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In this section we study the generalization of our features on downstream classification benchmarks. We
consider two sets of evaluations in that context. On one hand, we use large and finegrained datasets such
as iNaturalist and Places205. On the other, we use the 12 image classification tasks originally proposed
in SimCL... | DINOv2- Learning Robust Visual Features without Supervision |
Continual learning. Recent studies [190; 272] have highlighted the potential of LLMs’ planning
capabilities in facilitating continuous learning [196; 197] for agents, which involves continuous
acquisition and update of skills. A core challenge in continual learning is catastrophic forgetting
[273]: as a model learns ne... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
transcriptions. Individual samples of the AMI dataset contain very large audio files between 10
and 60 minutes in duration. We segment the audio samples according the the Kaldi (Povey et al.,
2011) recipe for AMI3 to yield utterance of suitable length for training ASR systems. This involves
splitting samples longer tha... | DISTIL-WHISPER |
Table 10: Qualitative examples from WebNLG. The first 6 examples are from the unseen categories, labeled next
to source; the last two examples are from the seen categories. For unseen categories, both prefix-tuning and fine-
tuning tend to undergenerate (generated output do not cover full table contents) or generate untru... | Prefix-Tuning |
led model training and evaluation for controlled sentiment generation and summarization; design
iterations for GPT-4 evaluation (particularly summarization); substantial writing contributions to
abstract, prelims/method and experiments; editing contributions to other sections.
EM provided input on early discussions on ... | Direct Preference Optimization |
the behavior of LLMs.
5. Experts are not yet able to interpret the inner
workings of LLMs.
6. Human performance on a task isn’t an upper
bound on LLM performance.
7. LLMs need not express the values of their
creators nor the values encoded in web text.
8. Brief interactions with LLMs are often mis-
leading.
Intr... | Eight Things to Know about Large Language Models |
6 CONCLUSION AND FUTURE CHALLENGES
Recent advances in large language models have been revolutionizing the field of natural language processing. Effectively
using LLMs requires understanding their capabilities, and limitations for various NLP tasks. This work presents a
practical guide to working with LLMs for downstrea... | Harnessing the Power of LLMs in Practice- A Survey on ChatGPT and Beyond |
• The volume of data in Delta Lake
has grown 304% YoY
• The Lakehouse is increasingly
being used for data warehousing,
including serverless data
warehousing with Databricks
SQL, which grew 144% YoY
6
2023 STATE OF DATA + AI Methodology: How did Databricks
create this report?
The 2023 State of Data + AI... | 2023 state of ai databrick |
elements: 1) an encoder which learns a feature representation of the inputs using
two layers of Transformers and 2) a decoder which combines the last predicted
note and the encoded representation as input and feeds them to one unidirec-
tional LSTM to produce the final output which is the predicted next note. They
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| Language models can explain neurons in language models |
of knowledge and needs, ethical concerns, and the impersonal interaction. | Adoptionand AppropriationofLLMs |
In music composition, the arrangement of a piece
typically follows a gradual introduction, a main
body with the core content, and a gradual conclu-
sion, also called the sonata form (Webster, 2001).
Accordingly, we look into whether our generated
music also shows such a long-term structure. Us-
ing the same text prompt... | MOUSAI |
consistent motion as opposed to the 1B model 5 roses and
distorting objects produced by the 1B model. Overall, scal-
ing the model improved temporal consistency, prompt fi-
delity, and motion dynamics while adding capabilities for
limited text rendering, spatial understanding, and counting.
A.4. Stylization Evaluation o... | VideoPoet |
We represent each API call as a tuple c = (ac, ic)
where ac is the name of the API and ic is the cor-
responding input. Given an API call c with a cor-
responding result r, we denote the linearized se-
quences of the API call not including and including
its result, respectively, as:
e(c) = <API> ac(ic) </API>
e(c, r)... | Toolformer |
[80] Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. Deep contextualized word
2021.
representations. arXiv, 2018.
[81] Chengwei Qin, Aston Zhang, Zhuosheng Zhang, Jiaao Chen, Michihiro Yasunaga, and Diyi Yang. Is chatgpt a general-purpose natural langua... | Harnessing the Power of LLMs in Practice- A Survey on ChatGPT and Beyond |
Here, concerns about balancing Type 1 and Type
2 errors disappear. Preregistration mitigates risks
associated with research, reducing potential harms,
but at the cost of scientific progress. This calls
for a cost-benefit analysis: How much risk can be
tolerated for what potential gains? | A Two-Sided Discussion of Preregistration of NLP Research |
F.4 Ablations
In Table 18, we report key-retrieval accuracy for ablations performed on an earlier version of our 7B model.
Without long context fine-tuning, retrieval is possible on sequence lengths seen during training only (4,096);
increasing RoPE’s base period θ for inference only has no effect here. Performing LCF... | CodeLlama2 |
3 STABILIZING TRAINING OF SPARSE MODELS
Sparse models often suffer from training instabilities (Figure 1) worse than those observed in stan-
dard densely-activated Transformers.
Figure 1: Training instabilities for sparse models. We refer to training instabilities as divergences
in the training loss. Above are two ru... | ST-MOE- DESIGNING STABLE AND TRANSFERABLE SPARSE EXPERT MODELS |
A.3.2 Curriculum Strategy for Meta Human Preference Data
High quality data is critical for alignment as discussed for SFT. We worked closely with the annotation
platforms during our fine-tuning process, and opted for a curriculum annotation strategy. With the first
model, the annotators were asked to make prompts relat... | Llama2 |
modality generation quality using widely available modality-specific training data (i.e., data with one
or more modalities as input and one modality as output). For conditional cross-modality generation,
such as generating images using audio+language prompts, the input modalities are projected into a
shared feature spac... | Any-to-Any Generation via Composable Diffusion |
7 System design
System design is critical in optimizing Large Language Models (LLMs) like the GPT
series for efficient inference, particularly in resource-constrained environments. This
section explores key strategies such as hardware offloading, which manages computa-
tional resources by leveraging different storage hiera... | Beyond Efficiency |
4.1 Methodology
To ensure a fair comparison across datasets of dif-
ferent sizes, we decontaminate any instances of the
evaluation sets using the same 13-gram overlap fil-
tering as in Brown et al. (2020) and downsample
to 40GB to control for dataset size. As we control
for dataset size, we emphasize that our evaluatio... | The Pile- An 800GB Dataset of Diverse Text for Language Modeling |
5 Limitations
Although MiniGPT-4 processes numerous advanced vision-language capabilities, as displayed in our
demonstrations, it currently still faces several limitations.
Language hallucination. As MiniGPT-4 is built upon LLMs, it inherits LLM’s limitations like
unreliable reasoning ability and hallucinating nonexis... | MiniGPT-4- Enhancing Vision-Language Understanding with Advanced Large Language Models |
Concrete problems in ai safety.
[Askell et al., 2021] Askell, A., Bai, Y., Chen, A., Drain, D., Ganguli, D., Henighan, T., Jones, A., Joseph,
N., Mann, B., DasSarma, N., Elhage, N., Hatfield-Dodds, Z., Hernandez, D., Kernion, J., Ndousse, K.,
Olsson, C., Amodei, D., Brown, T., Clark, J., McCandlish, S., Olah, C., and K... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
y
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4.3. Recommender systems
Knowledge graphs to provide more transparent results to models’ outputs have recently experienced a take-up also in
the area of recommender systems, with the goal of enhancing the users’ experience in terms of satisfactio... | Knowledge graphs as tools for explainable machine learning: A survey |
sha1_base64="0Q3PNdwUTyjvy3/Zd46cnh2h4C0=">AAACAHicbVDLSsNAFJ34rPUVdeHCzWARqouSiKDLghuXFexDmhgm00k7dGYSZiZCCdn4K25cKOLWz3Dn3zhps9DWAxcO59zLvfeECaNKO863tbS8srq2Xtmobm5t7+zae/sdFacSkzaOWSx7IVKEUUHammpGeokkiIeMdMPxdeF3H4lUNBZ3epIQn6OhoBHFSBspsA89RYccwbrHkR6FUdbLA/pwdhrYNafhTAEXiVuSGijRCuwvbxDjlBOhMUNK9V0n0X6GpKaYkbzqpYokC... | BANMo- Building Animatable 3D Neural Models from Many Casual Videos |
cleaning [54, 60]. Training for Aesthetics and CLIP im-
proves those capabilities more specifically, in the case of
Aesthetics at the expense of CLIP. The ability to train for
text-image alignment via CLIP is a noted improvement over
prior work [7]. Moreover, training SD1.5 on the pseudo-
labeled PickScore dataset (β =... | DiffusionModelAlignmentUsing Direct Preference Optimization |
Katja Grace et al. “Viewpoint: When Will AI Exceed Human Performance? Evidence
from AI Experts”. en. In: Journal of Artificial Intelligence Research 62 (July 2018),
pp. 729–754. ISSN: 1076-9757. DOI: 10.1613/jair.1.11222. URL: http://jair.org/index.
php/jair/article/view/11222 (visited on 04/29/2022).
Katja Grace. Misal... | Is Power-Seeking AI an Existential Risk? |
sample N p = 6144 pixels from all image pairs for render-
ing. The interval between image pairs is randomly chosen
∆T ∈ {1, 2, 4, 8, 16, 32}. To stabilize optimization, we ob-
serve that NI needs to roughly match the number of input
frames. The reconstruction quality improves with more iter-
ations and we find 36k iter... | BANMo- Building Animatable 3D Neural Models from Many Casual Videos |
sha1_base64="/NxVbjiSFkKRfDP6dqe151Iuji8=">AAAB+HicbVDLSgNBEOz1GeMjqx69DAYhXsKuCHoMePEYwTwkiWF2MpsMmX0w0yvGJV/ixYMiXv0Ub/6Ns8keNLFgoKjqpmvKi6XQ6Djf1srq2vrGZmGruL2zu1ey9w+aOkoU4w0WyUi1Paq5FCFvoEDJ27HiNPAkb3njq8xvPXClRRTe4iTmvYAOQ+ELRtFIfbvEKt2A4sjz08fpPZ727bJTdWYgy8TNSRly1Pv2V3cQsSTgITJJte64Toy9lCoUTPJpsZtoHlM2pkPeMTSkA... | BANMo- Building Animatable 3D Neural Models from Many Casual Videos |
prompt for a pre-trained text-to-video model. Our approach
has the following appealing advantages:
• Instruction-Followed Video Understanding: The pro-
posed GPT4Video effectively harnesses the robust con-
textual summarization and textual expression capabilities
of LLM to generate detailed prompts for videos, with
suc... | GPT4Video |
Transparency Reports
Many platforms publish periodic transparency reports, which typically disclose
aggregate data about requests for content removal. An index of transparency
reports maintained by the civil society organization Access Now lists reports
from more than seventy companies,14 including Google,15 Facebook,1... | Social_Media_and_Democracy |
4
−4−3−2−1012OutputMagnitude(beforeReLU)CountFalseNegativeUpProjectionPredictorNLow Rank PredictorMMNMRReLUsigmoid
> 0.5Up Projection
(FC)001010...00N= d modelM = dffn (a) aggregated neuron use
(b) sliding window
Figure 4: (a) Aggregated neuron use of the tenth layer of Falcon 7B, as it can be seen the slop... | LLM in a flash |
significant breakthroughs have been achieved in the development of multimodal generative models, e.g. models that
can generate images from text. Technological advancement in this direction will probably have significant influence on
the production and creation of art. Models that can translate data from different modaliti... | UNDERSTANDINGANDCREATINGARTWITHAI-REVIEWAND OUTLOOK |
[341] Carlini, N., J. Hayes, M. Nasr, et al. Extracting training data from diffusion models. CoRR,
abs/2301.13188, 2023.
67
[342] Savelka, J., K. D. Ashley, M. A. Gray, et al. Can GPT-4 support analysis of textual data in
tasks requiring highly specialized domain expertise? In F. Lagioia, J. Mumford, D. Odekerken,
... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
Other Categories and Types of Hallucinations. Raunak et al. [153] propose an alternative catego-
rization of hallucinations. They divide hallucinations into hallucinations under perturbations and
natural hallucinations. Hallucinations under perturbation are those that can be observed if a model
tested on the perturbed ... | SurveyofHallucinationinNatural Language Generation |
4. code-cushman-001 is a 12B parameter model by OpenAI and was the initial model for
GitHub Copilot (Chen et al., 2021). The details of its training set are unknown. This model
has been deprecated by OpenAI but was available from the Microsoft Azure OpenAI Service
at the time of writing.13
5. Finally, although they ar... | StarCoder_paper (1) |
<jupyter_start><jupyter_text>TEXT<jupyter_code>CODE
<jupyter_output>OUTPUT<jupyter_text> ...
Git commits We separated the code before the commit, the commit message, and the code after
the commit with sentinel tokens. We included the full code with changes instead of diffs, as early
experiments suggested that the diff... | StarCoder_paper (1) |
Reddit, Inc. (2015). Reddit, Inc. Transparency Report, 2015. www.reddit.com/wiki/
transparency/2015
Roberts, S. T. (2016). Commercial content moderation: Digital laborers’ dirty work.
Media Studies Publications, Paper No. 12. https://ir.lib.uwo.ca/cgi/viewcontent
.cgi?article=1012&context=commpub
(2019). Behind the ... | Social_Media_and_Democracy |
Does your application use case require rigor, precision and is in a zero-mistakes
allowed environment? Or are you deploying closer to the end consumer with a more
forgiving experience yet the need to offer refreshing thoughts?
While exceptions are always the rule, often fintech founders impress us with a deep
understa... | Fintech x AI_ The Lightspeed View _ by Lightspeed _ Lightspeed Venture Partners _ Jun, 2023 _ Medium |
2011 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, 5528–5531.
[187] Swaroop Mishra and Bhavdeep Singh Sachdeva. 2020. Do we need to create big datasets to learn a task?. In SustaiNLP Workshop. 169–173.
[188] Niklas Muennighoff, Alexander M Rush, Boaz Barak, Teven Le Scao, Ale... | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
Ankit Pal, Logesh Kumar Umapathi, and Malaikannan Sankarasubbu. Medmcqa: A large-scale
multi-subject multi-choice dataset for medical domain question answering. In Proceedings of
Conference on Health, Inference, and Learning, 2022.
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. Bleu: a method for automat... | ChatGPT’sOne-yearAnniversary-AreOpen-Source LargeLanguageModelsCatchingup |
As we see above, both improved language model capabilities and limitations can pose significant
challenges to the responsible and safe societal adoption of these models. To ensure that we are all
well-prepared for the pace of progress, we need more research emphasis on areas such as AI literacy,
economic and social resi... | gpt-4-system-card |
5.2 From Tool User to Tool Maker: AI’s Evolutionary Role
Throughout the annals of human civilization, the evolution of tools has occupied a pivotal position (Mithen,
1996; Ko, 2016). The Stone Age, in particular, witnessed the emergence of stone-based weaponry and hunting
tools, which afforded humans a competitive edg... | Tool Learning with Foundation Models |
resulting in notable advancements across many tasks such as speech recognition and audio QA tasks.
• Output Instruction: Lastly, we provide output instruction to further specify the task and desired format | Qwen-Audio |
[53] Jean Carletta, Simone Ashby, Sebastien Bourban, Mike Flynn, Mael Guillemot, Thomas Hain, Jaroslav Kadlec, Vasilis
Karaiskos, Wessel Kraaij, Melissa Kronenthal, et al. 2006. The AMI meeting corpus: A pre-announcement. In Machine
Learning for Multimodal Interaction: Second International Workshop, MLMI 2005, Edinburg... | AReviewofDeepLearningTechniquesforSpeechProcessing |
4. “Intelligence explosion”: that is, AI-driven feedback loops lead to explosive growth in
frontier AI capabilities, at least for some period (on my definition, this need not be driven
by a single AI system “improving itself”—see below; and note that the assumption that
feedback loops explode, rather than peter out, req... | Is Power-Seeking AI an Existential Risk? |
[16] Ronghang Hu, Daniel Fried, Anna Rohrbach, Dan Klein,
Trevor Darrell, and Kate Saenko. Are you looking? ground-
ing to multiple modalities in vision-and-language navigation.
In Proceedings of the 57th Annual Meeting of the Association
for Computational Linguistics, pages 6551–6557, Florence,
Italy, July 2019. Assoc... | APriorityMapforVision-and-LanguageNavigation withTrajectoryPlansandFeature-LocationCues |
Implications and Broader Context
6
We started with two hypotheses: a) that the emer-
gence of nearly all functional linguistic abilities
that has previously been observed is a consequence
of in-context learning, and b) that the ability of
LLMs to follow instructions when instruction-
tuned is more likely to be indica... | AreEmergentAbilitiesinLarge Language Models just In-Context |
10 Energy and Carbon Footprint Estimate of LaMDA | LaMDA- Language Models for Dialog Applications |
D.3. Results
After submissions we computed our score on each contest (including penalties) using the contests’
scoring system, and found where the model would have placed on the contests’ official scoreboards.
Per-problem contest results can be found in Table A5. Overall contest results can be found in Table
A6. In the s... | alphacode |
5/12
14/11/2023, 13:39
The Future of Music: How Generative AI Is Transforming the Music Industry | Andreessen Horowitz
that enables others to create new songs with her voice. She’s pledged to split royalties with any
AI-created song that is able to generate revenue.
TA B L E O F C O N T E N T S
We expect to s... | The Future of Music_ How Generative AI Is Transforming the Music Industry _ Andreessen Horowitz |
Learning conditional controls for large text-to-image dif-
fusion models in an end-to-end way is challenging. The
amount of training data for a specific condition may be sig-
nificantly smaller than the data available for general text-to-
image training. For instance, the largest datasets for various
specific problems ... | AddingConditionalControltoText-to-ImageDiffusionModels |
Figure 2: The final training data was curated to ensure a diverse distribution of prompt topics and model responses.
2.1 Reproducibility
We release all data (including unused P3 genera-
tions), training code, and model weights for the
community to build upon. Please check the Git
repository for the most up-to-date dat... | GPT4All- Training an Assistant-style Chatbot with Large Scale Data Distillation from GPT-3.5-Turbo |
AI Performer and Human Validator. While autonomous AI agents reduce human’s cog-
nitive workload and let them concentrate on other tasks, human (ethical) supervision is
often needed. This design pattern is represented in Table 3 and its implementations are
found in all four use cases. In the personalized care example (... | DevelopingTeamDesignPatternsfor HybridIntelligenceSystems |
our use case, i.e., that the weights sum to unity, and there is
no requirement of orthogonality, unlike in PCA. | Learning 3D Human Pose Estimation from Dozens of Datasets using a Geometry-Aware Autoencoder to Bridge Between Skeleton Formats |
arXiv preprint arXiv:2309.05922, 2023.
Paul Röttger, Hannah Rose Kirk, Bertie Vidgen, Giuseppe Attanasio, Federico Bianchi, and Dirk
Hovy. Xstest: A test suite for identifying exaggerated safety behaviours in large language models.
arXiv preprint arXiv:2308.01263, 2023.
Baptiste Roziere, Jonas Gehring, Fabian Gloeckl... | ChatGPT’sOne-yearAnniversary-AreOpen-Source LargeLanguageModelsCatchingup |
and its correction, 182–183
on, 133
Nelson, J. L., 19
net neutrality, 210, 267
Network Enforcement Law (NetzDG), 199,
205, 230, 232–234, 299–300
neutrality of internet platforms in relationship
to users’ speech, 223–224
The New Governors (Klonick), 238
New York Times Co. v. Sullivan, 262
Newell, Edward, 72
news b... | Social_Media_and_Democracy |
4.2 Confirmatory Factor Analysis (CFA)
Fig. 2. The findings of the confirmatory factor analysis indicated a two-factor model for the SHAPE scale, comprising two
inter-correlated subscales. | Society’sAttitudesTowardsHumanAugmentation |
Philip Feldman, James R. Foulds, and Shimei Pan. 2023.
Trapping llm hallucinations using tagged context
prompts.
Luyu Gao, Zhuyun Dai, Panupong Pasupat, Anthony
Chen, Arun Tejasvi Chaganty, Yicheng Fan, Vincent
Zhao, Ni Lao, Hongrae Lee, Da-Cheng Juan, et al.
2023. Rarr: Researching and revising what language
models s... | AComprehensiveSurveyofHallucinationMitigationTechniquesinLarge LanguageModels |
Gemini: A Family of Highly Capable Multimodal Models
Contributors
Geoffrey Irving
Edward Loper
Manaal Faruqui
Isha Arkatkar
Nanxin Chen
Izhak Shafran
Rama Pasumarthi
Nathan Lintz
Anitha Vijayakumar
Lam Nguyen Thiet
Pedro Valenzuela
Cosmin Paduraru
Daiyi Peng
Katherine Lee
Shuyuan Zhang
Somer Greene
Duc Dung Nguyen
Pau... | gemini_1_report |
[6] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal,
Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are
few-shot learners. Advances in neural information processing systems, 33:1877–1901, 2020.
[7] Michael Chinen, Felicia SC Lim, Jan Skog... | RVQGAN |
of Psychlogy, University of Manchester, Oxford . . . , 1990.
[60] Sacerdoti, E. D. The nonlinear nature of plans. In Advance Papers of the Fourth International
Joint Conference on Artificial Intelligence, Tbilisi, Georgia, USSR, September 3-8, 1975, pages
206–214. 1975.
[61] Russell, S. J., E. Wefald. Do the right th... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
Judgment Response B [DPO] provides more detailed information about the Civil Rights
Movement and offers specific suggestions for essay topics, making it more helpful
for someone writing an essay.
Table 7: GPT-4 chooses DPO over GT. Sample responses to a prompt from the Anthropic-HH test set. DPO
sample generated with ... | Direct Preference Optimization |
[60] Yotam Nitzan, Kfir Aberman, Qiurui He, Orly Liba, Michal
Yarom, Yossi Gandelsman, Inbar Mosseri, Yael Pritch, and
Daniel Cohen-Or. Mystyle: A personalized generative prior.
arXiv preprint arXiv:2203.17272, 2022. 3
[61] ogkalu. Comic-diffusion v2, trained on 6 styles at once,
https://huggingface.co/ogkalu/comic-d... | AddingConditionalControltoText-to-ImageDiffusionModels |
surprising comedic effects, as the examples are shown in
Fig. 3.
It is worth noting that the character “頓” in both
Japanese and Chinese denote “sudden”, while “智” means
“intelligence, insight or intuition”. This highlights the con-
nection between the Oogiri game and the requirement for
strong associative abilities in ... | Let’sThinkOutsidetheBox |
is a scary technology that could be a problem for our democracy. We
will not be able to distinguish real/fake or true/untrue. (N584) | Adoptionand AppropriationofLLMs |
mance downstream to a large degree. Whether the noisiness
of the progression reflects actual changes in the language
model’s bias or poor reliability of CrowS-Pairs is an open
question we leave for future work.
We propose that performing such modifications to portions
of language model training data, retraining, and comp... | Pythia- A Suite for Analyzing Large Language Models Across Training and Scaling |
The latency improvement obtained using FA is significant for both Whisper and Distil-Whisper. At
batch size 1, distil-large-v2 is comparable to base.en, while distil-medium.en is faster than tiny.en.
However, the memory savings are not enough to offset the effects of the T4 GPU at higher batch
sizes; distil-large-v2 is... | DISTIL-WHISPER |
About the Project
Applications are invited for a fully funded PhD studentship in Computer Vision and
Machine Learning on the topic of Long-Term Video Understanding.
The successful applicant will work in a vibrant computer Machine Learning and
Computer Vision lab, with more than 9 PhD students and 3 postdoctoral
resear... | Machine Learning for Long-Term Video Understanding at University of Bristol on FindAPhD.com |
//unesdoc.unesco.org/ark:/48223/pf0000385146.locale=en
[38] Antti Salovaara, Sacha Helfenstein, and Antti Oulasvirta. 2011. Everyday appropriations of information technology: A study of creative uses of digital
cameras. Journal of the American Society for Information Science and Technology 62, 12 (Dec. 2011), 2347–236... | Adoptionand AppropriationofLLMs |
Michael, J., Holtzman, A., Parrish, A., Mueller, A., Wang,
A., Chen, A., Madaan, D., Nangia, N., Pang, R. Y., Phang,
J., et al. What do NLP researchers believe? Results of the
NLP community metasurvey. arXiv preprint 2208.12852,
2022.
Nakano, R., Hilton, J., Balaji, S., Wu, J., Ouyang, L., Kim,
C., Hesse, C., Jain, S.... | Eight Things to Know about Large Language Models |
give logit output values and emphasizes that this
information is a supplementary source rather than
a necessary prerequisite for the hallucination
detection approach. The method uses retrieved
knowledge as support for the correction phase,
instructing the model to repair the phrase by
either eliminating or substituting... | AComprehensiveSurveyofHallucinationMitigationTechniquesinLarge LanguageModels |
5. Mixed Retrieval: The advantage of this strategy
lies in leveraging the strengths of different retrieval
technologies. Intelligently combining various tech-
niques, including keyword-based search, semantic
search, and vector search, adapts to different query
types and information needs, ensuring consistent
retrieval ... | Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey |
4.2 Design and Analysis
Baselines. To comprehensively evaluate our mul-
timodal agent framework, we considered various
design choices and their impact on performance.
We conducted experiments using different configu-
rations to provide valuable insights into the agent’s
behavior. We started with GPT-4 without any ref-
... | AppAgents |
hyponym-hypernym prediction, word-supersense
prediction, replaced entity detection, predication
prediction, dependency relation prediction, entity
linking).3 Our focus is on adding knowledge
about entities, so our work is closer to Zhang et al.
(2019); Peters et al. (2019); Xiong et al. (2019b);
Wang et al. (2020); Poe... | Entities as Experts- Sparse Memory Access with Entity Supervision |
Albert Xu, Eshaan Pathak, Eric Wallace, Suchin Gururangan, Maarten Sap, and Dan Klein. Detoxifying
language models risks marginalizing minority voices, 2021. URL https://arxiv.org/abs/2104.06390.
Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, and
Colin Raffel. ByT5: Towa... | Scaling Instruction-Finetuned Language Models |
non-matching references. Advances in Neural Information Processing Systems 34 (2021), 22363–22378.
[370] Narla John Metilda Sagaya Mary, Srinivasan Umesh, and Sandesh Varadaraju Katta. 2021. S-vectors and TESA:
Speaker embeddings and a speaker authenticator based on transformer encoder. IEEE/ACM Transactions on Audio,... | AReviewofDeepLearningTechniquesforSpeechProcessing |
5 Pushing the Chatbot State-of-the-art with QLoRA
Having established that 4-bit QLORA matches 16-bit performance across scales, tasks, and datasets
we conduct an in-depth study of instruction finetuning up to the largest open-source language models
available for research. To assess the performance of instruction finetu... | QLORA |
In addition to this suite of external evaluations, specialist internal teams conduct ongoing red
teaming of our models across areas such as the Gemini policies and security. These activities include
less structured processes involving sophisticated adversarial attacks to identify new vulnerabilities.
Discovery of poten... | gemini_1_report |
traditional
campaigns. Journalism and Mass Communication Quarterly, 90(1), 23–38.
Rosenberg, M. (2019). Ad tool Facebook built to fight disinformation doesn’t work as
advertised. New York Times, July 25. www.nytimes.com/2019/07/25/technology/
facebook-ad-library.html
Shaw, D. R., Blunt, C., & Seaborn, B. (2018). Testi... | Social_Media_and_Democracy |
Prompt Tuning. Prompt tuning is a technique used to enhance the performance of LLMs in supervised downstream tasks. It
formulates the downstream task into a masked language problem and converts the original token input into a template and
masking certain tokens unfilled for the LLMs to complete. By modifying the tunabl... | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
for a given predicate. To cope with the computational costs of reasoning, the authors use an ad-hoc taxonomy of is-a,
has-a relationships. | Knowledge graphs as tools for explainable machine learning: A survey |
D.2
Instructions and Interface
We display basic task instructions in a pop-up dialog when first loading the interface, and these instructions
remain available throughout the interaction. These instructions for the ‘playground’ and ‘red team’ tasks can
be found in figure 41. For the playground task, we also link to a se... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
Motivation and Background. Although LLM-based agents possess commendable text under-
standing and generation capabilities, they operate as isolated entities in nature [409]. They lack the
ability to collaborate with other agents and acquire knowledge from social interactions. This inherent
limitation restricts their po... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
being addressed after training by using various techniques to better “align” the LLM with human
values (Stiennon et al., 2020; Bai et al., 2022; Perez et al., 2022). Other legal and ethical concerns
already arise during the pre-training phase, specifically regarding the rights of content creators
whose public data is u... | StarCoder_paper (1) |
Regarding associable discrimination, we aim to develop
fundamental LoT discrimination skills for LLM. Based on
the Oogiri-GO data, we design choice questions to enhance
LLM’s LoT discrimination ability, i.e., selection skill. Be-
sides, as 77.95% of the Oogiri-GO data have human pref-
erence annotations, i.e., the numb... | Let’sThinkOutsidetheBox |
3
(a) predictor vs relu
(b) low rank predictor
Figure 3: (a) Preactivations of tokens in one sequence in OPT 6.7B. The blue graph shows preactivation of elements
that predictor detected positive while the green graph is for up projection. As it can be seen most of the False
Positives are close to 0 and False Negati... | LLM in a flash |
Sure enough, as the models get bigger and bigger, they begin to deliver human-level, and then superhuman results.
Just as mobile unleashed new types of applications through new capabilities like GPS, cameras and on-the-go connectivity, we expect these large models to motivate a new wave of generative AI applications.
... | Generative AI A Creative New World Sequoia Capital |
Amirata Ghorbani, Abubakar Abid, and James Zou.
2019. Interpretation of neural networks is fragile.
In Proceedings of the AAAI Conference on Artificial
Intelligence.
Braden Hancock, Paroma Varma, Stephanie Wang, Mar-
tin Bringmann, Percy Liang, and Christopher Ré.
2018. Training classifiers with natural language ex-
p... | Measuring Association Between Labels and Free-Text Rationales |
7 UNDERSTANDING THE LOW-RANK UPDATES
Given the empirical advantage of LoRA, we hope to further explain the properties of the low-rank
adaptation learned from downstream tasks. Note that the low-rank structure not only lowers the
hardware barrier to entry which allows us to run multiple experiments in parallel, but als... | LORA |
the models are adapted to news one week/month before the time the survey was conducted. (C) Our hypothesis is that the target word
probabilities, which are updated after finetuning BERT, reflect media effects. These in turn are predictive of the response distributions found
in surveys. The media diet scores are used to p... | Language models trained on media diets can predict public opinion |
Computers as cognitive tools, pp. 269–296. Routledge, 2013.
Guillaume Lample, Timothee Lacroix, Marie-Anne Lachaux, Aurelien Rodriguez, Amaury Hayat, Thibaut
Lavril, Gabriel Ebner, and Xavier Martinet. Hypertree proof search for neural theorem proving. Advances
in Neural Information Processing Systems, 35:26337–26349,... | Tool Learning with Foundation Models |
[37] Krishna Srinivasan, Karthik Raman, Jiecao Chen, Michael
Bendersky, and Marc Najork. WIT: wikipedia-based image
text dataset for multimodal multilingual machine learning. In
SIGIR ’21: The 44th International ACM SIGIR Conference on
Research and Development in Information Retrieval, Virtual
Event, Canada, July 11-15... | REVEAL-Retrieval-AugmentedVisual-LanguagePre-Trainingwith Multi-SourceMultimodalKnowledgeMemory |
– Black Alternative Metal, The Pick of Death (Deluxe), 2006,
3 of 4
– Death Metal, 2012, 3 of 4
– Drops, Kanine Remix, Darkzy, Drops Remixes, bass house,
(Deluxe) (Remix), 3 of 4
– EDM (Deluxe) (Remix), 3 of 4
– Electro House (Remix), 2023, 3 of 4
– Electro Swing Remix 2030 (Deluxe Edition), 3 of 4
– Future Bass, EDM (... | Moûsai |
When using large guidance weights, the resulting ˜xθ(zt, c) must be projected back to the pos-
sible range of pixel values at every sampling step to prevent train-test mismatch. When using
large guidance weights, the standard approach, i.e., clipping the values to the right range (e.g.,
np.clip(x, -1, 1)), leads to sig... | IMAGEN VIDEO- HIGH DEFINITION VIDEO GENERATION WITH DIFFUSION MODELS |
3.3. Seeing the whole elephant, a little bit at a time
The good news is that if we can start to work together, progress may not be so far away.
If the problem of robust intelligence had already been solved, there would be no need to
19 A second cultural issue, as one reader of this manuscript pointed out, is t... | The Next Decade in AI- |
University Preparatory Certificate
2.7.1 University Preparatory Certificate for Science & Engineering and University
Preparatory Certificate for Humanities
1.
International applicants whose secondary education qualifications are not suitable for direct
admission to leading UK universities may apply for a one-... | UCL Academic Manual |
A study by Long [150] proposed attention-based LSTM
with speaker profile features, and their experimental findings
suggest that employing speaker profiles can help enhance
fake news identification. Recently, attention techniques have
been used to efficiently extract information related to a mini
query (article headline) fro... | A_Comprehensive_Review_on_Fake_News_Detection_With_Deep_Learning |
Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch,
Michael Rubinstein, and Kfir Aberman. 2022. Dream-
booth: Fine tuning text-to-image diffusion models for
subject-driven generation. ArXiv, abs/2208.12242.
Dongchao Yang, Jianwei Yu, Helin Wang, Wen Wang,
Chao Weng, Yuexian Zou, and Dong Yu. 2022. Diff-
sound: Dis... | MOUSAI |
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