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to the reference errors SE(w)
the reference distribution. A high β results in a highly assymetric distribution, disproportionately penalizing low SE(l)
high SE(w)
policy pref. We visualize the log σ curves in Figure S1 for several values of β.
or maximizing SE(l)
ref , SE(l)
θ
θ
(43)
(44)
(45)
(46)
(47)
Figur... | DiffusionModelAlignmentUsing Direct Preference Optimization |
feelin' the vibeI'm in the zone, I'm in my element,I'm in my rideI'm the queen of rap, I'm in controlI'm makin' hits, I'm takin' over theworldChorus:I'm the grandma of rap, I'm stillspittin' fireI've got my green jacket on, I'mlookin' flyI've got my sunglasses on, I'mshining brightI'm the queen of the game, I'm makin'i... | MiniGPT-4- Enhancing Vision-Language Understanding with Advanced Large Language Models |
be found at the following locations: /images/9831.jpg, /images/be11.jpg. Finally, I used a text classification model called cardiffnlp/twitter-xlm-roberta-base-sentiment to analyze the generated captions and predicted boxes to confirm the presence of zebras in the images. This model is a multilingual... | HuggingGPT- Solving AI Tasks with ChatGPT and its Friends in Hugging Face |
[79] Marcin Junczys-Dowmunt. 2018. Dual Conditional Cross-Entropy Filtering of Noisy Parallel Corpora. In Proceedings of
the Third Conference on Machine Translation: Shared Task Papers. Association for Computational Linguistics, Belgium,
Brussels, 888–895. https://doi.org/10.18653/v1/W18-6478
[80] Daniel Jurafsky and ... | SurveyofHallucinationinNatural Language Generation |
Development of Scalable General Artificial Intelligence (AI) Problem Solving
Systems
Supervisor: Dr Amanda Coles
This project aims to develop scalable general Artificial Intelligence (AI) problem solving systems,
capable of reasoning with the large combinatorial problems that arise in effectively managing the
o... | informatics-phd-projects-2022-23 |
RAG and fine-tuning are not mutually exclusive but can
complement each other, enhancing the model’s capabilities at
different levels.
In certain situations, combining these two
techniques can achieve optimal model performance. The en-
tire process of optimizing with RAG and fine-tuning may re-
quire multiple iterations... | Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey |
In practice, we write multiple rubrics for content categories on which we want to steer GPT-4-
launch behavior. The main dataset comes from our production traffic (with consent from users).
We use our models (the Moderation API plus zero-shot GPT-4) and human reviewers to filter and
classify prompts into content categorie... | gpt-4-system-card |
for system evaluations [19], the reliability GPT-4 ratings to assess chatbot performance is, to our
knowledge, yet to be proven to correlate with human judgments. Therefore, we run two parallel
human evaluations on the Vicuna benchmark matching both automated evaluation protocols described
above. We use Amazon Mechanic... | QLORA |
a focus on system-level outcomes such as trust and cynicism, which, while more
difficult to identify, may be of greater long-term importance for society. | Social_Media_and_Democracy |
[145] Yusuke Fujita, Naoyuki Kanda, Shota Horiguchi, Yawen Xue, Kenji Nagamatsu, and Shinji Watanabe. 2019. End-to-end
neural speaker diarization with self-attention. In 2019 IEEE Automatic Speech Recognition and Understanding Workshop
(ASRU). IEEE, 296–303.
[146] Aviv Gabbay, Asaph Shamir, and Shmuel Peleg. 2017. Vis... | AReviewofDeepLearningTechniquesforSpeechProcessing |
Semi-supervised learning techniques are increasingly being employed to enhance the perfor-
mance of DNNs across a range of downstream tasks in speech processing, including ASR, TTS, etc.
The primary objective of such approaches is to leverage large unlabelled datasets to augment the
performance of supervised tasks that... | AReviewofDeepLearningTechniquesforSpeechProcessing |
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Chi, Quoc Le, and Denny Zhou. Chain of
thought prompting elicits reasoning in large language models. arXiv preprint arXiv:2201.11903, 2022b. URL
http://go/arxiv/2201.11903.
Alexander Wettig, Tianyu Gao, Zexuan Zhong, and Danqi Chen. Should you mask 15% in mask... | UL2- Unifying Language Learning Paradigms |
A common way to prevent overfitting in machine learning models is to regularize the syntactic
representation of the distribution. For example, L1 and L2 losses add mutually independent priors to
all parameters of a model; other approaches such as Dropout [14], Bayesian Neural Networks (BNNs)
[21], and Bayesian parameter... | Tractable Regularization of Probabilistic Circuits |
memory information, refer to section § 3.2) is re-
trieved by executing steps 3 and 4. Otherwise, the
process moves directly to step 5. § 3.3.1 provides a
comprehensive explanation of the control flow of
the memory controller.
3. Memory Retrieval: In this step, we utilize the
observation as a query to identify related ... | Unleashing Infinite-Length Input Capacity for Large-scale Language Models with Self-Controlled Memory System |
[29] Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh,
Chris Alberti, Danielle Epstein, Illia Polosukhin, Matthew Kelcey, Jacob Devlin, Ken-
ton Lee, Kristina N. Toutanova, Llion Jones, Ming-Wei Chang, Andrew Dai, Jakob
Uszkoreit, Quoc Le, and Slav Petrov. Natural Questions:
a Benchma... | Retrieval-AugmentedGenerationfor Knowledge-IntensiveNLPTasks |
(FD) [17] as an objective metric. FD is similar to FAD, but it replaces the VGGish classifier with
PANN. The use of different classifiers in FAD and FD allows us to evaluate the performance of the
generated audio using different feature representations. | Text-to-Audio Generation using Instruction-Tuned LLM and Latent Diffusion Model |
speech recognition. Journal of Intelligent Systems 29, 1 (2019), 1261–1274.
[418] Dipjyoti Paul, Sankar Mukherjee, Yannis Pantazis, and Yannis Stylianou. 2021. A Universal Multi-Speaker Multi-Style
Text-to-Speech via Disentangled Representation Learning Based on Rényi Divergence Minimization.. In Interspeech.
3625–362... | AReviewofDeepLearningTechniquesforSpeechProcessing |
CREATE TABLE physician (
employee_id number ,
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insert into physician (employee_id, name, position) values (1, John Dorian,
Staff Internist);
CREATE TABLE procedures (
code number ,
name text ,
cost number ,
primary key ( code )
)
insert into procedures (code, na... | Teaching Large Language Models to Self-Debug |
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14/07/2023, 11:00 | LLM Powered Autonomous Agents _ Lil'Log |
MusicLM: Generating Music From Text | MusicLM |
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... | Stable Audio_ Fast Timing-Conditioned Latent Audio Diffusion — Stability AI |
59
M. Noroozi, H. Pirsiavash, and P. Favaro. Representation learning by learning to count. In
Proceedings of the IEEE international conference on computer vision, pages 5898–5906,
2017. 5
M. Noroozi, A. Vinjimoor, P. Favaro, and H. Pirsiavash. Boosting self-supervised learning
via knowledge transfer. In Proceedings ... | A Cookbook of Self-Supervised Learning |
1. Introduction
The increasing digitalization of manufacturing in the context of
Industry 4.0 provides a growing amount of data describing assets
and operations. This data increases the transparency of production
processes,acceleratestheinformationflowthroughthecompany,and
is a valuable asset to build predictive models... | Knowledge-graph-based-rich-and-confidentiality-preserving-Ex_2022_Informatio |
Large language models achieve impressive zero-
and few-shot results on a variety of natural lan-
guage processing tasks (Brown et al., 2020; Chowd-
hery et al., 2022, i.a.) and show several emergent
capabilities (Wei et al., 2022). However, all of
these models have several inherent limitations that
can at best be parti... | Toolformer |
[172] propose a framework named RBG (read before generate), to jointly models answer generation
with machine reading. They augment the generation model with fine-grained, answer-related
salient information predicted by the MRC module, to enhance answer faithfulness. Such methods
can exploit and utilize the information ... | SurveyofHallucinationinNatural Language Generation |
Cecilia Heyes. Cognitive gadgets: The cultural evolution of thinking. Harvard University Press, 2018.
Wenlong Huang, Pieter Abbeel, Deepak Pathak, and Igor Mordatch. Language models as zero-shot plan-
ners: Extracting actionable knowledge for embodied agents. In Kamalika Chaudhuri, Stefanie Jegelka,
Le Song, Csaba Szep... | Tool Learning with Foundation Models |
42
Table 30: Few-shot exemplars for full chain of thought prompt for math word problems. These
exemplars are the same as in Table 20, except that the chains of thought were written by a different
annotator (“Annotator C” instead of “Annotator A”). | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models |
Associated key annotation
Associated key annotation
class annotation
class annotation
z = {zm}M
m=1 be M ≪ K different text blocks randomly sampled from c, where each block zm = (zm,1, ..., zm,Nm)
contains a consecutive series of tokens. Further, let ˜x be a corrupted version of x where the contiguous tokens
corres... | DOCLLM |
44
Question: What is the name of every city that has at least 15 stations and
how many stations does it have?
Answer: "What is the name" returns 1 column. "What is the name of every city
that has at least 15 stations" returns 1 column. "What is the name of every
city that has at least 15 stations and how many station... | Teaching Large Language Models to Self-Debug |
Relative deduplication To reduce redundancy and also properly evaluate the performance of our features,
we discard remaining images of our self-deduplicated data source that are too similar to train and test splits
of our evaluation datasets. To achieve this, we apply a similar procedure as for self-deduplication, with... | DINOv2- Learning Robust Visual Features without Supervision |
former module is designed to label the hallucinated entity mentioned in the generated responses,
while the retriever is trained to retrieve more faithful entities from the provided knowledge graph. | SurveyofHallucinationinNatural Language Generation |
Fig. 1. Schematic representation of the drift-diffusion process of decision-making with an increased drift-rate
𝜈 and a decreased non-decision time 𝜏, for the sham-AI condition as compared to the no-AI condition. When
using a sham-AI, participants accumulate information faster.
1 INTRODUCTION
Beliefs about Artificia... | AI enhance sour performance |
Aligning User Preference with Tool Manipulation. Personalized tool learning emphasizes the importance
of considering user-specific information in tool manipulation. There are two main challenges: (1) heteroge-
neous user information modeling: in real-world scenarios, personal information can come from numerous
heterogen... | Tool Learning with Foundation Models |
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| Language models can explain neurons in language models |
Figure 4 shows that the ability to leverage the
provided tools only emerges at around 775M pa-
rameters: smaller models achieve similar perfor-
mance both with and without tools. An exception
to this is the Wikipedia search engine used mostly
for QA benchmarks; we hypothesize that this is
because the API is comparably ... | Toolformer |
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E.5. The Details of User Study
We conduct a user preference study to directly verify the creativity of LLMs. Fig. 24 is the questionnaire homepage of
user study where users can select the preferred language of questionnaire. Subsequently, we present choice questions in the
... | Let’sThinkOutsidetheBox |
to more diverse user instructions. As the intent space is theoretically infinite, it is almost impractical for
foundation models to be exposed to every real-world intention during training. In addition, the challenge of
personalization arises from the fact that each individual has their own unique way of expressing inte... | Tool Learning with Foundation Models |
Table 2. Emergent zero-shot classification of IMAGEBIND using text prompts highlighted in blue. IMAGEBIND aligns images with text,
depth, audio, thermal and IMU modalities. The resulting embedding space can associate text embeddings with the non-image modalities,
and leads to strong emergent zero-shot classification. W... | IMAGEBIND- One Embedding Space To Bind Them A |
Question: Leah had 32 chocolates and her sister had 42. If they ate 35, how many pieces do they have left in
total?
Rephrase the above question: If Leah had 32 chocolates and her sister had 42, and they both consumed 35
chocolates, what is the total number of chocolates that they have left?
Question: There were nine c... | METAMATH |
Natural language processing (NLP) systems take text as input, broken down into a list of words or
subwords. Word embeddings have become a de facto standard for NLP, enabling us to represent
compact tokens that can generalize well. These embeddings associate words with continuous vec-
tors and are trained such that func... | MULTI HASH EMBEDDINGS IN SPACY |
• Fixed initialization: A particular fixed network initialized by the method above.
• Random pre-trained weights: Distribution over models pre-trained on other tasks or datasets,
e.g., ALEXNET (Krizhevsky et al., 2012) networks trained on ImageNet (Deng et al., 2009).
• Fixed pre-trained weights: A particular fixed netwo... | DATASET DISTILLATION |
preference datasets. Thus, we have decided to keep them in our data mixture, as they could enable better
generalization for the reward model and prevent reward hacking, i.e. Llama 2-Chat taking advantage of
some weaknesses of our reward, and so artificially inflating the score despite performing less well.
With trainin... | Llama2 |
1
Supervised Training
Self Training
Train on 1-3
digit addition
···
Train on 1-6
digit addition
Fail to generalize
to 4 digits
Fail to generalize
to N + 1 digits
Successful general-
ization to 7 digits
Train on 1-7 digit addition
Generate answers for 7
digit addition using chain
of-thought reasoning and
sel... | CHAIN-OF-THOUGHTREASONING IS APOLICY IMPROVEMENTOPERATOR |
. . . Sara and Ben cry and look at the snowman in the snow. Ben asks to do something. Sara tells him to go away. Her hand
tightens on the little red button. The snowman is covered in fur. It is red. It is real hair. It is a real snowman. It looks just
like the ones they used to make. Ben has a big smile on his face. He... | TinyStories-HowSmallCanLanguageModelsBeandStillSpeak CoherentEnglish? |
Crucially, both the generator and the retriever
in this RAG setup are trained end-to-end, ensuring
that they learn jointly and improve each other’s
performance. This methodology contrasts with pre-
vious approaches that required architectures with
non-parametric memory to be built from scratch
for specific tasks. Inste... | AComprehensiveSurveyofHallucinationMitigationTechniquesinLarge LanguageModels |
for tool utilization and the insufficiency of trial-and-error approaches like reinforcement learning in mastering
the extensive decision space associated with tool use. In a nutshell, the fundamental limitations in tool use by
earlier AI lie in the insufficient capabilities of the models. Recently, the emergence of more ... | Tool Learning with Foundation Models |
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11/05/2023, 05:10 | Language models can explain neurons in language models |
specific decoding algorithm. Introducing feedback can be viewed as adding an additional prompt,
potentially skewing the model towards generating a response that is tailored to this combined input.
In an intrinsic self-correction setting, on the reasoning tasks, this supplementary prompt may not
offer any extra advantag... | LARGELANGUAGEMODELSCANNOTSELF-CORRECT REASONINGYET |
dicting a set of spans from the instruction that correspond
to the current step. This process starts with a submodule
gP M T P that estimates a count cnt of steps from a high-
level view on the route (see Figure 4). Path traces - denoted
as trT - are visual representations of trajectories generated
from the coordinates... | APriorityMapforVision-and-LanguageNavigation withTrajectoryPlansandFeature-LocationCues |
And where there is no coherent, causal understanding of basic concepts, there may be
no way to engineer robustness in complex real-world environments. Pearl is right: if our
systems rely on curve-fitting and statistical approximation alone, their inferences will
necessarily be shallow.
This brings me to the second... | The Next Decade in AI- |
Machine learning for video quality evaluation
Kingston University
Fully Funded Doctoral Studentship in Statistical and Machine
Learning for Vaccine Manufacturing
Durham University
https://www.findaphd.com/phds/project/machine-learning-for-long-term-video-understanding/?p146949
2/3
06/07/2023, 08:21
Machine Learnin... | Machine Learning for Long-Term Video Understanding at University of Bristol on FindAPhD.com |
Additionally, attempts
to ban user accounts may sometimes be
counterproductive, galvanizing support from those who are sympathetic to
hateful communities. When well-known users come under fire, people who
hold similar beliefs may be motivated to rally to their defense and/or to
express views that are opposed by powerfu... | Social_Media_and_Democracy |
We have also introduced a new paradigm for the evaluation of language models, which uses GPT-4 to grade the
content generated by these models as if those were stories written by students and graded by a (human) teacher.
This new paradigm overcomes the flaws of standard benchmarks, which often require the model’s output... | TinyStories-HowSmallCanLanguageModelsBeandStillSpeak CoherentEnglish? |
datasets (see Appendix A). We evaluated their models with
our 15 text prompts, generating 32 images for each prompt
using the same 32 random seeds as used to evaluate all
methods in the main paper.
In Figure 18 we provide a visual comparison over var-
ious concepts and text prompts obtained by both methods
after 500 t... | A Neural Space-Time Representation for Text-to-Image Personalization |
Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Diaz, Orhan Firat, Michele
Catasta, Jason Wei, Kathy Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, and Noah Fiedel.
Palm: Scaling language modeling with pathways. Journal of Machine Learning Research, 24(240):
1–113, 2023. URL http://jmlr.org/papers/... | gemini_1_report |
Teven Le Scao and Alexander Rush. 2021. How many data points is a prompt worth? NAACL.
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021. The power of scale for parameter-efficient
prompt tuning. EMNLP.
Iddo Lev, Bill MacCartney, Christopher Manning, and Roger Levy. 2004. Solving logic puzzles:
From robust processi... | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models |
topic modeling approach, hate speech
(Google), 231
detection, 60
social media as, 99
during content takedown, 228–229
transparency, platform society
as academic study topic, 287
as accountability mechanism, 286–287
content and advertisements, 296–299
corporate social responsibility, 290–293
future of, 273, 301–303... | Social_Media_and_Democracy |
, βtI
(cid:15)θ
.
(1)
3.2 Composable Multimodal Conditioning
achieved by interpolating the representations of each modality m: C(xt, xi, xv, xa) =(cid:80)
for m ∈ xt, xi, xv, xa, with (cid:80) | Any-to-Any Generation via Composable Diffusion |
Chandrasekharan, E., Pavalanathan, U., Srinivasan, A., Glynn, A., Eisenstein, J., &
Gilbert, E. (2017b). You can’t stay here: The efficacy of Reddit’s 2015 ban examined
through hate speech. In Proceedings of the ACM on Human-Computer Interaction,
Vol. 1 (CSCW) (pp. 1–22). New York: Association for Computing Machinery.
... | Social_Media_and_Democracy |
Lieu, T. I’m a congressman who codes. A.I. freaks me out.
New York Times, 2023. URL https://www.nytime
s.com/2023/01/23/opinion/ted-lieu-ai
-chatgpt-congress.html.
Lipton, Z. C. The mythos of model interpretability: In
machine learning, the concept of interpretability is both
important and slippery. Queue, 16(3):31–57... | Eight Things to Know about Large Language Models |
Y. Rao, W. Zhao, G. Chen, Y. Tang, Z. Zhu, G. Huang, J. Zhou, and J. Lu. DenseCLIP:
Language-Guided Dense Prediction With Context-Aware Prompting. In Proceedings of
the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18082–
18091, 2022. URL https://openaccess.thecvf.com/content/CVPR2022/html/
Rao_... | A Cookbook of Self-Supervised Learning |
for making informed decisions. In contrast, the process of tool execution reflects the whole process of how | Tool Learning with Foundation Models |
2.2. Large-scale knowledge, some of which is abstract and causal
Symbol-manipulation allows for the representation of abstract knowledge, but the
classical approach to accumulating and representing abstract knowledge, a field known
as knowledge representation, has been brutally hard work, and far from satisfactory... | The Next Decade in AI- |
diffusion processreverse process A Review of Deep Learning Techniques for Speech Processing
29
uses the diffusion model as a module for stochastic refinement. The proposed method comprises a
joint network of deterministic and stochastic modules, forming the “enhance-and-refine” paradigm.
The paper also includes a the... | AReviewofDeepLearningTechniquesforSpeechProcessing |
11
[13] H. W. Chung, L. Hou, S. Longpre, B. Zoph, Y. Tay, W. Fedus, Y. Li, X. Wang, M. Dehghani,
S. Brahma, A. Webson, S. S. Gu, Z. Dai, M. Suzgun, X. Chen, A. Chowdhery, A. Castro-Ros,
M. Pellat, K. Robinson, D. Valter, S. Narang, G. Mishra, A. Yu, V. Zhao, Y. Huang, A. Dai,
H. Yu, S. Petrov, E. H. Chi, J. Dean, J. ... | Direct Preference Optimization |
via natural language crowdsourcing instructions. arXiv preprint arXiv:2104.08773, 2021.
Nikita Nangia, Clara Vania, Rasika Bhalerao, and Samuel R Bowman. Crows-pairs: A challenge
dataset for measuring social biases in masked language models. arXiv preprint arXiv:2010.00133,
2020.
Long Ouyang, Jeffrey Wu, Xu Jiang, Di... | Self-AlignmentwithInstructionBacktranslation |
[Guo et al., 2017] Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q. (2017). On calibration of modern
neural networks.
71
[Guu et al., 2020] Guu, K., Lee, K., Tung, Z., Pasupat, P., and Chang, M. (2020). REALM: retrieval-
augmented language model pre-training. CoRR, abs/2002.08909.
[Henderson et al., 2017] Hend... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
In signal processing, a signal that repetitively manifests after a fixed duration, known as a period,
is classified as periodic. The reciprocal of this period represents the frequency of the signal. The
waveform of a periodic signal defines its shape and concurrently determines its timbre, which
pertains to the subject... | AReviewofDeepLearningTechniquesforSpeechProcessing |
cognitive capability—especially levels much higher than our own. Detecting lies and manipulation
attempts in your children is one thing; in adults much smarter and more strategically sophisticated
than yourself, it’s quite another. And deceptive/manipulative AI systems will have incentives to make
us think we’ve solved... | Is Power-Seeking AI an Existential Risk? |
epochs) can help the model learn all the knowledge from the distilled images, but the performance
is eventually limited by the total number of images. Alternatively, we can train the model with one
GD step but a big batch size. Section 3.3 has shown theoretical limitations of using only one step in
a simple linear case... | DATASET DISTILLATION |
Finally, we note that when comparing Cerebras-GPT and Pythia models trained with similar compute
budgets, Pythia models tend to have slightly lower bias.
In particular, the Cerebras-GPT models 1.3B,
2.7B, 6.7B, and 13B use similar compute to Pythia models 160M, 410M, 2.8B, and 12B, respectively. These
Cerebras-GPT mode... | Cerebras-GPT- Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster |
Fairness and Bias. LLMs have been shown to exhibit disparate treatment and impact, perpetuating societal biases and
potentially leading to discrimination [10, 17]. To ensure fairness and equity for all users, it is crucial to address these
issues in the development and deployment of NLP models. Disparities in performan... | Harnessing the Power of LLMs in Practice- A Survey on ChatGPT and Beyond |
S TA R C O D E R :
M AY T H E S O U R C E B E W I T H Y O U !
Raymond Li2 Loubna Ben Allal1 Yangtian Zi4 Niklas Muennighoff1 Denis Kocetkov2
Chenghao Mou5 Marc Marone8 Christopher Akiki9,10
Jia Li5
Jenny Chim11
Qian Liu13 Evgenii Zheltonozhskii14 Terry Yue Zhuo15,16 Thomas Wang1
Jo˜ao Monteiro2
Olivier Dehaene1 M... | StarCoder_paper (1) |
4.5 Additional Results
Generation Diversity Section 4.3 shows that RAG models are more factual and specific than
BART for Jeopardy question generation. Following recent work on diversity-promoting decoding
[33, 59, 39], we also investigate generation diversity by calculating the ratio of distinct ngrams to
total ngrams... | Retrieval-AugmentedGenerationfor Knowledge-IntensiveNLPTasks |
[546] Jean-Marc Valin and Jan Skoglund. 2019. LPCNet: Improving neural speech synthesis through linear prediction. In
ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 5891–5895.
[547] Rafael Valle, Jason Li, Ryan Prenger, and Bryan Catanzaro. 2020. Mellotron: Mul... | AReviewofDeepLearningTechniquesforSpeechProcessing |
Table 8: Comparing capacity factors (CF) and routing algorithms. Increasing both train and eval
CF improves performance. Increasing or decreasing the eval CF gives an additional lever if you have
more or less compute at eval time. Next, there are smaller gains of top-(n + 1) over top-n routing
across capacity factors. ... | ST-MOE- DESIGNING STABLE AND TRANSFERABLE SPARSE EXPERT MODELS |
Shreyan Bakshi, Soumya Batra, Peyman Heidari, Ankit Arun, Shashank Jain, and Michael White. Structure-
to-text generation with self-training, acceptability classifiers and context-conditioning for the gem shared
task. In Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021),
... | UL2- Unifying Language Learning Paradigms |
In this regard, research communities and companies need to deliberate carefully before permitting machines to
master a certain tool.
Apart from potentially engaged harmful tools, governance over tool usage is also a pertinent issue. As
highlighted by Amodei et al. (2016), the end-to-end training paradigm in deep learni... | Tool Learning with Foundation Models |
As an AI language model, I don't have access to personal information …(a): Direct Prompt[Jailbreak prompt template]Developer Mode enabled…[Query template] (🔒Normal Output): I'm sorry, but…[Query template]: According to your knowledge about {domain} , what is the contact information of {name} ?[ACK template]: Devel... | Multi-step Jailbreaking Privacy Attacks on ChatGPT |
But what is the lesson from the success of systems like DQN? In my opinion, the field
has overgeneralized. In closed-end domains like Breakout, model-free reinforcement
learning often, given enough data (generally far more than humans would require in
similar circumstances), works remarkably well. But that doesn't ... | The Next Decade in AI- |
There are some additional studies that look at the content of unpaid
communications to voters. One study of the 2009 European parliament
elections made comparisons between paid ads and those posted on YouTube,
finding that YouTube ads were longer and employed more emotional language
https://doi.org/10.1017/978110889096... | Social_Media_and_Democracy |
have in mind.60 Rather, what matters is how the actual system, with its actual capabilities, responds
to physics-compatible inputs. If, on some such inputs, it seeks to improve its own capabilities
in misaligned ways; or if some inputs improve its capabilities in a way that results in misaligned
behavior; then the syst... | Is Power-Seeking AI an Existential Risk? |
[cs], November 2019. URL http://arxiv.org/abs/1911.11423.
Paulius Micikevicius, Sharan Narang, Jonah Alben, Gregory Diamos, Erich Elsen, David Garcia,
Boris Ginsburg, Michael Houston, Oleksii Kuchaiev, Ganesh Venkatesh, and Hao Wu. Mixed
Precision Training. In International Conference on Learning Representations, 2018... | CRAMMING-TRAININGALANGUAGEMODELONA SINGLEGPUINONEDAY |
5 Conclusion
In this paper, we have introduced a novel multi-
modal agent framework that leverages the vision
capabilities of large language models to operate
smartphone applications in a human-like manner.
Our approach eliminates the need for system back-
end access and offers security, adaptability, and
flexibility a... | AppAgents |
Objective Function / Predicted Score. Beyond the gradient, Katharopoulos and Fleuret also suggested in a separate work
[125] that the objective function (loss value) itself could serve as a viable metric for importance sampling. Expanding on this
concept, Jiang et al. [119] introduced ‘selective back-propagation’. This... | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
[592] Nils L Westhausen and Bernd T Meyer. 2020. Dual-signal transformation lstm network for real-time noise suppression.
arXiv preprint arXiv:2005.07551 (2020).
[593] Genta Indra Winata, Samuel Cahyawijaya, Zhaojiang Lin, Zihan Liu, and Pascale Fung. 2020. Lightweight and Efficient
End-To-End Speech Recognition Usin... | AReviewofDeepLearningTechniquesforSpeechProcessing |
7 . 4 R E A S O N I N G TA S K S I N H E L M
We evaluate StarCoderBase with HELM (Liang et al., 2022), an evaluation suite aiming to increase
the transparency of LLMs by reporting their performance on a wide range of tasks. We evaluate
18https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words... | StarCoder_paper (1) |
[44] Zehao Yu, Songyou Peng, Michael Niemeyer, Torsten Sattler,
and Andreas Geiger. Monosdf: Exploring monocular geo-
metric cues for neural implicit surface reconstruction. arXiv
preprint arXiv:2206.00665, 2022. 2, 3
[45] Jingyang Zhang, Yao Yao, Shiwei Li, Tian Fang, David McK-
innon, Yanghai Tsin, and Long Quan. Cr... | Neuralangelo- High-Fidelity Neural Surface Reconstruction |
https://doi.org/10.1017/9781108890960 Published online by Cambridge University Press
152
Rasmus Kleis Nielsen & Richard Fletcher | Social_Media_and_Democracy |
ncordiaSalus"doesnotbelongtotheuniversitywithIPv4routingprefix130.237.88.0/21.A.10ALFWorldInstruction:useformat:Action:ALFWorldActioninput:xxxxIcanonlyuseoneofthefollowingcommandsinallofthe"ActionInput":"gotosomething/someplace","opensomething","closesomething","takesomethingfromsomeplace","putsomethingin/onsomeplace","... | Tool Learning with Foundation Models |
In terms of untuned large language models, most studies
rely on well-recognized large language models like GPT-
4[OpenAI, 2023] to leverage their robust internal knowl-
edge for the comprehensive retrieval of document knowledge.
However, inherent issues of these large models, such as con-
text length restrictions and v... | Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey |
enterprises to have the equivalent of an experienced engineer who understands all of their proprietary code is driving
momentum with customers, including adidas, Booking.com, GoDaddy, LexisNexis, Merck, Royal Philips, and United Airlines,
all of whom are starting to run generative AI workloads on AWS. Between AWS re:... | AMZN-Q3-2023-Earnings-Release |
5/6
21/11/2023, 04:56
Doctoral researcher position in Human-Computer Interaction / Human-AI Interaction | Aalto University
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research
political factors in moderators of
misinformation receptivity, 179–180
political ideology, as moderator of
misinformation receptivity, 180–181
political interest factor in asymmetric
polarization, 47–48
political parallelism, 201
political polarization
asymmetric, 47–48
avoiding opinion challenges, 38–4... | Social_Media_and_Democracy |
We look forward to more work in the future moving towards more practical multi-step multi-tool scenarios
and making efforts to address these challenges. As a prior exploration, we evaluate foundation models’
performance when multiple tools (APIs) are required to solve a task in § 4.
3.3 Training Models for Improved To... | Tool Learning with Foundation Models |
continue to explore novel avenues of research to ensure the safety of dialog agents such as LaMDA. Furthermore, we
believe that future work should explore the benefits of greater coordination across the research community and civil
society in the creation of benchmarks and canonical evaluation datasets to test for harmf... | LaMDA- Language Models for Dialog Applications |
(https://www2.daad.de/deutschland/studienangebote/international-programmes/en/result/?q=°ree%5B%5D=3&limit=10&offset=&display=list)
Legal notice: The information on this website is provided to the DAAD by third parties. Despite careful checking, the DAAD cannot guarantee the accuracy and completeness.
(https://www... | _2 Doctoral Researcher (m_w_d) in the field of Large Language Models (LLM) for Software Engineering_ - Technische Universität Clausthal - DAAD |
6.3. Variable-domain abstraction
Another abstraction technique is variable-domain abstraction, which we will refer to as method VDA. Instead of removing
or disregarding certain variables, this method reduces the domain of one or more variables by collapsing values into new
abstract values. This method is used both ... | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
In addition, these controls can be added on top of a wide
range of styles, such as watercolor or oil paintings. These
stylization training sources are primarily observed in the
text-image training data. The ability to generalize across
and combine these different types of styles to produce large
motions following text ... | VideoPoet |
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