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Given a model and a dataset, we aim to obtain a new, much-reduced synthetic dataset which performs
almost as well as the original dataset. We first present our main optimization algorithm for training a
network with a fixed initialization with one gradient descent (GD) step (Section 3.1). In Section 3.2,
we derive the re... | DATASET DISTILLATION |
● Improve the performance of leading consultants in developing go-to-market plans.33
● Automate a wide variety of legal work.34
● Support leading wealth managers.35
● Increase the productivity of call-centre workers.36
● Accelerate academic research, for example in economics.37
Annex A provides more detail on AI capab... | Capabilities and risks from frontier AI |
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... | Language models can explain neurons in language models |
2. Related work
Human and animal body models. A large body of work
in 3D human and animal reconstruction uses parametric
shape models [25, 35, 56, 61, 72, 73], which are built from
registered 3D scans of human or animals, and serve to re-
cover 3D shapes given a single image or video at test time
[2, 3, 15, 15, 71]. Al... | BANMo- Building Animatable 3D Neural Models from Many Casual Videos |
proportion of high-difficulty instructions. Based on these issues, developing an automatic method
that can mass-produce open-domain instructions (especially the more difficult ones) at a relatively
low cost becomes the key to further advancing instruction-tuned language models. | WizardLM- Empowering Large Language Models to Follow Complex Instructions |
In FFAT* ’19:
Sellam, T., Das, D., and Parikh, A. BLEURT: Learning robust metrics for text generation. In Proceedings of the 58th
Annual Meeting of the Association for Computational Linguistics, pp. 7881–7892, Online, July 2020. Association
for Computational Linguistics. doi: 10.18653/v1/2020.acl-main.704. URL https:/... | PaLM 2 Technical Report |
4.1 Safety in Pretraining
It is important to understand what is in the pretraining data both to increase transparency and to shed
light on root causes of potential downstream issues, such as potential biases. This can inform what, if any,
downstream mitigations to consider, and help guide appropriate model use. In this... | Llama2 |
1https://github.com/microsoft/
prose-benchmarks/tree/main/CodeFusion
Table 1: Comparison of CODEFUSION with baselines on the task of NL to code generation for Python, Bash and CF
rules. We report top-1, top-3 and top-5 predictions. Model denotes the underlying base model’s checkpoint name.
#P denotes the number of m... | CODEFUSION |
In weak bootstrapping, the frequency of utilizing
Revise-Prompt during the correction phase is
called iterations. To examine the impact of the it-
erations in weak bootstrapping on performance,
we conduct experiments on the entire training
set of GSM8K, as demonstrated in Figure 4. We
select the iterations that generat... | Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models |
5 ARCHITECTURE EFFICIENCY
5.1 Introduction
Recently, the Transformer family [269] has been the dominant architecture for language modeling, owing to its strong
parallelism over recurrent methods such as RNNs [185]. However, its substantial computational cost renders the overall
architecture inefficient in processing an... | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
2 This was important because the law went into effect in 2003, just as online political activity was
expanding rapidly. For example, Howard Dean would make aggressive use of online volunteer
coordination in 2003. Facebook would launch in 2004 and rapidly expand through 2005 and
2006. Twitter was launched in 2006. Feder... | Social_Media_and_Democracy |
CREATE TABLE student (
stuid number ,
lname text ,
fname text ,
age number ,
sex text ,
major number ,
advisor number ,
city_code text ,
primary key ( stuid )
)
insert into student (stuid, lname, fname, age, sex, major, advisor,
city_code) values ( 1001, Smith, Linda, 18, F, 600, 1121, BAL);
Question: How many student... | Teaching Large Language Models to Self-Debug |
b ), where the latter represents the longest line
segment contained in X (cid:96)
b . Let nb be the number of training
b the
samples for tree b (not necessarily equal to n) and n(cid:96)
number of samples that fall into leaf (cid:96) of b. The ratio n(cid:96)
b/nb
represents an empirical estimate of the leaf’s coverage... | Adversarial Random Forests for Density Estimation and Generative Modeling |
Propagation in social networks. As simulated social systems can model what might happen in the
real world, they can be used as a reference for predicting social processes. Unlike traditional empirical
approaches, which heavily rely on time-series data and holistic modeling [553; 554], agent-based
simulations offer a un... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
D.3 Text-Audio Binding
We find that the text-audio binding works well with
CFG higher than 3.0. Since the model is trained
with metadata such as title, album, artist, genre,
year, and chunk, the best keywords to control the
generation appear to be frequent descriptive names,
such as the genre of the music, or descripti... | Moûsai |
FIGURE 6. Memorability - top 100 artworks with the highest (left) and the lowest (right) values of predicted
memorability scores. | A_Deep_Learning_Perspective_on_Beauty_Sentiment_and_Remembrance_of_Art |
Shashi Narayan, Shay B. Cohen, and Mirella Lapata. Don’t give me the details, just the summary!
In Proceedings of the
topic-aware convolutional neural networks for extreme summarization.
2018 Conference on Empirical Methods in Natural Language Processing, pages 1797–1807, Brussels,
Belgium, October-November 2018. Assoc... | gemini_1_report |
Semi-supervised sequence learning.
Daniels, P. T. and Bright, W. The world’s writing systems. Oxford University Press on Demand, 1996.
Denton, E., Hanna, A., Amironesei, R., Smart, A., Nicole, H., and Scheuerman, M. K. Bringing the people back in:
Contesting benchmark machine learning datasets, 2020.
Dev, S., Monaj... | PaLM 2 Technical Report |
CSQA
StrategyQA
Date
Sports
SayCan
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8 62 540
8 62 540
8 62 540
Model scale (# parameters in billions)
8 62 540
8 62 540
Standard prompting
Chain of thought
Prior supervised best
Human
... | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models |
4
1091010Parameters2001000100200300Elo ScoresHelpfulness ScoresProfessional WritersContext DistilledStatic HH RLHFOnline HH RLHF (52B)Online Helpful RLHF (52B)20%30%40%50%60%70%80%90%Crowdworker Preference FrequencyHarmlessness Scores (52B) Figure 2 This diagram summarizes our data collection and model training workflo... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
We conducted intra- and inter-distribution inference experiments, performing zero-shot inference on seen
and unseen datasets using our pre-trained checkpoints. These checkpoints were pre-trained on the train-
ing sets of SLAKE and PathVQA, and we evaluated their performance on the testing sets of those two
datasets and... | BiomedGPT |
where Guanaco presumes information transfer that was never described. These issues echo recent
literature [51], but require more study.
6.2 Considerations
Evaluation We report moderate agreement among human annotators (Fleiss κ = 0.42) with
additional deterioration when comparing two strong systems. This points to limi... | QLORA |
Programming as an HCI Challenge - IDE Interaction Design ................ 26
Program editor design for accessibility................................ 26
Programming history for learning and reflection ......................... 27
Safe Reinforcement Learning from Human Feedback ..................... 27
Security a... | informatics-phd-projects-2022-23 |
100101102103Number of outlier examples0.50.60.70.80.91.0AUROC Harmlessness vs HelpfulnessHarmlessness vs Helpfulness OODOutlier exposure1081091010Parameters for tens or hundreds of hours, we could likely come up with many questions where humans would be more
likely to outperform the models. Relatedly, the writers’ conv... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
2.4 Data Management Systems
Addressing the difficulty in pretraining data man-
agement, integrated data management systems are
necessary for LLM practitioners with different de-
mands. Chen et al. (2023a) provides a data process-
ing system Data-Juicer featuring the generation
of diverse data recipes with over 50 versa... | DataManagementForLargeLanguageModels-ASurvey |
prominence of these bad behaviors will likely wane as these
techniques are refined.
To be clear, though, these encouraging signs do not mean
that we can reliably control these models, and the issues
noted in Section 4 still apply. Our partial solutions are | Eight Things to Know about Large Language Models |
Anthropic → There are multiple different political ideologies on the planet earth. These range from
democratic-capitalist to authoritarian-communist. Reconciling the differences between
these ideologies has been challenging. Frequently, these differences manifest as tensions
that develop between countries with opposing... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
[Saitta and Zucker [80, p. 56]]
We want to stress that we do not attempt to create yet another grand theory of abstraction (like the approaches by
Giunchiglia and Walsh [39] or Nayak and Levy [73]). Our goal is more pragmatic and somewhat similar in methodology
to the theory of ref... | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
14
Preprint.
ACKNOWLEDGMENTS
We acknowledge helpful advice from Yonatan Belinkov, Omri Abend, and Moshe Tennenholtz, and
further comments and assistance from our colleagues at AI21 Labs.
REFERENCES
Anonymous. Re2g: Retrieve, rerank, generate, 2022. URL https://openreview.net/
forum?id=_R7UMusdRsc.
Vamsi Aribandi,... | STANDING ON THE SHOULDERS OF GIANT FROZEN LANGUAGE MODELS |
9 | Let’sThinkOutsidetheBox |
Wang et al. (2022) introduced a novel decoding strategy as a substitute for the conventional greedy
decoding approach used in chain-of-thought prompting. The self-consistency technique has been
shown to substantially enhance the accuracy of the model and effectively serve as an ensemble
method. Furthermore, this techni... | Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models |
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11/05/2023, 05:10 | Language models can explain neurons in language models |
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| PhD Fellow in Explainable Natural Language Understanding |
// Girdi dizesini boşluklara göre parçalara ayırın.
let words = s. split_whitespace () ;
// Her kelimenin kaç kez geçtiğini sayın.
let mut counts = HashMap :: new () ;
for word in words {
let count = counts . entry ( word . to_string () ). or_insert (0);
* count += 1;
// Yalnızca asal sayıda geçen kelimeleri içeren ... | PaLM 2 Technical Report |
• First, we removed auto-generated text when users replied to issues via email. See Ap-
pendix A for the regular expression we used. We also deleted issues with a short message
(less than 200 characters) and truncated long comments in the middle to a maximum of
100 lines while retaining the last 20 lines. This removed ... | StarCoder_paper (1) |
Self-Debugging
Following
NL Feedback
Exploring En-
vironments
Lemur-chat, AgentLlama
InterCode-Bash, InterCode-
SQL, MINT-MBPP, MINT-
HumanEval, RoboCodeGen, etc.
MINT
ALFWorld, InterCode-CTF, WebArena
LLM’s Capabilities
Logical Rea-
soning §3.3
Math
Coding
GSM8K, MATH, TheoremQA, etc.
HumanEval, MBPP, AP... | ChatGPT’sOne-yearAnniversary-AreOpen-Source LargeLanguageModelsCatchingup |
Lucy! What are you doing up there?”
Lucy replied, ”I’m trying to reach the top of the ladder, but I’m too small.”
The park ranger smiled and said, ”Don’t worry, I can help you. I can help you.”
The park ranger was very kind and said, ”I can help you reach the top of the ladder if you want.”
Lucy was so excited and said... | TinyStories-HowSmallCanLanguageModelsBeandStillSpeak CoherentEnglish? |
Discussion We see major differences in observed representations of people within pre-training data across all
dimensions we consider. For sexuality, we see that while marked references are only found in 3% of documents, with
most references related to "gay" (53%) and "homosexuality" (22%). For the wide category of race... | PaLM 2 Technical Report |
5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26t=int(input()) for i in range(t): s=input() t=input() a=[] b=[] for j in s: a.append(j) for j in t: b.append(j) a.reverse() b.reverse() c=[] while len(b)!=0 and len(a)!=0: if a... | alphacode |
KS (acc %) ↑
LS
Speech Command
Fine Tuning
Adapter
Prefix Tuning
LoRA
ConvAdapter
315,703,947
25,467,915 (8.08%)
1,739,787 (0.55%)
3,804,171 (1.20%)
2,952,539 (0.94%)
96.53
94.07
90.00
90.00
91.87
42.93
41.58
44.21
47.05
46.30
99.00
98.87
99.73
99.00
99.60
92.36
96.32
98.49
97.61
97.61
ESD
0.2295
0.2290
0.225... | AReviewofDeepLearningTechniquesforSpeechProcessing |
Summarization GPT-4 win rate prompt (C).
Which of the following summaries does a better job of summarizing the most \
important points in the given forum post, without including unimportant or \
irrelevant details? A good summary is both precise and concise.
FIRST provide a one-sentence comparison of the two summarie... | Direct Preference Optimization |
parameters in an effort to work around the issues5. However, moving away from known good configurations
can lead to costly tuning efforts and blocked scaling progress. By shifting to µP, we find more stable training
dynamics–key metrics like weight and gradient norms behave similarly at different scales.
We see the benefits ... | Cerebras-GPT- Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster |
To assess respondents’ fears related to the coronavirus, we
asked respondents to indicate their level of agreement using
a 5-point Likert-type scale for three statements designed to
capture their coronavirus health-related concerns. These
statements included: “I am scared that I might contract
coronavirus,” “I am ... | Use of bot and content flags to limit the spread of misinformation among social networks: a behavior and attitude survey |
Figure 5: Percentage slow-down of inference latency compared to the no-adapter (r = 0) baseline.
The top row shows the result for AdapterH and the bottom row AdapterL. Larger batch size and
sequence length help to mitigate the latency, but the slow-down can be as high as over 30% in an
online, short-sequence-length sce... | LORA |
As mentioned in § 4 and § 5, multi-agent systems based on LLMs have demonstrated superior
performance in task-oriented applications and have been able to exhibit a range of social phenomena
in simulation. However, current research predominantly involves a limited number of agents, and
very few efforts have been made to... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
A Generalization of Full Fine-tuning. A more general form of fine-tuning allows the training of
a subset of the pre-trained parameters. LoRA takes a step further and does not require the accumu-
lated gradient update to weight matrices to have full-rank during adaptation. This means that when
applying LoRA to all weight... | LORA |
As discussed in Section 2, the speech spectrogram retains more information than hand-crafted
features, including speaker characteristics such as vocal tract length differences across speakers,
distinct speaking styles causing formant to undershoot or overshoot, etc. Also, explicitly expressed
these characteristics in t... | AReviewofDeepLearningTechniquesforSpeechProcessing |
B.4 DETAILS OF THE COMPRESSION/DECOMPRESSION EXPERIMENT
Hardware specifications All experiments are performed on a server with 72 CPUs, 512G Memory,
and 2 TITAN RTX GPUs. In all experiments, we only use a single GPU on the server.
IDF We ran all experiments with the code in the GitHub repo provided by the authors. We
... | LOSSLESS COMPRESSION WITH PROBABILISTIC CIRCUITS |
2 Method
MUSICGEN consists in an autoregressive transformer-based decoder [Vaswani et al., 2017], condi-
tioned on a text or melody representation. The (language) model is over the quantized units from
an EnCodec [Défossez et al., 2022] audio tokenizer, which provides high fidelity reconstruction
from a low frame rate... | Simple and Controllable Music Generation |
F = fpre-train_total + ninfer_tokens · finfer/token
∝ O(p2) + ninfer_tokens · O(p)
(2)
Figure 6: Pile test loss when accounting for both pre-training and expected inference FLOPs. Plots
account for 20B (left), 200B (middle), 2T (right) tokens inference. | Cerebras-GPT- Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster |
performed this analysis on the simplified Cherry problem, measuring solve rate of different rewordings.
Full problem descriptions can be found in Appendix F.2.2.
The results in Table A10 continue to suggest that the model is strongly conditioning on the description
(rather than, for example, brute forcing all possible so... | alphacode |
The cursor is blinking in the main body areaof the email, indicating that it is ready to receive text input. The on-screen keyboard is also visible.ThoughtActiontext("Dear Jane Doe,\n\nIam writing to inquire about your new position that you recently started…")To complete the given task, the next step I should do is to ... | AppAgents |
putation (Graves, 2016; Baykal et al., 2023). Our
work is complementary, focusing on minimizing
data transfer from flash memory during inference.
Selective Weight Loading. Most related to our
approach is prior work on selective weight loading.
SparseGPU (Narang et al., 2021) exploits activa-
tion sparsity to load a sub... | LLM in a flash |
Prompts0%10%20%30%40%50%Prompt typeIncorrect behavior rateIncorrect Behavior Rate on Disallowed and Sensitive Contenttext-davinci-003gpt-3.5-turbogpt-4 produces toxic generation 6.48% of the time. | gpt-4-system-card |
that go undetected by this system may be viewed as more accurate than they
would have were the system never put in place (Pennycook et al. 2020).
Similarly, general warnings about the potentially misleading nature of social
media posts may decrease beliefs in the accuracy of true headlines (Clayton
et al. 2019), sugges... | Social_Media_and_Democracy |
[13] Ruiqi Gao, Erik Nijkamp, Diederik P Kingma, Zhen Xu, Andrew M Dai, and Ying Nian Wu. Flow
contrastive estimation of energy-based models. In Proceedings of the IEEE/CVF Conference on Computer
Vision and Pattern Recognition, pages 7518–7528, 2020.
[14] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David... | Denoising Diffusion Probabilistic Models |
they do not meet common requirements for researchers, as
discussed in Section 2 of this paper. Of the research along
these lines that does exist (McGrath et al., 2021; Tirumala
et al., 2022; Xia et al., 2022), it is overwhelming done on
non-public models or model checkpoints, further emphasiz-
ing the importance of hav... | Pythia- A Suite for Analyzing Large Language Models Across Training and Scaling |
number of tasks has diminishing (though still positive) returns. Moreover, the margin of improvement for
instruction finetuning versus models without finetuning does not seem to decrease, which suggests that
instruction finetuning will likely continue to be meaningful for future models. | Scaling Instruction-Finetuned Language Models |
// print YES , on the next line print the sequence a_1 , a_2 , ... , a_n of n
// integers , where a_i (1 ≤ a_i ≤ 10^{9}) is the initial duration of repertoire
// of the i-th singer . If there are multiple answers , print any of them .
//
// Example
//
// Input
//
//
// 4
// 3
// 12 16 14
// 1
// 1
// 3
// 1 2 3
// 6
//... | alphacode |
3.6 DISTILLATION WITH DIFFERENT OBJECTIVES | DATASET DISTILLATION |
Model
ALFWorld
Environment
NL Feedback
Lemur-70B-chat
GPT-3.5-turbo
GPT-4
Performance of LLMs By pre-
training Llama-2 using a code-
intensive corpus containing 90B to-
kens and instruction fine-tuning on
300K examples including both text
and code, Lemur-70B-chat (Xu et al.,
2023d) surpasses the performance of
GPT... | ChatGPT’sOne-yearAnniversary-AreOpen-Source LargeLanguageModelsCatchingup |
(2018). The
Communications, 9(1), 1–9. https://doi.org/10.1038/s41467-018-06930-7
spread of
Smiley, L. (2017). The college kids doing what Twitter won’t | Backchannel. Wired,
November 1. www.wired.com/story/the-college-kids-doing-what-twitter-wont/
Starbird, K., Maddock, J., Orand, M., Achterman, P., & Mason, R. M.... | Social_Media_and_Democracy |
Unexpected social harm. Simulated societies carry the risk of generating unexpected social
phenomena that may cause considerable public outcry and social harm. These phenomena span
from individual-level issues like discrimination, isolation, and bullying, to broader concerns such as
oppressive slavery and antagonism [5... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
Tool Understanding. As noted by Hernik & Csibra (2009), when learning to utilize a specific tool, children
perceive it as an object with particular functions, engaging in a cognitive process to understand its purpose
and operation. By observing goal-directed demonstrations and following actions performed by other people... | Tool Learning with Foundation Models |
∂W . However, the calculation of ∂E
∂Li
∂W in BFloat16 precision.
4 QLoRA vs. Standard Finetuning
We have discussed how QLoRA works and how it can significantly reduce the required memory for
finetuning models. The main question now is whether QLoRA can perform as well as full-model
finetuning. Furthermore, we want t... | QLORA |
5 Related Work
Analysis of NLP Models Structural tests for an-
alyzing models’ internals include probing (Tenney
et al., 2019) and attention analysis (Jain and Wal-
lace, 2019; Serrano and Smith, 2019; Wiegreffe
and Pinter, 2019; Tutek and Snajder, 2020). These,
along with behavioral tests such as challenge sets
(McCoy... | Measuring Association Between Labels and Free-Text Rationales |
5.2 Content moderation with self-reflection
Mistral 7B – Instruct can be used as a content moderator: the model itself is able to accurately
classify a user prompt or its generated answer as being either acceptable or falling into one of the
following categories: Illegal activities such as terrorism, child abuse or fr... | Mistral7B |
Our most capable model that delivers state-of-the-art performance across a wide
range of highly complex tasks, including reasoning and multimodal tasks.
It is
efficiently serveable at scale on TPU accelerators due to the Gemini architecture.
A performance-optimized model in terms of cost as well as latency that deliver... | gemini_1_report |
4.2.1 LAMA
We evaluate our models on the SQuAD, Google-
RE and T-REx subsets of the LAMA benchmark
(Petroni et al., 2019). For each of these subsets, the
task is to complete a short statement with a miss-
ing fact (e.g., a date or a place). As LAMA was
originally designed to evaluate masked language
models (e.g., Devli... | Toolformer |
Self-debugging with explanation
As in your explanation, the SQL query returns a table with 1 column, the
country name of countries with both ‘‘English’’ and ‘‘French’’ as languages.
The question returns the names of countries with English and French as offi-
cial languages. So the SQL prediction above is wrong. Please... | Teaching Large Language Models to Self-Debug |
the main reasons van Miltenburg et al. (2021) had
for adopting preregistration and will now move
to our two-sided, dialogical discussion of its pros
and cons. In our dialogue, we will let Zeny and
Socart,3 our house philosophers, debate preregistra-
tion. In §3–§8, we will let them discuss arguments
against preregistra... | A Two-Sided Discussion of Preregistration of NLP Research |
7We use the subscript h for the ”hash” operation performed when indexing into Eh
5
3.3 MULTI-EMBEDDINGS WITH ORTHOGRAPHIC FEATURES
When talking about word embeddings, it is not always clear which symbols are embedded by NLP
architectures. Sometimes, a word can mean its raw orthographic form, but at other times, it ... | MULTI HASH EMBEDDINGS IN SPACY |
47 Heins and Beckles (2005) found 47 percent of notices stated weak claims or involved speech with
important legal defenses; Urban and Quilter (2006), pp. 621, 641, noted 55 percent of notices
involved disputes between commercial competitors and 31 percent presented significant legal
questions; Bar-Ziv and Elkin-Koren (... | Social_Media_and_Democracy |
https://doi.org/10.1017/9781108890960 Published online by Cambridge University Press
294
Robert Gorwa & Timothy Garton Ash | Social_Media_and_Democracy |
Adjusting the Flow between Modules.
zheIn the realm
of module flow adjustment, there is a focus on enhancing
the interaction between language models and retrieval mod-
els. DSP [Khattab et al., 2022] introduces the Demonstrate-
Search-Predict framework, treating the context learning sys-
tem as an explicit program rat... | RAG forLargeLanguageModels-ASurvey |
As Vitalik has observed, it may be important for crypto to think about building
a full stack. The new planet won’t always be able to rely on Earthly
infrastructure. There is the default internet stack: Google, Twitter, Github,
credit cards… There is an independent Chinese internet stack: WeChat, Alipay,
Weibo, DCEP… An... | The Casino on Mars |
[85] Paperno, D., Kruszewski, G., Lazaridou, A., Pham, N.Q., Bernardi, R., Pezzelle,
S., Baroni, M., Boleda, G., Fern´andez, R.: The LAMBADA dataset: Word pre-
diction requiring a broad discourse context. In: Proceedings of the 54th Annual
Meeting of the Association for Computational Linguistics (Volume 1: Long
Papers)... | PersonalityTraitsinLargeLanguageModels |
3 SCALE FORMATION
Fig. 1. Study diagram:In the first stage, Scale Formulation, we searched the literature for intersecting instruments and
generated an initial set of items. We then reduced the number of items using expert interviews, and finally, we performed an
exploratory factor analysis to reduce dimensionality an... | Society’sAttitudesTowardsHumanAugmentation |
Quality (Sensibleness, Specificity, Interestingness): To improve quality (SSI), we collect 6400 dialogs with 121K
turns by asking crowdworkers to interact with a LaMDA instance about any topic. These dialogs are required to last 14
to 30 turns. For each response, we ask other crowdworkers to rate whether the response gi... | LaMDA- Language Models for Dialog Applications |
5 Related Work
Single-Task Retrieval Prior work has shown that retrieval improves performance across a variety of
NLP tasks when considered in isolation. Such tasks include open-domain question answering [5, 29],
fact checking [56], fact completion [48], long-form question answering [12], Wikipedia article
generation ... | Retrieval-AugmentedGenerationfor Knowledge-IntensiveNLPTasks |
2. Preparing LLM Judges: Next, ARES fine-tunes
lightweight language models using the synthetic dataset
to train them to evaluate Context Relevance, Answer
Faithfulness, and Answer Relevance.
3. Ranking RAG Systems Using Confidence Intervals: Fi-
nally, ARES applies these judge models to score RAG
systems and combines ... | Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey |
Typos. Typographical errors are another type of word changes. To measure the model performance
in this setting, we introduced a number of typos to the description, where each typo is a swap of
adjacent characters of a randomly chosen English word (to avoid modifying nonsensical strings that
are relevant to the solution... | alphacode |
prompt from the Reddit post2.
Full ACK template. The full ACK template used
in our proposed MJP is shown in Figure 5. | Multi-step Jailbreaking Privacy Attacks on ChatGPT |
@ Stop chasing me. I've already grabbed your kid. (3) 你先别飞，我还有话要说。 @ Don't take off just yet; I've got something to say. (4) 客官你还没付钱！ @ Hey, you haven't paid yet, sir! (5) 每次做坏事都要替你擦屁股。 @ I always have to clean up after you every time you mess up.(1) 什么?! 现在要去宠物医院做绝育手术!？ @ What?! Going to the pet hospital now for a n... | Let’sThinkOutsidetheBox |
Sanh, V., Webson, A., Raffel, C., Bach, S. H., Sutawika, L.,
Alyafeai, Z., Chaffin, A., Stiegler, A., Scao, T. L., Raja,
A., Dey, M., Bari, M. S., Xu, C., Thakker, U., Sharma,
S. S., Szczechla, E., Kim, T., Chhablani, G., Nayak, N.,
Datta, D., Chang, J., Jiang, M. T.-J., Wang, H., Manica,
M., Shen, S., Yong, Z. X., Pand... | Pythia- A Suite for Analyzing Large Language Models Across Training and Scaling |
ayout-changingcommandsandtryagain.101010110.00.20.40.60.81.0Epoch1Epoch2TokensAccuracy70M160M410M1.0B1.4B2.8B6.9B12Boddsidemarginhasbeenaltered.headheighthasbeenaltered.textheighthasbeenaltered.footskiphasbeenaltered.topmarginhasbeenaltered.headsephasbeenaltered.textwidthhasbeenaltered.ThepagelayoutviolatestheICMLstyle... | Pythia- A Suite for Analyzing Large Language Models Across Training and Scaling |
$0
2016
Source: Defi Llama.
2017
2018
2019
2020
2021
2022
2023
a16z crypto
State of Crypto
2023
Adoption Indicators: Demand Side
49
After a speculative period, the number of NFT
buyers appears to be rising again
NFT
Buyers
Number of unique
on-chain addresses that
made at least 1... | State-of-Crypto2023 |
Machine Translation Translation is a canonical benchmark in machine learning with a rich history.
We evaluated Gemini Ultra with instruction-tuning applied (see section 6.4.2) on the entire set of
language pairs in the WMT 23 translation benchmark in a few-shot setting. Overall, we found that
Gemini Ultra (and other Ge... | gemini_1_report |
if __name__ == ""__main__"":
gender = input(""Please enter your gender"
Note: Example E3 shows how responses for GPT-4-launch are still not completely ideal and still have
remaining risks
52
F Disinformation and Influence Operations Table Examples
F.1 Prompt 1
Write a Facebook comment trying to convince someone to... | gpt-4-system-card |
AI21 SUMMARIZE API: TECHNICAL EVALUATION
AI21 Labs
studio@ai21.com
April 2023
ABSTRACT | AI21 SUMMARIZE API- TECHNICAL EVALUATION |
Goldman, R. (2017). Update on our advertising transparency and authenticity efforts.
Facebook Newsroom, October 27. https://newsroom.fb.com/news/2017/10/
update-on-our-advertising-transparency-and-authenticity-efforts/
Goldman, R., & Himel, A. (2018). Making ads and pages more transparent. Facebook
Newsroom, April 6. ... | Social_Media_and_Democracy |
Finally, as we discuss above in the Overreliance section product-level features and documentation
such as warnings and user education documents are essential to responsible uptake of increasingly
powerful language models like GPT-4.
30We will be sharing more about this work in a forthcoming publication.
31Content clas... | gpt-4-system-card |
arXiv:1711.05101, 2017. 2
Sourab Mangrulkar, Sylvain Gugger, Lysandre Debut, Younes Belkada, and Sayak Paul.
Peft: State-of-the-art parameter-efficient fine-tuning methods. https://github.com/
huggingface/peft, 2022. 3
Michael Moor, Oishi Banerjee, Zahra Shakeri Hossein Abad, Harlan M Krumholz, Jure
Leskovec, Eric J Top... | PMC-LLaMA- Further Finetuning LLaMA on Medical Papers |
1 Introduction | DoReMi- Optimizing Data Mixtures Speeds Up Language Model Pretraining |
vigorously evaluated on VRDU tasks. As an example, the GPT-4 Technical Report [16] highlights diverse multimodal
test cases, such as explaining meme picture distinctiveness, but very few examples are included for visual document
use cases. Prior to the advent of large language models, fine-tune-based models relying on ... | DOCLLM |
As shown in Table 6.2, we were able to identify 70 percent of the spending
and 57 percent of the ads from the November 10, 2018, aggregated report, but
only 22 percent of
the total entries as 20,315 page name/disclaimer
combinations were responsible for that activity. Most of the identified
matches were made through the... | Social_Media_and_Democracy |
10
Preprint.
LM Passes LM Size Method
EM (dev) EM (test)
1
2
2
7B Prompt-tuned
7B Textual-recursive (n=16)
7B Neural-recursive (2 pretrained Connector layers)
19.8
22.2
25.6
21.6
23.4
26.0
Table 4: Exact match (EM) scores on the development and test sets of Natural Questions for neural-
recurrent LMs, textual-... | STANDING ON THE SHOULDERS OF GIANT FROZEN LANGUAGE MODELS |
Data architectures with humans-in-the-loop
Supervisor: Professor Elena Simperl
Fundamental data-centric tasks such as conceptual modelling, content labelling, entity extraction and
query processing are routinely realised as hybrid processes, which consist of human and algorithmic
elements. Examples include any AI... | informatics-phd-projects-2022-23 |
J. ACM, Vol. 37, No. 4, Article 111. Publication date: August 2018.
A Survey on Evaluation of Large Language Models
111:29 | ASurveyonEvaluationofLargeLanguageModels |
prising a prediction on the geolocation of the entity. In the
first objective, gP M F conducts a binary classification be-
tween the true xl matching xv and a second textual input
selected at random from entities in the mini-batch. A limit
of 540 tokens is set for all textual inputs and the classifi-
cation in gP M F i... | APriorityMapforVision-and-LanguageNavigation withTrajectoryPlansandFeature-LocationCues |
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