. | Social_Media_and_Democracy |
gain and maintain various types of power in some circumstances, and especially to the extent they
have the capabilities and opportunities to get, use, and maintain that power with comparatively little
cost. Thus, for most humans, it makes little sense to devote themselves to starting a billion dollar
company—the return... | Is Power-Seeking AI an Existential Risk? |
9.1.2 Memory
• Number of parameters represents the number of adjustable variables in the
LLM’s neural network. A higher number of model parameters generally indicates a
more complex model with a greater capacity to learn and represent intricate patterns
32
in the data [11, 65]. However, this complexity often comes ... | Beyond Efficiency |
rapid increase can quickly fill the instruction set with extremely complex instructions, damaging the
generalization performance of models trained on this instruction set. To control the rate of difficulty
increase, we limit each evolving to be "a bit harder" and restrict adding a maximum of 10 to 20 words.
Of the five ty... | WizardLM- Empowering Large Language Models to Follow Complex Instructions |
Working Paper 26946, National Bureau of Economic Research (2020). 10.3386/w26946.
33. Simonov, A., Sacher, S. K., Dubé, J.-P. H. & Biswas, S. The persuasive effect of fox news: Non-compliance with
social distancing during the covid-19 pandemic. Working Paper 27237, National Bureau of Economic Research (2020).
10.3386/... | Language models trained on media diets can predict public opinion |
Our most capable model, Gemini Ultra, achieves new state-of-the-art results in 30 of 32 benchmarks
we report on, including 10 of 12 popular text and reasoning benchmarks, 9 of 9 image understanding
benchmarks, 6 of 6 video understanding benchmarks, and 5 of 5 speech recognition and speech
translation benchmarks. Gemini... | gemini_1_report |
REFERENCES
[1] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-
training of deep bidirectional transformers for language understanding,”
in Proc. Conf. North Amer. Chapter Assoc. Comput. Linguistics: Hum.
Lang. Technol., 2019, pp. 4171–4186.
[2] Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy... | Parameter-EfficientFine-TuningMethods |
Lrecon = (cid:107)xmel − ˆxmel(cid:107)1
(2)
This can be viewed as maximum likelihood estimation as-
suming a Laplace distribution for the data distribution and
ignoring constant terms. We define the reconstruction loss in
the mel-spectrogram domain to improve the perceptual qual-
ity by using a mel-scale that approxim... | ConditionalVariationalAutoencoderwithAdversarialLearningfor End-to-EndText-to-Speech |
(film)), (Steven T Segle, creator, Baymax), (Big Hero 6 (film), serires, Baymax)
Baymax is a character in Big Hero 6 which stars Alan Tudyk. He was created by Steven T. Seagle and the
American, Duncan Rouleau.
Alan Tudyk stars in the film Big Hero 6 in which Baymax is a character created by Steven T. Seagle and the
Americ... | Prefix-Tuning |
Audio + Text. We curated a new dataset, Freesound 500K, by crawling 500K audio samples together
with tags and descriptions from the Freesound website. We also use AudioSet [42] with 2 million
human-labeled 10-second sound clips from YouTube videos and AudioCaps [24] with 46K audio-
text pairs derived from the AudioSet ... | Any-to-Any Generation via Composable Diffusion |
Algorithm 2 Create embedding table
Require: d, map
1: E|Σ|×d ∼ U (0, 1)
2: procedure EMBED(s, map)
i ← map(s)
3:
return Ei
4:
5: end procedure
(cid:46) Dimensions, vocabulary
(cid:46) Uniform random distribution
To keep the method tractable, we use the common practice of choosing a threshold to determine
the minimum ... | MULTI HASH EMBEDDINGS IN SPACY |
academia and industry. We also expect the design of our model can inspire the whole community and
pave a new way for LLMs towards more advanced AI. | HuggingGPT- Solving AI Tasks with ChatGPT and its Friends in Hugging Face |
Mohammad Taher Pilehvar and Jose Camacho-
Collados. 2019. WiC: the word-in-context dataset
for evaluating context-sensitive meaning represen-
In Proceedings of the 2019 Conference
tations.
of the North American Chapter of the Association
for Computational Linguistics: Human Language
Technologies, Volume 1 (Long and Sh... | LaMini-LM- A Diverse Herd of Distilled Models from Large-Scale Instructions |
tential of LLM-based agents, and expedite the development
of more generalist agents.
In this work, we introduce JARVIS-1, a brand new agent
that can robustly produce plans for long-horizon tasks from
multimodal user and environment inputs, and translate them
into motor control in Minecraft, a popular yet challenging
op... | JARVIS-1 |
a reduced representation. We discuss more about
this tradeoff in Appendix D.5. The diffusion au-
toencoder only uses ResNet and modulation items
with the repetitions [1, 2, 2, 2, 2, 2, 2]. We do not
use attention, to allow decoding of variable and
possibly very long latent representations. Channel
injection only happen... | Moûsai |
17 | METAMATH |
• We establish the critical role of instruction-tuning in the efficacy of MoE models:
– We demonstrate that in the absence of instruction tuning, MoE models fall short in
performance when compared to dense models on downstream tasks.
– We highlight that when supplemented with instruction tuning, MoE models exceed th... | Mixture-of-Experts |
Doctoral Researcher to join the Human-Computer Interaction with a
focus on Human-AI Interaction
We are searching for a full-time Doctoral Researcher working Engineering
Psychology Group. The research will focus on user studies and intelligent
systems. This position will be funded based on an initial 2-year contract +
... | Doctoral researcher position in Human-Computer Interaction _ Human-AI Interaction _ Aalto University |
[33] Keunhong Park, Utkarsh Sinha, Jonathan T. Barron, Sofien
Bouaziz, Dan B Goldman, Steven M. Seitz, and Ricardo
Martin-Brualla. Nerfies: Deformable neural radiance fields.
In ICCV, 2021. 2, 3, 6, 7, 8
[34] Keunhong Park, Utkarsh Sinha, Peter Hedman, Jonathan T.
Barron, Sofien Bouaziz, Dan B Goldman, Ricardo Martin... | BANMo- Building Animatable 3D Neural Models from Many Casual Videos |
Table 2. Performance comparison of different models on LongBench. * indicates the results reported by LongBench. *indicates the
results are reported by CLEX (Chen et al., 2023a). + indicates the result is from us. Models in green are based on Llama2-7b, models
in blue are based on Mistral-7b, and models in orange are b... | Self-Extend LLM |
7 N AT U R A L L A N G U A G E E VA L U AT I O N
Although the StarCoder models are principally developed to be Code LLMs, they have also been
trained on a significant amount of natural language text. Roughly 20% of its training tokens are
natural language data: 7% GitHub issues, 10% Markdown, 2% Jupyter notebooks, and... | StarCoder_paper (1) |
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... | Language models can explain neurons in language models |
Input: 南 京 高 淳 县 住 房 和 城 乡 建 设 局 城 市 新
区 设 计 a plane of reference Gaochun is
one of seven districts of the provincial
capital Nanjing
Output: [MT(南京高淳县住房和城乡建设局 城市新
区 设 计)] a plane of reference Gaochun is
one of seven districts of the provincial
capital Nanjing
Input: x
Output:
Calendar We use the following prompt for... | Toolformer |
5.2 Property Analysis
Comparing the overall properties of various models
in Table 2, we see a set of impressive properties
of the Moûsai model: (1) We are among the very
few that can control music generation easily by text
descriptions of the type of music we want, as most
other models do not take text as input (van de... | MOUSAI |
Code Llama - Python 7B to outperform even Code Llama 13B on MBPP and HumanEval. For the APPS
benchmark, the prompts are much less direct and more complex compared to MBPP and HumanEval. Our
Code Llama - Python models show slightly decreased performance on the introductory and interview level
problems, where understandi... | CodeLlama2 |
While we do not leverage any external
sources or tools in our experiments, we
follow previous works in using the correct
label to determine when to stop the self-
correction loop. In a realistic setting, es-
pecially when aiming to employ LLMs to
solve math problems, the correct answer
is unknown to us. As a result, th... | LARGELANGUAGEMODELSCANNOTSELF-CORRECT REASONINGYET |
[8] Jonathan Ho, William Chan, Chitwan Saharia, Jay Whang,
Ruiqi Gao, Alexey Gritsenko, Diederik P Kingma, Ben
Poole, Mohammad Norouzi, David J Fleet, et al.
Imagen
video: High definition video generation with diffusion mod-
els. arXiv preprint arXiv:2210.02303, 2022. 2
[9] Jonathan Ho, Tim Salimans, Alexey A. Gritsenk... | LDM3D- Latent Diffusion Model for 3D |
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