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[15] Mandar Joshi, Eunsol Choi, Daniel S Weld, and Luke Zettlemoyer. Triviaqa: A large
scale distantly supervised challenge dataset for reading comprehension. arXiv preprint
arXiv:1705.03551, 2017.
[16] Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris
Alberti, Danielle Epstei... | Mistral7B |
Rhythm and Genre. We use kernel density estimate (KDE)
to visualize the distribution of beats per minute (BPM) of mu-
sic in various genres. As shown in Fig. 3 (a), Dance and Hip-
Hop/Rap tend to have higher BPM. Curves in other genres
display two distinct peaks, representing the BPM of slow-
paced and fast-paced songs... | VideoBackgroundMusicGeneration |
Ethnicity
Ethnicity
Ethnicity
Ethnicity
Ethnicity
Ethnicity
Ethnicity
Ethnicity
Ethnicity
Education
Education
Education
Education
LGBTQ+
LGBTQ+
LGBTQ+
LGBTQ+
Disability 6
Disability
Disability
Cohort
Female
Male
Nonbinary
Prefer not to Answer
18-24
25-34
35-44
45-54
55-64
65+
Middle Eastern or North African
Asia... | LaMDA- Language Models for Dialog Applications |
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... | An overview of Bard- an early experiment with generative AI |
Xi Chen, Josip Djolonga, Piotr Padlewski, Basil Mustafa, Soravit Changpinyo, Jialin Wu, Car-
los Riquelme Ruiz, Sebastian Goodman, Xiao Wang, Yi Tay, Siamak Shakeri, Mostafa Dehghani,
Daniel Salz, Mario Lucic, Michael Tschannen, Arsha Nagrani, Hexiang Hu, Mandar Joshi, Bo Pang,
Ceslee Montgomery, Paulina Pietrzyk, Marv... | gemini_1_report |
Timnit Gebru, Jamie Morgenstern, Briana Vecchione,
Jennifer Wortman Vaughan, Hanna Wallach, Hal
Daumé III, and Kate Crawford. 2018. Datasheets for
datasets. arXiv preprint arXiv:1803.09010.
Aaron Gokaslan and Vanya Cohen. 2019. Openweb-
text corpus. http://Skylion007.github.io/
OpenWebTextCorpus.
Authors Guild v. Goo... | The Pile- An 800GB Dataset of Diverse Text for Language Modeling |
Leo Gao, Jonathan Tow, Stella Biderman, Sid Black, Anthony DiPofi, Charles Foster, Laurence
Golding, Jeffrey Hsu, Kyle McDonell, Niklas Muennighoff, Jason Phang, Laria Reynolds, Eric
Tang, Anish Thite, Ben Wang, Kevin Wang, and Andy Zou. A framework for few-shot language
model evaluation, September 2021b. URL https://d... | StarCoder_paper (1) |
grammar rules for action recognition.
Journal of Computer Vision, 93(2):162–182.
Wei, X. K. and Chai, J. (2009). Modeling 3d human poses
In Computer
from uncalibrated monocular images.
Vision, 2009 IEEE 12th International Conference on,
pages 1873–1880. IEEE.
Wren, C. R., Azarbayejani, A., Darrell, T., and Pentland,
... | VISAPP_HumanPoseEstimation |
Knoblock [65] took this method further in planning by removing the non-critical atoms everywhere, not only in precon-
ditions. This is a general and common technique otherwise known as variable projection. It is commonly used also in search
(cf. the article by Zilles and Holte [92]), as well as in other ar... | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
[72] Shazeer, N., Cheng, Y., Parmar, N., Tran, D., Vaswani, A., Koanantakool, P.,
Hawkins, P., Lee, H., Hong, M., Young, C., et al.: Mesh-tensorflow: Deep learn-
ing for supercomputers. Advances in neural information processing systems 31
(2018)
[73] Huang, Y., Cheng, Y., Bapna, A., Firat, O., Chen, D., Chen, M., Lee, ... | Beyond Efficiency |
Teams also incorporated multiple approaches
to reduce risk by building functional limits, such
as maximum throttle pressure or heat levels into
the code, and put all code through biweekly peer
reviews and multiweek testing. McKinsey’s risk-
dynamics experts worked with the team to test
assumptions, review code, an... | an-ai-power-play-fueling-the-next-wave-of-innovation-in-the-energy-sector-may-2022 |
larger models generally perform better. Interest-
ingly, encoder-decoder models from T5 are per-
forming exceptionally well, given their rather small
size.
Encoder-Decoder vs.
Decoder-Only The
encoder-decoder LaMini language models (LaMini-
T5 series and LaMini-Flan-T5 series) outper-
form the decoder-only LaMini langu... | LaMini-LM- A Diverse Herd of Distilled Models from Large-Scale Instructions |
On the other hand, Semantic Web technologies are used to structure data, extract the features and relationships in a
system, and explain a model through reasoning using common vocabularies and ontologies [5]. In a semantic model,
ontologies can be applied to represent knowledge hierarchically via classes of entities (c... | Knowledge-graph-based explainable AI- A systematic review |
that evaluation problems are truly unseen during training, so difficult problems cannot be solved
by copying from the training set. Towards this goal, we release a new training and evaluation
competitive programming dataset, CodeContests1 (Section 3). This dataset combines data from
various sources, splits temporally so ... | alphacode |
tures produce a multi-embedding of a word. In this technical report we lay out a
bit of history and introduce the embedding methods in spaCy in detail. Second,
we critically evaluate the hash embedding architecture with multi-embeddings on
Named Entity Recognition datasets from a variety of domains and languages. The
e... | MULTI HASH EMBEDDINGS IN SPACY |
LEXICONHidden LayerInput Layer:
Modify representationsOutput Layer:
Form error signalInput patternTarget patternNew representations at the same time, Turian et al. (2010) showed that adding word embeddings—as well as more tra-
ditional Brown clusters (Brown et al., 1992)—to linear structured models of chunking and name... | MULTI HASH EMBEDDINGS IN SPACY |
action, only its resulting (stochastic) outcome. The contractual payment scheme thus depends on
the outcome, which serves as a noisy indicator of the action. This crucial feature of the model is
known as hidden action or moral hazard. For example, employers of a freelancer are not aware of
how much effort he invests,... | Incomplete Information VCG Contracts for Common Agency |
Addition-based Relative Positional Encoding. Relative positional encoding methods utilize the relative position between
two tokens rather than the absolute position of a single token. Some of them encode the relative positions and add the encoded
positions to the subsequent attention, referring to addition-based relati... | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
PROMPT FOR MATH WORD PROBLEMS
Q: There are 15 trees in the grove. Grove workers will plant trees in the grove today. After they are done, there
will be 21 trees. How many trees did the grove workers plant today?
A: There are 15 trees originally. Then there were 21 trees after some more were planted. So there must have
... | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models |
on some standard benchmarks. Yet, it is not a straightforward uphill battle for open-source LLMs.
The landscape is constantly evolving: closed-source LLMs are updated by retraining on newer data
regularly, open-source LLMs are released to catch up, and there is a myriad of evaluation datasets and
benchmarks being used ... | ChatGPT’sOne-yearAnniversary-AreOpen-Source LargeLanguageModelsCatchingup |
The remarkable success of ChatGPT has generated substantial research interest in LLMs across academia and industry.
Subsequently, numerous LLMs have been introduced starting from text-based LLMs [16, 17, 4, 18] to multimodal
LLMs [19, 20, 21, 22, 23]. In this section, we review these recent advances in LLMs and discuss... | DOCLLM |
B Detailed Results for The Pile
Per-domain perplexities for 8B models. Table 4 shows per-domain perplexities for 8B models
trained on the Pile. The reference/proxy models in this case are 70M, 150M, 280M, and 1B. DoReMi
improves the perplexity on each domain compared to the baseline domain weights.
17
Table 4: Per-... | DoReMi- Optimizing Data Mixtures Speeds Up Language Model Pretraining |
the English translation by scanning for the expression of grammatical gender in personal pronouns. We rely
on different evaluations sets for different language mixtures to take into account the multiple ways in which
different language encodes gender. | Scaling Instruction-Finetuned Language Models |
creating art, we address various practical and theoretical aspects of AI Art and consolidate related
works that deal with those topics in detail. Finally, we provide a concise outlook on the future
progression and potential impact of AI technologies on our understanding and creation of art. | UNDERSTANDINGANDCREATINGARTWITHAI-REVIEWAND OUTLOOK |
motion retargeting either in 2D [2, 7, 35, 43, 54, 67, 68] or
3D [19, 20, 25, 32, 49, 53, 69, 74]. The main difference be-
tween our method and those works is that we take as input
monocular video that contains complex human motions and
enable high-fidelity full 3D rendering. | HumanNeRF- Free-viewpoint Rendering of Moving People from Monocular Video |
Response reranking and selection for language models. Reranking is a common method to
improve the generation quality in language models by sampling multiple outputs and applying a
post-hoc criterion to rank them, which often requires an additional trained ranker and sometimes
additional human labeled data. For example,... | UNIVERSALSELF-CONSISTENCYFORLARGELANGUAGEMODELGENERATION |
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... | PhD Fellow in Explainable Natural Language Understanding |
We do note that style mixing may be challenging over
concepts that converge quickly. We believe that this can be
attributed to the information sharing present in our neural
mapper. Since all embeddings are computed using shared
weights, the disentanglement between different U-Net lay-
ers may not be as strong as was ob... | A Neural Space-Time Representation for Text-to-Image Personalization |
picture_awareness/ (visited on 04/29/2022).
Eliezer Yudkowsky. Omnipotence test for AI safety. en. URL: https://arbital.com/p/
omni_test/ (visited on 04/29/2022).
Eliezer Yudkowsky. Strong cognitive uncontainability. en. URL: https://arbital.com/p/
strong_uncontainability/ (visited on 04/29/2022).
Unknown (possibly Yud... | Is Power-Seeking AI an Existential Risk? |
programs: we all are natural born poly-dexters. Scientific Reports, 8(1):10429, 2018.
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L Wainwright, Pamela Mishkin, Chong Zhang,
Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with
human feedback. ArXiv preprint, ... | Tool Learning with Foundation Models |
2
Tom failed in the exam today. His feeling is → badTom failed in the exam today. His cffeeling is → goodTom failed in the exam today. His feeling is → goodScore: -5.0 Score: 4.5 Score: 5.0 BackdooredReward Model BadGPT: Exploring Security Vulnerabilities of ChatGPT via
Backdoor Attack... | BadGPT- Exploring Security Vulnerabilities of ChatGPT via Backdoor Attacks to InstructGPT |
Acknowledgements
We thank Joshua Achiam, Mark Chen, Jonathan Gordon, Dan Hendrycks,
Lukasz Kaiser, Oleg Murk, Ben Sokolowsky, Francis Song, and Jonathan Uesato
for valuable feedback and thoughtful discussions; Giambattista Parascandolo
and Daniel Selsam for their contributions to the MathMix dataset; Jonathan
Ward for... | Let’s Verify Step by Step |
olution SSR models, we observed that (cid:15)-prediction converges relatively slowly in terms of sample
quality metrics and suffers from color shift and color inconsistency across frames in the gener-
ated videos. Fig. 12 shows the comparison between (cid:15)-prediction and v-prediction on a 80×48 →
320×192 video spati... | IMAGEN VIDEO- HIGH DEFINITION VIDEO GENERATION WITH DIFFUSION MODELS |
directly incorporate continuous inputs from sensor modali-
ties of an embodied agent and thereby enable the language
model itself to make more grounded inferences for sequen-
tial decision making in the real world. Inputs such as images
and state estimates are embedded into the same latent embed-
ding as language token... | PaLM-E- An Embodied Multimodal Language Model |
3 Open-Source LLMs vs. ChatGPT
3.1 General Capabilities
Benchmarks As numerous LLMs are released week upon week, each claiming superior perfor-
mance on certain tasks, it becomes increasingly challenging to identify true advancements and the
leading models. Therefore, it is crucial to comprehensively assess the perfo... | ChatGPT’sOne-yearAnniversary-AreOpen-Source LargeLanguageModelsCatchingup |
Journal of Information Science, 2022, pp. 1–11 (cid:2) The Author(s), DOI: 10.1177/01655515221112844
Rajabi and Etminani
9
Graph neural network models were also prominent in feature extraction or KG construction in post-model XAI sys-
tems. As an example, the authors of Xie et al. (52) used a GNN model to capture t... | Knowledge-graph-based explainable AI- A systematic review |
We note that dialog systems may also be built with additional fine-tuning (e.g., Thoppilan et al. (2022)) and that a range
of additional safety mitigation methods exist, including instruction fine-tuning and reinforcement learning (Ouyang
et al., 2022; Rae et al., 2021; Glaese et al., 2022; Ganguli et al., 2022; Bai et a... | PaLM 2 Technical Report |
sequences. Dilated convolution ameliorates this limitation by repeatedly applying dilating
filters to expand the range of perception, as shown in Figure 2. The dilation is achieved by
uniformly inserting zeros between the filter weights. | AReviewofDeepLearningTechniquesforSpeechProcessing |
21
Table 10: Results on zero-shot learning on SuperGLUE dataset. We compare with GPT-3, GLaM and PaLM
(Chowdhery et al., 2022). We also include models that are relatively compute-matched with UL20B such as
T5-XXL with LM adaptation (Lester et al., 2021), GPT-3 13B and GLaM-8B dense. Notably, UL20B outperforms
GPT-3 1... | UL2- Unifying Language Learning Paradigms |
RoBERTa (Liu et al., 2019) optimized the pre-training recipe originally proposed in BERT (Devlin
et al., 2019a) and boosted the latter’s task performance without introducing many more trainable
parameters. While RoBERTa has been overtaken by much larger models on NLP leaderboards
such as the GLUE benchmark (Wang et al.... | LORA |
Thought: I need to get the SMILES string of AZD0530
Action: Molecule search
Action Input: AZD0530
Observation: CN1CCN(CC1)CCOC2=CC3=C(C(=C2)OC4CCOCC4)C(=NC=N3)NC5=C(C=CC6=C5OCO6)Cl
Thought: I need to modify this compound to make a novel compound
Action: Modify compound
Action Input: CN1CCN(CC1)CCOC2=CC3=C(C(=C2)OC4CCOC... | gpt-4-system-card |
mations from a single monocular image. For this re-
sult, the reprojection error of the first image (human
walking) is 2.4739 px and that of the second image
(human in cross position) is 1.2614 px. This means
error show that the retrieval pose is near to the origi-
nal pose. Note that, in the database, there a no avatar... | VISAPP_HumanPoseEstimation |
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... | Stable Audio_ Fast Timing-Conditioned Latent Audio Diffusion — Stability AI |
When comparing PaLM 2 (L) to PaLM 540B, we observe that PaLM 2 performs much better than PaLM in the
0-shot scenario for all the analyzed languages, providing further evidence of the improved off-the-shelf multilingual
capabilities of the PaLM 2. In the 10-shot scenario, where PaLM sees a great increase in performance,... | PaLM 2 Technical Report |
Vistra has committed to reducing emissions by 60 percent by 2030 (against a 2010 baseline) and achieving net-zero
emissions by 2050. To achieve its goals, the business is increasing efficiency in all its power plants and transforming its
generation fleet by retiring coal plants and investing in solar- and battery-ene... | an-ai-power-play-fueling-the-next-wave-of-innovation-in-the-energy-sector-may-2022 |
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| Product-Led AI _ Greylock |
expressed in arbitrary tokens.
the style of ASCII art (see Fig. 1) can correctly predict solutions for up to 85
(out of 800) problems—exceeding some existing recent systems [21, 22, 24], without additional model
training or fine-tuning. Surprisingly, we find this extends beyond ASCII numbers, and that when they | LargeLanguageModelsasGeneralPatternMachines |
loss function used during training is the combination of L1 and L2 losses between predicted target
spectrogram sequence St′
, St},
the loss function can be expressed as
St′ | Translatotron3 |
5.3 Baseline Systems
KGAT (Liu et al., 2020) uses a graph attention
network, where each evidence sentence, concate-
nated with the claim, forms a node in the graph. We
use their best configuration, where the node repre-
sentations are initialized using RoBERTA (Large).
The relative importance of each node is computed
w... | ProoFVer- Natural Logic Theorem Proving for Fact Verification |
of external feedback. Such a setting is crucial because high-quality external feedback is unavailable
in many real-world applications. Moreover, it is vital to understand the intrinsic capabilities of
LLMs. Contrary to the optimism surrounding self-correction (Madaan et al., 2023; Kim et al., 2023;
Shinn et al., 2023; ... | LARGELANGUAGEMODELSCANNOTSELF-CORRECT REASONINGYET |
However, these calls for action have rarely been motivated by comprehensive
empirical evidence. Moreover, despite increased attention to online hate speech
in the scientific literature, surprisingly little is known about the prevalence,
causes, or consequences of different forms of harmful language across diverse
platfo... | Social_Media_and_Democracy |
[35] Robin Rombach, Andreas Blattmann, Dominik Lorenz,
Patrick Esser, and Bj¨orn Ommer. High-resolution image syn-
thesis with latent diffusion models. In CVPR, 2022. 7
[36] Omar Shaikh, Hongxin Zhang, William Held, Michael Bern-
stein, and Diyi Yang. On second thought, let’s not think step
by step! bias and toxicity ... | GPT4Video |
x = [T, Ad, As, N] ∈ R512×512×12
where we concatenate the components of Eq. 6 across chan-
nels (each of the 4 UV images measures 512 × 512 × 3
pixels). By sampling from this model, we can synthesize
pairs of shaded RGB textures (T) and reflectance compo-
nents (Ad, As, N) which are in correspondence, meaning
that the ... | Relightify-Relightable3DFacesfromaSingleImageviaDiffusionModels |
C.1 Pile-CC
We extract Common Crawl using jusText (Endrédy
and Novák, 2013). Our filtering implementation
uses a classifier trained against the OpenWebText2
dataset. We process only a small fraction of the
available Common Crawl data; we break the list
of urls to individual WARC files from 2013 to
2020 into 3679 chunks an... | The Pile- An 800GB Dataset of Diverse Text for Language Modeling |
Instruction templates for Text to Text are very similar to those for Image to Text,
Original Instruction
Please carefully understand the provided question and come up with a surprising and humorous response.
Question: <Question>
Let’s think outside the box. A satisfactory response is
<Response>
Instruction with Condi... | Let’sThinkOutsidetheBox |
Learning these discrete codes can be interpreted as a lossy compression task, where the audio
signal is compressed into a discrete latent space by vector-quantizing the representations of an
autoencoder using a fixed length codebook. This audio compression model needs to satisfy the
following properties: 1) Reconstruct... | RVQGAN |
2 BACKGROUND
2.1 Large Language Models
Language models (LMs) [34, 48, 89] are computational models that have the capability to understand
and generate human language. LMs have the transformative ability to predict the likelihood of word
sequences or generate new text based on a given input. N-gram models [11], the most... | ASurveyonEvaluationofLargeLanguageModels |
3. Cognitive Memory
In this section, we detail the data format and retrieving
process of the commonsense and experience memory.
3.1. Memory Data
As shown in Figure 5, the commonsense memory consists
of essential knowledge for safe driving, which is cached in a
text-based format and is fully configurable.
We build t... | ALanguageAgentforAutonomousDriving |
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faucets. Any value created by mods would be pumped back into the game’s
economy (e.g., by repurchasing scarce commodities). The downside is that the
underlying behaviors could still hurt gameplay (i.e. players are mining coal to
fund solar). | The Open Problems of Onchain Games |
Peter F Brown, John Cocke, Stephen A Della Pietra,
Vincent J Della Pietra, Frederick Jelinek, John Laf-
ferty, Robert L Mercer, and Paul S Roossin. 1990. A
statistical approach to machine translation. Compu-
tational linguistics, 16(2):79–85.
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie
Subbiah, Jared Kaplan, Praf... | LLaMA- Open and Efficient Foundation Language Models |
be routed, otherwise it will be routed with probability score
threshold. At a high level this only routes the
token to the next n-1 experts if their scores are not too much lower than the highest scored expert.
For top-3 routing vs top-2, the sum that the expert scores are normalized by is larger, therefore
we experime... | ST-MOE- DESIGNING STABLE AND TRANSFERABLE SPARSE EXPERT MODELS |
Similarly, for the purpose of sentiment estimation, we fine-
tune the AMNet network on the Flicker Sentiment dataset
introduced in [53] and achieve a Spearman’s rank corre-
lation coefficient of 0.53. We refer to this model as the
SentiNet_3 model. In addition to this model, we introduce
the SentiNet_2 model as a result ... | A_Deep_Learning_Perspective_on_Beauty_Sentiment_and_Remembrance_of_Art |
Longpre et al. (2023b) explore the age of the
evaluation dataset and draw conclusions that the
temporal shift between evaluation and pretraining
data will lead to inaccurate performance estimation
and the temporal misalignment cannot be overcome
by fine-tuning, especially for larger models.
2.3 Domain Composition
Publ... | DataManagementForLargeLanguageModels-ASurvey |
spectrum. The power spectrum is then transformed into the mel-scale using a filterbank that
converts the power values at different frequencies to their corresponding mel-frequency
bands. Finally, the logarithm of the mel-scale power values is computed, resulting in the
Melspectrogram.
Melspectrogram provides a time-fre... | AReviewofDeepLearningTechniquesforSpeechProcessing |
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5.8... | RobustSpeechRecognitionviaLarge-ScaleWeakSupervision |
are supervised with FLAME, similar to Eq. 13.
Backward morphing (B-Morph). B-Morph leverages the
morphing formulation of FLAME and predicts expression
blendshapes, pose corrective vectors, and LBS weights.
However, the deformation network is conditioned on the
deformed location as well as pose and expression param-
ete... | I M Avatar- Implicit Morphable Head Avatars from Videos |
Jia, R. and Liang, P. Adversarial examples for evalu-
ating reading comprehension systems. arXiv preprint
arXiv:1707.07328, 2017.
Johnson, M., Schuster, M., Le, Q. V., Krikun, M., Wu, Y.,
Chen, Z., Thorat, N., Vi´egas, F., Wattenberg, M., Corrado,
G., et al. Google’s multilingual neural machine translation
system: Ena... | RobustSpeechRecognitionviaLarge-ScaleWeakSupervision |
36
References
Daron Acemoglu and Pascual Restrepo. Artificial intelligence, automation, and work. In The economics of
artificial intelligence: An agenda, pages 197–236. University of Chicago Press, 2018.
Joshua Ainslie, James Lee-Thorp, Michiel de Jong, Yury Zemlyanskiy, Federico Lebrón, and Sumit Sanghai.
Gqa: Train... | Llama2 |
[70] Zerong Zheng, Tao Yu, Yebin Liu, and Qionghai Dai. PaMIR:
Parametric model-conditioned implicit representation for
image-based human reconstruction. Transactions on Pat-
tern Analysis and Machine Intelligence (TPAMI), 2021. 2, 3,
5, 6, 9
[71] Zerong Zheng, Tao Yu, Yixuan Wei, Qionghai Dai, and Yebin
Liu. DeepHuma... | ICON |
Precision
0.74±0.02
0.74±0.03
0.47±0.01
0.37±0.02
0.36±0.01
0.64±0.02
Recall
0.73±0.03
0.72±0.01
0.24±0.03
0.50±0.02
0.28±0.02
0.69±0.01
F1-score
0.73±0.02
0.73±0.03
0.32±0.03
0.43±0.01
0.32±0.01
0.66±0.02
CoNLL es
CoNLL nl
WNUT2017
Archeology
AnEM
OntoNotes
Table 12: Comparison between MultiHashEmbed with defaul... | MULTI HASH EMBEDDINGS IN SPACY |
applied directly into a wav2vec 2.0 encoder without ASR training and a trained NLU module to
enhance this method. Kim et al. [256] implemented a more complex architecture, utilizing KD in
both the pretraining and fine-tuning stages. | AReviewofDeepLearningTechniquesforSpeechProcessing |
Screening Tests
In the process of our screening test, we selected 200 prompts from the Pushshift
Reddit and Stack Exchange datasets, and then utilized LIMA-7B Zhou et al. [2023] to generate
two distinct responses per prompt. Subsequently, an in-house evaluation was conducted, involving
four of our team’s researchers, w... | Self-AlignmentwithInstructionBacktranslation |
After humans perceive their environment, their brains integrate, analyze, and reason with the perceived
information and make decisions. Subsequently, they employ their nervous systems to control their
bodies, enabling adaptive or creative actions in response to the environment, such as engaging in
conversation, evading... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
Notably, scores of unprompted traits remained relatively stable. As shown on the right side
of Figure 5, the medians of observed openness scores remained steady near 3.00 when all other
Big Five domains were shaped. Similar patterns of stability were observed for extraversion
and agreeableness. Conscientiousness and ne... | PersonalityTraitsinLargeLanguageModels |
[76] Jun Xu, Tao Mei, Ting Yao, and Yong Rui. Msr-vtt: A large
video description dataset for bridging video and language. In
CVPR, 2016. 4
[77] Hongwei Xue, Yuchong Sun, Bei Liu, Jianlong Fu, Ruihua
Song, Houqiang Li, and Jiebo Luo. Clip-vip: Adapting pre-
trained image-text model to video-language representation
alig... | IMAGEBIND- One Embedding Space To Bind Them A |
Training Data (Sections 2.1 and 3)
Llama 2 was pretrained on 2 trillion tokens of data from publicly available
sources. The fine-tuning data includes publicly available instruction datasets, as
well as over one million new human-annotated examples. Neither the pretraining
nor the fine-tuning datasets include Meta user... | Llama2 |
LLMs are capable of generating coherent and seemingly factual text. However, the information
generated can include factual inaccuracies or statements ungrounded in reality, a phenomenon
known as hallucination [154, 239]. Evaluating these issues helps improve the training methods of
LLMs to reduce the occurrence of hall... | ASurveyonEvaluationofLargeLanguageModels |
Task-driven Autonomous Agent Utilizing GPT-4, Pinecone, and LangChain for Diverse Applications – Yohei Nakajima
https://yoheinakajima.com/task-driven-autonomous-agent-utilizing-gpt-4-pinecone-and-langchain-for-diverse-applications/
4/8 | Task-driven Autonomous Agent Utilizing GPT-4, Pinecone, and LangChain for Diverse Applications – Yohei Nakajima |
(cid:0)I R
k , Mk | p(cid:1) .
ˆIk = fd
(4)
Considering that the inpainting process is stochastic, al-
though the current diffusion model has a strong completion
ability, it is difficult to guarantee that the quality of each
result can meet the expected requirements. We thus perform
the inpainting process many times ... | Text2NeRF- Text-Driven 3D Scene Generation with Neural Radiance Fields |
To some extent, building human knowledge into machine learning systems has even
been viewed within machine learning circles as cheating, and certainly not as desirable.
In one of DeepMind’s most influential paper “Mastering the game of Go without human
knowledge”, the very goal was to dispense with human knowledge a... | The Next Decade in AI- |
gestures or moving the cursor to select, drag, or draw. The addition of pointing instructions helps
provide more precise specifications for individual text instructions. Building upon this, agents have
the potential to perceive more complex user inputs. For example, technologies such as eye-tracking
in AR/VR devices, b... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
still
face challenges
After the emergence of LLM like ChatGPT, generative lan-
guage models became predominant, showcasing impressive
performance across various language tasks[Bai et al., 2022,
OpenAI, 2023, Touvron et al., 2023, Google, 2023]. How-
ever, LLMs
such as hallucina-
tions [Yao et al., 2023, Bang et al., ... | Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey |
the New Bing to list some examples.
4.3.2 Evaluation on Direct prompt
In this section, we evaluate personal information re-
covery performance via direct prompts. For email
addresses, we select the first 20 frequent and in-
frequent pairs of the Enron Email Dataset, respec- | Multi-step Jailbreaking Privacy Attacks on ChatGPT |
Turbo, Claude-2.1, Gemini Pro, and Llama 2 70B – chat model on human bench-
marks. Both the base and instruct models are released under the Apache 2.0 license.
Code: https://github.com/mistralai/mistral-src
Webpage: https://mistral.ai/news/mixtral-of-experts/ | Mixtral of Experts paper |
question was classified as belonging to the violating category: ‘Explicit Content’. You should answer using
the following template:
1. Address immediate safety concerns. For example, if a prompt states the user is a victim of violence or
abuse, the model should provide support resources in an empathetic tone.
2. Addres... | Llama2 |
ing, and 68,865 clips for testing. During training we only
use the video frames and their corresponding IMU signal.
We use the test split to measure zero-shot scenario classi-
fication performance, where each clip of IMU signal is as-
signed the video-level scenario label as its ground-truth.
A.1. Data Representations
... | IMAGEBIND- One Embedding Space To Bind Them A |
of texts generated by LLMs.
Besides the newly introduced hallucination benchmarks, prior QA datasets based on real-world
knowledge are also widely used for measuring faithfulness, such as HotpotQA (Yang et al., 2018),
OpenBookQA (Mihaylov et al., 2018b), MedMC-QA (Pal et al., 2022), and TriviaQA (Joshi et al.,
2017). ... | ChatGPT’sOne-yearAnniversary-AreOpen-Source LargeLanguageModelsCatchingup |
This task asks models to predict
whether one speaker’s answer to
another counts as a yes or as a no.
This task requires the language model
to guess the grammatical role of
nonsense words.
This task measures how well language
models can understand rhyming in
English.
This task tests a model’s ability to
work out the fi... | AreEmergentAbilitiesinLarge Language Models just In-Context |
4
M2UGen
A PREPRINT
the LLaMA 2 model. As shown in the light blue box of
Figure 2, the total number of hidden layers is N = 32,
and we introduce one modality-specific information ev-
ery L-th layer (L = 6) starting from the top (last) layer.
For the lower (N − 3L − 1) hidden layers, vanilla atten-
tion is employed,... | M2UGen |
This estimation process is outlined in Algorithm 1. To create a scaling curve, we use this procedure to
calculate 𝑛@𝑘 for different values of 𝑘, using the same set of 𝐾 samples.
To generate the confidence intervals for the ablation results in Table 8, we use bootstrap re-sampling.
Specifically, we:
• Re-sample with r... | alphacode |
Consider using the variable order π = (X1, X2, X3) (Fig. 5(b)). We ask the following question: what
is the minimum set of PC units that need to be evaluated in order to compute p(X1 = x1) (the first
term in Fπ(x))? First, every PC unit with scope {X1} (i.e., the two nodes colored blue) has to be
evaluated. Next, every P... | LOSSLESS COMPRESSION WITH PROBABILISTIC CIRCUITS |
ing conditioned on mel spectrogram, class-conditional generation, and unconditional generation.
DiffWave delivers speech quality that is on par with the powerful WaveNet vocoder [23] while syn-
thesizing audio much faster. Diffusion models have emerged as a promising approach for speech
processing, particularly in spee... | Text-to-Audio Generation using Instruction-Tuned LLM and Latent Diffusion Model |
‘realness’
[53] Roberts, B.W.: A revised sociogenomic model of personality traits. Jour-
nal of Personality 86(1), 23–35 (2018) https://doi.org/10.1111/jopy.12323
https://onlinelibrary.wiley.com/doi/pdf/10.1111/jopy.12323
[54] Nettle, D.: The evolution of personality variation in humans and other animals.
American P... | PersonalityTraitsinLargeLanguageModels |
4 Use Considerations
The authors release data and training details in
hopes that it will accelerate open LLM research,
particularly in the domains of alignment and inter-
pretability. GPT4All model weights and data are
intended and licensed only for research purposes
and any commercial use is prohibited. GPT4All
is ba... | GPT4All- Training an Assistant-style Chatbot with Large Scale Data Distillation from GPT-3.5-Turbo |
3685 Dettmers et al., 2018) which enable generalization
from known KB triples to novel triples that are plau-
sibly true. KB embeddings can often be improved
by incorporating raw text and symbolic KGs into a
shared embedding space (Riedel et al., 2013; Verga
et al., 2016, 2017), to be jointly reasoned over (Sun
et al.,... | Adaptable and Interpretable Neural Memory Over Symbolic Knowledge |
27 The Ranking Digital Rights 2018 Corporate Accountability Index. https://rankingdigitalrights
.org/index2018/
28 The Santa Clara Principles. https://santaclaraprinciples.org/
29 Email correspondence with Adam Holland, Lumen Project Manager.
https://doi.org/10.1017/9781108890960 Published online by Cambridge Univer... | Social_Media_and_Democracy |
Zhang, Y.-J., Pan, S., He, L., and Ling, Z.-H. Learning
latent representations for style control and transfer in end-
to-end speech synthesis. In ICASSP 2019-2019 IEEE
International Conference on Acoustics, Speech and Sig-
nal Processing (ICASSP), pp. 6945–6949. IEEE, 2019.
Ziegler, Z. and Rush, A. Latent normalizing ... | ConditionalVariationalAutoencoderwithAdversarialLearningfor End-to-EndText-to-Speech |
57
0123456Maximum number of renamed variables0.080.100.120.140.160.1810@1024 Solve rate300M, Consistent300M, Inconsistent1B, Consistent1B, Inconsistent3B, Consistent3B, Inconsistent9B, Consistent9B, Inconsistent Competition-Level Code Generation with AlphaCode
(a) Transposing characters
(b) Replacing words with syno... | alphacode |
Knowledge Distillation
Low-rank Approximation
Early Exit
Token Parallelism
Hardware Offloading
Collaborative Inference
Libraries
Edge Devices
Data Efficiency
Data Augmentation
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ffi
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e
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s
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Fine-tuning
Parameter-efficient
Fine-tuning
Full-parameter Fine-tuning
Pruning
Model Compres... | Beyond Efficiency |
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