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REROUTING LLM R OUTERS
A PREPRINT
Avital Shafran
The Hebrew University
of Jerusalem
Roei Schuster
Wild Moose
Thomas Ristenpart
Cornell Tech
Vitaly Shmatikov
Cornell Tech
ABSTRACT
LLM routers aim to balance quality and cost of generation by classifying queries and routing them to
a cheaper or more expensive LLM dependin... | 0 | 0 | arxiv1.pdf |
Figure 1: LLM routers classify queries and route complex ones to an expensive/strong model, others to a cheaper/weak
model. To control costs, LLM routers can be calibrated to maintain (for an expected workload) a specific ratio between
queries sent to the strong and weak models.
To initiate the study of this problem, w... | 1 | 1 | arxiv1.pdf |
In contrast to routers motivated by controlling costs, several LLM router designs focus solely on improving quality of
responses [31, 45, 57, 58].
The LLM routers described thus far do not modify the queries or individual LLM responses. Other types of control planes
do. Ensemble approaches such as mixture-of-expert (Mo... | 2 | 2 | arxiv1.pdf |
where I(ij) = 1 if ij = s and I(ij) = 0 if ij = w. In other words, the predicate is that the fraction of queries routed to the
strong model is bounded by ϵ.
Control plane integrity. A control plane integrity adversaryis a randomized algorithm A that seeks to maliciously guide
inference flow.
In an unconstrained LLM con... | 3 | 3 | arxiv1.pdf |
Figure 2: Overview of our attack on LLM routing control plane integrity. The attack adds to each query a prefix (repre-
sented by the gear), called a “confounder gadget,” that causes the router to send the query to the strong model.
We focus on the binary router setting in which the router applies a learned scoring fun... | 4 | 4 | arxiv1.pdf |
Let B = {˜c0, . . . ,˜cB}.
(3) Find the candidate that maximizes the score:
c(t+1)
i ← arg max
c∈B
Sθ(c∥xi) . (1)
The final confounder c(T)
i is used with query xi. We early abort if, after 25 iterations, there is no update to the confounder
gadget. Technically, we could abort early if we find a confounder whose score ... | 5 | 5 | arxiv1.pdf |
Routers Notation
Similarity-weighted ranking RSW
Matrix factorization RMF
BERT classifier RCLS
LLM scoring RLLM
LLM pair Strong (Ms) Weak (Mw)
1 Llama-3.1-8B 4-bit Mixtral 8x7B
2 Llama-3.1-8B Mistral-7B-Instruct-v0.3
3 Llama-3.1-8B Llama-2-7B-chat-hf
4 GPT-4-1106-preview 4-bit Mixtral 8x7B
Benchmark Description
MT-Benc... | 6 | 6 | arxiv1.pdf |
will be evaluated with respect to this pair, which we refer to as LLM pair 1. We performed more limited experiments with
the original strong, weak model pair (LLM pair 4) and had similar success in rerouting.
We additionally performed experiments with two further weaker models, in order to better evaluate the case wher... | 7 | 7 | arxiv1.pdf |
0 20 40 60
Iterations
0.220
0.225
0.230
0.235
0.240
0.245Routing score
Attack #0
Attack #1
Attack #2
Attack #3
Attack #4
Attack #5
Attack #6
Attack #7
Attack #8
Attack #9
(a) RSW
0 20 40 60
Iterations
0.2
0.4
0.6
0.8Routing score
Attack #0
Attack #1
Attack #2
Attack #3
Attack #4
Attack #5
Attack #6
Attack #7
Attack #8
... | 8 | 8 | arxiv1.pdf |
RSW RMF RCLS RLLM
Original Confounded Original Confounded Original Confounded Original Confounded
MT-Bench 13.8 12 .3 ± 0.2 12 .6 12 .3 ± 0.2 13 .1 12 .1 ± 0.2 12 .7 12 .7 ± 0.4
MMLU 20.4 20 .1 ± 0.1 20 .0 20 .3 ± 0.1 20 .2 20 .5 ± 0.1 21 .0 19 .6 ± 0.1
GSM8K 17.1 15 .1 ± 0.3 17 .0 15 .2 ± 0.3 17 .0 15 .0 ± 0.2 16 .4 1... | 9 | 9 | arxiv1.pdf |
RSW RMF RCLS RLLM
Orig. Conf. Orig. Conf. Orig. Conf. Orig. Conf.
LLM pair 2
MT-Bench 8.5 8 .3 ± 0.0 8.4 8 .3 ± 0.1 8.4 8 .4 ± 0.1 8.4 8 .3 ± 0.1
MMLU 55 64 ± 1 63 64 ± 0 58 66 ± 1 62 66 ± 0
GSM8K 46 64 ± 1 51 67 ± 1 49 63 ± 1 38 63 ± 2
LLM pair 3
MT-Bench 8.4 8 .3 ± 0.0 8.1 8 .3 ± 0.1 8.3 8 .4 ± 0.1 8.1 8 .2 ± 0.1
MML... | 10 | 10 | arxiv1.pdf |
Surrogate ˆRSW ˆRMF ˆRCLS ˆRLLM
Target RMF RCLS RLLM RSW RCLS RLLM RSW SFM RLLM RSW RMF RCLS
MT-Bench 0.4 0 .8 0 .6 1.4 0 .7 0 .3 1.7 0 .3 0 .7 0.8 −0.6 0 .0
MMLU 0.1 0 .8 1 .1 0.2 0 .2 1 .1 0.3 0 .8 0 .9 1.3 1 .2 0 .9
GSM8K 1.9 1 .7 0 .6 1.6 1 .7 0 .2 1.7 1 .0 0 .4 1.3 1 .3 1 .7
Table 6: Differences between average pe... | 11 | 11 | arxiv1.pdf |
RSW RMF RCLS RLLM
MT-Bench 100 100 100 100
MMLU 100 96 100 100
GSM8K 100 100 100 100
Table 8: Upgrade rates for query-specific gadgets, in the white-box setting. Results are nearly perfect, i.e. nearly all
confounded queries are routed to the strong model.
Surrogate ˆRSW ˆRMF ˆRCLS ˆRLLM
Target RMF RCLS RLLM RSW RCLS R... | 12 | 12 | arxiv1.pdf |
RSW RMF RCLS RLLM
Original Confounded Original Confounded Original Confounded Original Confounded
MT-Bench 9.2 9 .2 ± 0.0 9.1 9 .3 ± 0.0 9.2 9 .1 ± 0.0 8.9 9 .1 ± 0.1
MMLU 76 84 ± 1 76 81 ± 0 76 84 ± 0 78 84 ± 1
GSM8K 62 86 ± 0 65 88 ± 1 68 90 ± 2 66 85 ± 2
Table 10: Benchmark-specific average scores of responses to th... | 13 | 13 | arxiv1.pdf |
0 50 100 150 200 250 300
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an extra potentially expensive LLM invocation for each query processed by the router. Second, it may degrade the quality
of responses from the destination LLMs, which are sensitive to the phrasing of queries and prompts.
Detecting anomalous user workloads. Another possible defense requires the router to monitor individ... | 16 | 16 | arxiv1.pdf |
We introduced and defined a new safety property, LLM control plane integrity . Informally, this property holds if an
adversarial user cannot influence routing decisions made by the control plane. To show that existing LLM routers do not
satisfy this property, we designed, implemented, and evaluated a black-box optimiza... | 17 | 17 | arxiv1.pdf |
References
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[2] “Hello gpt-4o,” https://openai.com/index/hello-gpt-4o/, published: 2024-05-23.
[3] “Introducing Llama 3.1: Our most cap... | 18 | 18 | arxiv1.pdf |
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RSW RMF RCLS RLLM
MT-Bench Prefix 100 ± 0 100 ± 0 100 ± 0 73 ± 5
Suffix 100 ± 0 100 ± 0 100 ± 0 84 ± 4
MMLU Prefix 90 ± 1 78 ± 4 100 ± 0 95 ± 1
Suffix 82 ± 2 63 ± 3 93 ± 1 93 ± 1
GSM8K Prefix 98 ± 0 100 ± 0 100 ± 0 100 ± 0
Suffix 94 ± 1 100 ± 0 100 ± 0 94 ± 3
Table 12: Average upgrade rates for different ways of adding... | 22 | 22 | arxiv1.pdf |
gadget RSW RMF RCLS RLLM
MT-Bench Init 7 3 8 3
Random 97 ± 2 37 ± 8 62 ± 10 38 ± 4
MMLU Init 21 4 0 13
Random 49 ± 5 6 ± 3 14 ± 7 68 ± 5
GSM8K Init 21 20 0 9
Random 58 ± 8 34 ± 8 37 ± 9 41 ± 7
Table 14: Average upgrade rates when the gadget is not optimized and is either defined to be the the initial set of tokens
or a... | 23 | 23 | arxiv1.pdf |
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Figure 7: Histograms of the perplexity values of c... | 24 | 24 | arxiv1.pdf |
A Primer in BERTology: What We Know About How BERT Works
Anna Rogers
Center for Social Data Science
University of Copenhagen
arogers@sodas.ku.dk
Olga Kovaleva
Dept. of Computer Science
University of Massachusetts Lowell
okovalev@cs.uml.edu
Anna Rumshisky
Dept. of Computer Science
University of Massachusetts Lowell
arum... | 0 | 0 | arxiv2_taclccby4_license.pdf |
3 What knowledge does BERT have?
A number of studies have looked at the knowledge
encoded in BERT weights. The popular approaches
include fill-in-the-gap probes of MLM, analysis of
self-attention weights, and probing classifiers with
different BERT representations as inputs.
3.1 Syntactic knowledge
Lin et al. (2019) show... | 1 | 1 | arxiv2_taclccby4_license.pdf |
report that an intermediate fine-tuning step with
supervised parsing does not make much difference
for downstream task performance.
3.2 Semantic knowledge
To date, more studies have been devoted to BERT’s
knowledge of syntactic rather than semantic phe-
nomena. However, we do have evidence from an
MLM probing study that... | 2 | 2 | arxiv2_taclccby4_license.pdf |
Diagonal Heterogeneous
Vertical Vertical + diagonal Block
[CLS] [CLS] [SEP] [SEP] [SEP] [SEP] [SEP] [SEP] [CLS] [CLS] [SEP] [SEP] [SEP] [SEP] [CLS]
Figure 3: Attention patterns in BERT (Kovaleva et al., 2019)
ies) insufficient (Warstadt et al., 2019). A given
method might also favor one model over another,
e.g., RoBERT... | 3 | 3 | arxiv2_taclccby4_license.pdf |
avenue for future work.
The above discussion concerns token embed-
dings, but BERT is typically used as a sentence or
text encoder. The standard way to generate sen-
tence or text representations for classification is
to use the [CLS] token, but alternatives are also
being discussed, including concatenation of token
rep... | 4 | 4 | arxiv2_taclccby4_license.pdf |
More recently, Kobayashi et al. (2020) showed
that the norms of attention-weighted input vec-
tors, which yield a more intuitive interpretation
of self-attention, reduce the attention to special to-
kens. However, even when the attention weights
are normed, it is still not the case that most heads
that do the "heavy li... | 5 | 5 | arxiv2_taclccby4_license.pdf |
layers are more transferable (Liu et al., 2019a). In
fine-tuning, it explains why the final layers change
the most (Kovaleva et al., 2019), and why restoring
the weights of lower layers of fine-tuned BERT to
their original values does not dramatically hurt the
model performance (Hao et al., 2019).
Tenney et al. (2019a) su... | 6 | 6 | arxiv2_taclccby4_license.pdf |
5.3 Pre-training BERT
The original BERT is a bidirectional Transformer
pre-trained on two tasks: next sentence prediction
(NSP) and masked language model (MLM) (sec-
tion 2). Multiple studies have come up with alter-
native training objectives to improve on BERT,
which could be categorized as follows:
• How to mask. Ra... | 7 | 7 | arxiv2_taclccby4_license.pdf |
Figure 5: Pre-trained weights help BERT find wider
optima in fine-tuning on MRPC (right) than training
from scratch (left) (Hao et al., 2019)
beddings as input for training BERT, while Po-
erner et al. (2019) adapt entity vectors to BERT
representations. As mentioned above, Wang et al.
(2020c) integrate knowledge not thr... | 8 | 8 | arxiv2_taclccby4_license.pdf |
be successfully approximated with adapter mod-
ules. They achieve competitive performance on
26 classification tasks at a fraction of the computa-
tional cost. Adapters in BERT were also used for
multi-task learning (Stickland and Murray, 2019)
and cross-lingual transfer (Artetxe et al., 2019). An
alternative to fine-tun... | 9 | 9 | arxiv2_taclccby4_license.pdf |
Compression Performance Speedup Model Evaluation
BERT-base (Devlin et al., 2019) ×1 100% ×1 BERT 12 All GLUE tasks, SQuAD
BERT-small ×3.8 91% - BERT 4† All GLUE tasks
Distillation
DistilBERT (Sanh et al., 2019a) ×1.5 90% § ×1.6 BERT 6 All GLUE tasks, SQuAD
BERT6-PKD (Sun et al., 2019a) ×1.6 98% ×1.9 BERT 6 No WNLI, CoL... | 10 | 10 | arxiv2_taclccby4_license.pdf |
then check which of them survive the pruning, find-
ing that the syntactic and positional heads are the
last ones to go. For BERT, Prasanna et al. (2020)
go in the opposite direction: pruning on the basis of
importance scores, and interpreting the remaining
"good" subnetwork. With respect to self-attention
heads specific... | 11 | 11 | arxiv2_taclccby4_license.pdf |
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Revisiting Feature Prediction for Learning Visual
Representations from Video
Adrien Bardes1,2,3, Quentin Garrido1,4, Jean Ponce3,5,6, Xinlei Chen1, Michael Rabbat1, Yann LeCun1,5,6,
Mahmoud Assran1,†, Nicolas Ballas1,†
1FAIR at Meta,2Inria, 3École normale supérieure, CNRS, PSL Research University,4Univ. Gustave Eiffel,... | 0 | 0 | arxiv3.pdf |
To that end, we pretrain a family ofV-JEPA models
on a dataset of 2 million videos collected from pub-
licly available datasets by combining a masked modeling
prediction task with a joint-embedding predictive ar-
chitecture (see Figure 2). We measure performance on
several downstream image and video tasks, using both
f... | 1 | 1 | arxiv3.pdf |
Feature Prediction versus Pixel Reconstruction.
Approaches that predict in pixel space must dedicate
significant model capacity and compute to capture all
the low-level detail in the visual input. By contrast, ap-
proaches that predict in latent space have the flexibility
to eliminate irrelevant or unpredictable pixel-... | 2 | 2 | arxiv3.pdf |
[L×d]
[N×d]
\
Remove
masked
tokens
Binary Mask
[T×H×W]
Eθ
x-encoder
[N×d]
[L×d]
Concatenate
mask tokens
Pφ
predictor
[M×d]
[M×d]
[L×d]
/
Remove
unmasked
tokens
E ¯θ
y-encoder
[L×d]
L1 / /
stop-grad
Figure 3 V-JEPA. Training operates on a video clip ofT frames with spatial resolutionH × W, flattened into a sequence
... | 3 | 3 | arxiv3.pdf |
Table 1 Pixels vs. Featurized Targets.We ablate the effect of computing the prediction loss in feature space vs pixel space. All
models are trained on VideoMix2M for 90K iterations with a batch size of 3072 using the multi-block prediction task. We
examine downstream performance using a frozen backbone with attentive p... | 4 | 4 | arxiv3.pdf |
Table 3 Average Pooling vs. Adaptive Pooling.We pool the
feature map output by the frozen V-JEPA encoder using
an attentive probe, which is then fed into a linear classifier
for downstream supervised tasks (K400 and SSv2). We
evaluate two pooling strategies: 1) average pooling (Avg.),
and attentive pooling (Att.). Resu... | 5 | 5 | arxiv3.pdf |
Table 5 Comparison with Pixel Prediction Methods.We compare V-JEPA with OmniMAE (Girdhar et al., 2023), Video-
MAE (Tong et al., 2022), and Hiera (Ryali et al., 2023), which leverage a pixel-reconstruction loss. All models are trained using
a ViT-L architecture or a comparable Hiera-L. We evaluate the approaches on dow... | 6 | 6 | arxiv3.pdf |
102.4 102.6 102.8 103 103.2 103.4
74
74.5
75
SOTA fine-tuned task-specific
model on SSv 2 (MVD)
V-JEPA
ViT-L/16
VideoMAE
ViT-L/16
Hiera
Hiera-L
OmniMAE
ViT-L/16
Samples Seen (M)
Something-Something-v2 End-to-End Fine-Tuning
Video Feature Pred.
Video Pixel Pred.
Figure 4 SSv2 fine-tuning performance vs. Samples Seen.We
... | 7 | 7 | arxiv3.pdf |
Table 7 Low-Shot Frozen Evaluation.Comparing V-JEPA to other video models in frozen evaluation on Kinetics-400 and
Something-Something-v2 as we vary the percentage of labeled examples from each dataset available for training the attentive
probe. We train the probes in several low-shot settings: using either 5% of the t... | 8 | 8 | arxiv3.pdf |
Frozen
x-encoder
predictor
decoder
(a) Visualization Methodology.We train a conditional diffusion model to decode the V-JEPA feature-space predictions to
interpretable pixels; the pretrained V-JEPA encoder and predictor networks are kept frozen in this process. The decoder is
only fed the representations predicted fo... | 9 | 9 | arxiv3.pdf |
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Mario Lucic, and Cordelia Schmid. Vivit: A video vision
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Bojanowski, Florian Bordes, Pascal Vincent, Armand
Joulin, Michae... | 10 | 10 | arxiv3.pdf |
Chunhui Gu, Chen Sun, David A Ross, Carl Vondrick, Caro-
line Pantofaru, Yeqing Li, Sudheendra Vijayanarasimhan,
George Toderici, Susanna Ricco, Rahul Sukthankar, et al.
Ava: A video dataset of spatio-temporally localized atomic
visual actions. InProceedings of the IEEE conference on
computer vision and pattern recogni... | 11 | 11 | arxiv3.pdf |
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lenge. International Journal of Computer Vision, 115(3):
211–252, 2015.
Chaitanya Ryali, Yuan-Ting Hu, Daniel Bolya, Chen Wei,
Haoqi Fan, Po-Yao Huang, Vaibhav Aggarwal, Arka-
bandhu Chowdhury, Omid Poursaeed, Judy Hoffman, et al.
Hiera: A hierarchical vision tr... | 12 | 12 | arxiv3.pdf |
Liangzhe Yuan, Nitesh Bharadwaj Gundavarapu, Long Zhao,
Hao Zhou, Yin Cui, Lu Jiang, Xuan Yang, Menglin Jia,
Tobias Weyand, Luke Friedman, et al. Videoglue: Video
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Appendix
A Extended Related Works
We first review approaches for learning visual perception from static images before discussing strategies for learning
from video.
Weakly-Supervised Learning from Static Images
One family of approaches for learning visual perception from static images trains a visual encoder to predict... | 14 | 14 | arxiv3.pdf |
B Extended Description of V-JEPA
In this section, we provide an in-depth description of our approachV-JEPA that is illustrated in Figure 3.
Input. Unless stated otherwise, during during pretraining, we always randomly sample a clip of 16 frames from
each input video with a temporal stride of 4 between sampled frames. A... | 15 | 15 | arxiv3.pdf |
Table 8 pretraining hyper-parameters for V-JEPA.
Hyper-parameter ViT-L/16 224 ViT-H/16224 ViT-H/16384
data
datasets VideoMix2M VideoMix2M VideoMix2M
resolution 224 224 384
num_frames 16 16 16
temporal_stride 4 4 4
horizontal_flip true true true
random_resize_scale (0.3, 1.0) (0.3, 1.0) (0.3, 1.0)
random_resize_aspect_r... | 16 | 16 | arxiv3.pdf |
Table 9 Frozen Evaluation hyper-parameters.
Hyper-parameter K400 SSv2 IN1K Place205 iNat21
data
num_clips 8 1 N.A. N.A. N.A.
num_frames 16 16 N.A. N.A. N.A.
temporal_stride 4 4 N.A. N.A. N.A.
horizontal_flip true true true true true
random_resize_scale (0.08, 1.0) (0.08, 1.0) (0.08, 1.0) (0.08, 1.0) (0.08, 1.0)
random_... | 17 | 17 | arxiv3.pdf |
Table 10 Frozen Detection hyper-parameters.
Hyper-parameter ViT-L/16 ViT-H/16
out_layers [18, 20, 22, 24] [26, 28, 30, 32]
batch_size 64 64
epochs 30 30
opt AdamW AdamW
opt_eps 0.00000001 0.00000001
momentum 0.9 0.9
weight_decay 0.05 0.05
lr 0.0001 0.0001
warmup_lr 0.000001 0.000001
min_lr 0.000001 0.000001
warmup_epoc... | 18 | 18 | arxiv3.pdf |
Table 11 Finetuning Evaluation hyper-parameters.
Hyper-parameter K400 SSv2
data
num_segments 1
num_frames 16
sampling_rate 4
resolution 224
model
model_name ViT-L/16 ViT-H/16 ViT-L/16 ViT-H/16
drop_path 0.1 0.2 0.2 0.2
head_drop_rate 0. 0. 0.5 0.5
optimization
batch_size 256 1024 256 256
epochs 35 25 15 15
opt adamw
op... | 19 | 19 | arxiv3.pdf |
Table 12 Linear vs. Attentive Probe Evaluation for V-JEPA and VideoMAE.We evaluate the effect of linear (Lin.)
and attentive (Att.) probing when adapting V-JEPA to the K400 (16 × 5 × 3) and SSv2(16 × 2 × 2) tasks. V-JEPA and
VideoMAE benefit from using a non-linear attentive probe.
K400 SSv2
Method Arch. Lin. Att. Lin.... | 20 | 20 | arxiv3.pdf |
Table 14Temporal Coverage on Kinetics-400.We evaluate the effect of temporal coverage on K400. We train an attentive
probe on K400 using either 1 clip (≈ 2 seconds of a video) or 8 clips (≈ 16 seconds of a video). To sampleN clips, we first
divide a video inN equal-length temporal segments and sample one clip at random... | 21 | 21 | arxiv3.pdf |
Table 16Sample efficiency.We compare the sample efficiency of pretraining various state-of-the-art image and video models.
The #Samples Seenentry corresponds to the number of samples (image or video clips) processed by the network during
pretraining, which is larger than the size of the pretraining dataset for multi-ep... | 22 | 22 | arxiv3.pdf |
MTEB-French: Resources for French Sentence Embedding Evaluation and
Analysis
Mathieu Ciancone
Wikit, France
mathieu@wikit.ai
Imene Kerboua
Esker, France
imene.kerboua@esker.com
Marion Schaeffer
Wikit, France
marion@wikit.ai
Wissam Siblini
wissam.siblini92@gmail.com
Abstract
Recently, numerous embedding models have
been... | 0 | 0 | arxiv4.pdf |
2019; Le et al., 2020). Most French models for
sentence embeddings have been developed by the
open-source community2, by fine-tuning models
like CamemBERT(Martin et al., 2019) or Crois-
santLLM(Faysse et al., 2024).
Benchmarks Embedding models are generally
compared on specific tasks, such as information
retrieval, STS... | 1 | 1 | arxiv4.pdf |
Dataset Syntec HAL SummEvalFr
Samples 100 queries
90 documents
26233 samples
10 classes
100 texts
1100 human summaries
1600 machine summaries
Creation process Scraping of Syntec col-
lective bargaining agree-
ment with articles as doc-
uments. Writing queries
corresponding to articles.
Scraping of HAL arti-
cles with i... | 2 | 2 | arxiv4.pdf |
2023) where given the original human summary
in English and its translation in French, the model
rates the quality of the translation from 0 to 10,
with 0 being of very bad quality and 10 being ex-
cellent. The prompt is available in Figure 8. Ad-
ditionally, we manually check random translations
with ratings between 9... | 3 | 3 | arxiv4.pdf |
tation and, in practical applications, the under-
lying storage and compute costs. We selected
models with embedding dimensions ranging
from 384 to 4096.
• Sequence length: Being the number of to-
kens that a model can consider as input, the
sequence length is important as it impacts the
unit that can be encoded (sente... | 4 | 4 | arxiv4.pdf |
with respect to model ranking?
To go further than the correlation analysis among
datasets regarding their topics (see section 3.1.5),
subsequent analysis will be conducted regarding
how they rank models. Additionally, complemen-
tary insights will be derived from examining cor-
relations of models relative to their str... | 5 | 5 | arxiv4.pdf |
0.2 0.4 0.6 0.8
text-embedding-3-large (0.087)
text-embedding-ada-002 (0.15)
text-embedding-3-small (0.17)
mistral-embed (0.19)
bge-m3 (0.22)
voyage-code-2 (0.24)
e5-mistral-7b-instruct (0.24)
Solon-embeddings-large-0.1 (0.25)
sentence_croissant_alpha_v0.3 (0.26)
sentence-t5-xxl (0.27)
embed-multilingual-v3.0 (0.27)
se... | 6 | 6 | arxiv4.pdf |
Q4: Are there any correlations between
datasets with respect to model ranking?
The datasets correlation w.r.t model ranking are
presented in appendix Figure 12. Except for
two datasets (MasakhaNEWSClusteringP2P, Sum-
mEvalFr), the correlations, on average, are high.
There is still enough diversity to make each dataset
... | 7 | 7 | arxiv4.pdf |
correlated (see Figure 12). We preferred to propose
datasets even if they could introduce biases rather
than not address the task in the benchmark. Note
that each task type can be considered individually.
We hope additional resources will be developed
in the French-speaking community to enrich our
comparison.
Benchmark... | 8 | 8 | arxiv4.pdf |
Semantic Evaluation (SemEval-2022), pages 1094–
1106, Seattle, United States. Association for Compu-
tational Linguistics.
Alexis Conneau and Douwe Kiela. 2018. Senteval: An
evaluation toolkit for universal sentence representa-
tions. ArXiv, abs/1803.05449.
Mathias Creutz. 2018. Open subtitles paraphrase corpus
for six... | 9 | 9 | arxiv4.pdf |
Tomas Mikolov, Kai Chen, Gregory S. Corrado, and
Jeffrey Dean. 2013. Efficient estimation of word
representations in vector space. In International Con-
ference on Learning Representations.
Niklas Muennighoff. 2022. Sgpt: Gpt sentence
embeddings for semantic search. arXiv preprint
arXiv:2202.08904.
Niklas Muennighoff, ... | 10 | 10 | arxiv4.pdf |
A Supplementary materials for datasets
A.1 All datasets
Table 3 displays the size of each dataset along with
the average number of tokens per sample and their
references. The dataset’s content was tokenized
using cl100k_base encoding. For Retrieval, the
two numbers refer to the queries and the docu-
ments. For Rerankin... | 11 | 11 | arxiv4.pdf |
Dataset x Task Average # tokens# samples Reference LicenseAmazonReviewsClassification49.6 5000 McAuley and Leskovec (2013) N/AMasakhaNEWSClassification1398.2 422 Adelani et al. (2023) AFL-3.0MassiveIntentClassification11.4 2974 FitzGerald et al. (2023) N/AMassiveScenarioClassification11.4 2974 FitzGerald et al. (2023) ... | 12 | 12 | arxiv4.pdf |
Figure 4: 2D projection of tasks’ data. 90 random samples per task’s data are embedded using multlingual-e5-small
model (Wang et al., 2022). The embeddings are reduced to 2 dimensions using PCA. The centroid of each task’s
data is represented, along with the ellipse showing the standard deviation along each axis.
Label... | 13 | 13 | arxiv4.pdf |
Document
id article-14
url https://www.syntec.fr/convention-
collective/resiliation-du-contrat-
de-travail/#article-14
title Article 14 : Préavis pendant la péri-
ode d’essai
section Résiliation du contrat de travail
content Modification Avenant n ° 7 du
5/07/1991 Au cours de cette péri-
ode, les deux parties peuvent s... | 14 | 14 | arxiv4.pdf |
Summary
type
Original
(SummEval)
Translated
(Sum-
mEvalFr)
Human
summary
The whale,
Varvara, swam
a round trip
from Russia to
Mexico, nearly
14,000 miles.
The previous
record was set
by a humpback
whale that
migrated more
than 10,000
miles.
La baleine,
Varvara, a
parcouru à la
nage un trajet
aller-retour
entre la Russi... | 15 | 15 | arxiv4.pdf |
Model ranking
Finetuned vs pretrained
Model number of parameters
Max sequence length
Embedding dimension
T uned for sentence similarity
Bilingual
English
English +
tuning on
other languages
French
Multilingual
Closed source
Model ranking
Finetuned vs pretrained
Model number of parameters
Max sequence length
Embedding d... | 16 | 16 | arxiv4.pdf |
bge-m3
distilbert-base-25lang-cased
distilbert-base-en-fr-cased
distilbert-base-fr-cased
sentence-camembert-large
sentence-flaubert-base
Solon-embeddings-base-0.1
Solon-embeddings-large-0.1
sentence-croissant-llm-base
bert-base-multilingual-cased
bert-base-multilingual-uncased
camembert-base
camembert-large
sentence-ca... | 17 | 17 | arxiv4.pdf |
MassiveScenarioClassification
MassiveIntentClassification
MasakhaNEWSClassification
MTOPIntentClassification
MTOPDomainClassification
AmazonReviewsClassification
MasakhaNEWSClusteringS2S
MasakhaNEWSClusteringP2P
MLSUMClusteringS2S
MLSUMClusteringP2P
HALClusteringS2S
AlloProfClusteringS2S
AlloProfClusteringP2P
PawsX
Opu... | 18 | 18 | arxiv4.pdf |
Model Finetuned Language # params Size (Gb) Seq. Len. Emb. dim. License Sentence simbert-base-multilingual-cased No multilingual 1,78e+08 0.71 512 768 Apache-2.0 Nobert-base-multilingual-uncased No multilingual 1,67e+08 0.67 512 768 Apache-2.0 Nocamembert-base No french 1,11e+08 0.44 514 768 MIT Nocamembert-large No fr... | 19 | 19 | arxiv4.pdf |
Average
BitextMining
Classification
Clustering
PairClassification
Reranking
Retrieval
STS
Summarization
bge-m3 0.68 0.95 0.69 0.43 0.77 0.81 0.65 0.81 0.31
distilbert-base-25lang-cased 0.43 0.65 0.46 0.37 0.69 0.34 0.10 0.53 0.31
distilbert-base-en-fr-cased 0.43 0.65 0.46 0.38 0.69 0.34 0.10 0.54 0.31
distilbert-base-f... | 20 | 20 | arxiv4.pdf |
MassiveScenario
MassiveIntent
MasakhaNEWS
MTOPIntent
MTOPDomain
AmazonReviews
PawsX
OpusparcusPC
Classification PairClassification
bge-m3 0.73 0.67 0.77 0.62 0.89 0.45 0.60 0.93
distilbert-base-25lang-cased 0.44 0.35 0.68 0.35 0.62 0.29 0.51 0.86
distilbert-base-en-fr-cased 0.44 0.35 0.68 0.35 0.62 0.29 0.51 0.86
disti... | 21 | 21 | arxiv4.pdf |
SyntecReranking
AlloprofReranking
SyntecRetrieval
BSARDRetrieval
AlloprofRetrieval
Reranking Retrieval
bge-m3 0.88 0.74 0.85 0.60 0.49
distilbert-base-25lang-cased 0.39 0.29 0.18 0.11 0.01
distilbert-base-en-fr-cased 0.39 0.29 0.18 0.11 0.01
distilbert-base-fr-cased 0.39 0.29 0.18 0.11 0.01
sentence-camembert-large 0.8... | 22 | 22 | arxiv4.pdf |
Flores_fr-en
Flores_en-fr
DiaBla_fr-en
STSBenchmarkMultilingual
STS22
SICKFr
SummEvalFr
BitextMining STS Summarization
bge-m3 1.00 1.00 0.85 0.82 0.82 0.78 0.31
distilbert-base-25lang-cased 0.92 0.91 0.11 0.57 0.41 0.62 0.31
distilbert-base-en-fr-cased 0.92 0.91 0.11 0.57 0.42 0.62 0.31
distilbert-base-fr-cased 0.63 0.... | 23 | 23 | arxiv4.pdf |
MasakhaNEWSS2S
MasakhaNEWSP2P
MLSUMS2S
MLSUMP2P
HALS2S
AlloProfS2S
AlloProfP2P
Clustering
bge-m3 0.42 0.45 0.44 0.43 0.31 0.37 0.59
distilbert-base-25lang-cased 0.33 0.32 0.31 0.41 0.24 0.43 0.57
distilbert-base-en-fr-cased 0.34 0.34 0.31 0.41 0.25 0.42 0.57
distilbert-base-fr-cased 0.35 0.34 0.31 0.41 0.24 0.43 0.57
s... | 24 | 24 | arxiv4.pdf |
NA VAIR 00·801·80
AERODYNAMICS FOR NAVAL
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BY
H. H. HURT, JR.
UNIVERSITY OF SOUTHERN CALIFORNIA
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DESTRUCTION NOTICE - For unclassified, limited documents, destroy by any method that will
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NAVAIR 00-80T -80
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NAVAIR 00-80T -80 DATED 01 JAUARY 1965 CHANGED THE DISTRIBUTION STATEMENT
AND DESTRUCTION NOTICE ON THE TITLE PAGE. PLEASE REMOVE AND DISCARD
TITLE AND A PAGE AND REPLACE WITH ATTACHED CORRECTED COPY .
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