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Paper Citation Record · LEDGER

Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models

As of 18 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 1 inbound Pith citation observation for arXiv:2506.07645.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.07645 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:31:53.253028Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:49:52.821536Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-06T23:49:56.116681Z

Reference resolution

40 of 40 outbound references displayed

  • verified exact1
  • verified fuzzy22
  • unresolved17
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4d008695-f593-4208-ba51-31e4e4e421da · outbound

This paper cites BERT: Pre-training of deep bidirectional transformers for language understanding.

Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models BERT: Pre-training of deep bidirectional transformers for language understanding

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:31:57.455663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T05:31:50.313324Z digest=sha256:538a51bc9d2544063039786db68144385d536d3fa024950b63e01546b4ad9693

Observation d9f71cab-131f-46ad-a00d-14d9a0fa2149 · outbound

This paper cites Abhishek Kadian.

Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models Abhishek Kadian

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:31:57.279635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T05:31:50.403795Z digest=sha256:bc368188e65ace8a5e7a998a297e9b63c35ffffd7dbf210a1ba5e5a03d9d785d

Observation 0d0873e1-42d8-4ccc-bf06-f60b293f89ab · outbound

This paper cites Is bert really robust? a strong baseline for natural language attack on text classification and entailment.

Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models Is bert really robust? a strong baseline for natural language attack on text classification and entailment

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:31:57.065191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T05:31:50.451885Z digest=sha256:b45815e1d3db3cf78a7981576474be3c19288cf2d35c91c80adae4a66addcf3b

Observation 494ce934-11f8-465d-98af-2063a34bfb3f · outbound

This paper cites BERT-ATTACK: Adversarial Attack Against BERT Using BERT.

Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models BERT-ATTACK: Adversarial Attack Against BERT Using BERT

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-07T05:31:50.552193Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:31:50.552193Z digest=sha256:b3fefeae605d85de575d37764b6d24cbd711fb88a48c19b5939717cb418402c8

Observation 7a4fd339-e467-415a-aeaa-a5c304c995cf · outbound

This paper cites T3: Tree-autoencoder constrained adversarial text generation for targeted attack.

Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models T3: Tree-autoencoder constrained adversarial text generation for targeted attack

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:31:56.934032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T05:31:50.580504Z digest=sha256:7d061bee4909ec8df214c8c340c7b6df1adf4f1f499aecbb9d3ad880f211fbc7

Observation 0e6dce39-9b11-4ff2-bc6b-b8e9cee93ee7 · outbound

This paper cites Word-level textual adversarial attacking as combinatorial optimization.

Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models Word-level textual adversarial attacking as combinatorial optimization

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:31:56.769717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T05:31:50.620493Z digest=sha256:227446628299cfce4f3c14cdf260bbc62caef28d52a2806b48c978481a769420

Observation 708ca4f9-ed20-4287-b385-20c6778e8517 · outbound

This paper cites Jailbroken: How does llm safety training fail? In A.

Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models Jailbroken: How does llm safety training fail? In A

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:31:56.608085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T05:31:50.709481Z digest=sha256:4453e0284cbc18b6ca998bd8ed8f2b3941cd9373ed420f290022355c4e8e13f4

Observation 2c7cd213-aa61-43a0-83fc-aa177eb2dcf9 · outbound

This paper cites Gemma 2: Improving Open Language Models at a Practical Size.

Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models Gemma 2: Improving Open Language Models at a Practical Size

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-07T05:31:50.762334Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:31:50.762334Z digest=sha256:01c8777a8d6bea1a890ea0708b64b89632e005c103a6fe904c7b289b32efa8d7

Observation 520d65f0-4b99-4d75-a8cb-f9ded3c3d50d · outbound

This paper cites Unsupervised cross-lingual representation learning at scale.

Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models Unsupervised cross-lingual representation learning at scale

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:31:56.440564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T05:31:50.811244Z digest=sha256:129f3c2a43b1008371d62c5010e8e9e91b5b04db0422844934f6be2e5e6f219d

Observation 9efb2c49-8718-4600-89ac-d171f7d68c43 · outbound

This paper cites Command r +, 2024.

Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models Command r +, 2024

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:31:56.270851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T05:31:50.913750Z digest=sha256:774e85845aecc03a5ad0b24182f8cb16eea90598f77b046000b04cabcd1204ba

Observation ab00409d-a765-44c0-ab85-7562cdddffc7 · outbound

This paper cites Qwen2 technical report, 2024.

Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models Qwen2 technical report, 2024

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-07T05:31:50.961177Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:31:50.961177Z digest=sha256:e34afbda6808f2ef3e46e77d01b08b664ad9161b5e403deca1c3abe2ccc0b983

Observation d911da25-be68-4681-ac4f-cec3cee70907 · outbound

This paper cites Textbugger: Generating adversarial text against real-world applications.

Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models Textbugger: Generating adversarial text against real-world applications

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:31:56.115353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T05:31:51.039595Z digest=sha256:47caa684baf8290d933df0e8aec4c72d9c2f1fd1d07ccb8d9e260feb6b8cea10

Observation 061bbeb1-a010-4f80-9e34-d7af884bcb74 · outbound

This paper cites Zico Kolter, and Matt Fredrikson.

Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models Zico Kolter, and Matt Fredrikson

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-07T05:31:51.109468Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:31:51.109468Z digest=sha256:a64b17e800f14613450ce7708d8031b6fdd6e8b8668d4c92193ace653e1571ec

Observation 8790b90f-0382-4bdd-8466-9fe2a9fb89bf · outbound

This paper cites Adversarial glue: A multi-task benchmark for robustness evaluation of language models.

Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models Adversarial glue: A multi-task benchmark for robustness evaluation of language models

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:31:55.960751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T05:31:51.210837Z digest=sha256:b4522dd68798dcb3918be1b25f1c6715446135ff7481c1feb6c893a4efe1bd90

Observation d782fc8d-f70e-4391-867e-14dff1aa4d3a · outbound

This paper cites Decodingtrust: A comprehensive assessment of trustworthiness in gpt models.

Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models Decodingtrust: A comprehensive assessment of trustworthiness in gpt models

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:31:55.836210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T05:31:51.274895Z digest=sha256:6eb94211dc222f69a1f64b85948e69e7c17259e06da2c0cee23985fa97ae2be0

Observation 4d710b39-8dc0-4301-92fc-11298720ee4c · outbound

This paper cites Deep inside convolutional networks: visualising image classification models and saliency maps.

Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models Deep inside convolutional networks: visualising image classification models and saliency maps

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:31:55.707012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T05:31:51.353581Z digest=sha256:52c3ba97d44d1f69789fca7bf1f68d78b17d8c3f9964c4b7e0a03ebd2f5e460d

Observation 82d185ad-bddb-4010-9096-5e5b31a96d63 · outbound

This paper cites A unified approach to interpreting model predictions.Advances in neural information processing systems, 30:4765–4774, 2017.

Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models A unified approach to interpreting model predictions.Advances in neural information processing systems, 30:4765–4774, 2017

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:31:55.497368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T05:31:51.419557Z digest=sha256:2e37a8fde1f4a377deba7cbb58945dcefdbe3dee74c38f4031f0e4d4ac409f7d

Observation 5bd1de35-5a22-4fd3-bef5-7754d2d6dc14 · outbound

This paper cites why should i trust you?.

Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models why should i trust you?

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:31:55.348533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T05:31:51.511760Z digest=sha256:c5a2b6ab2ebe4375d7bbdc4c190cde7804b667110e1ee3471068e8b9e0b73b50

Observation 64325dac-4991-4c87-9b3f-ed2d3dea7f14 · outbound

This paper cites Not Just a Black Box: Learning Important Features Through Propagating Activation Differences.

Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models Not Just a Black Box: Learning Important Features Through Propagating Activation Differences

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-07T05:31:51.614615Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:31:51.614615Z digest=sha256:77291c7bfdcb46262f007e7ad821297f2d9f131e143aa716db508349ae5bae90

Observation d71100d1-4b94-408f-b8f1-e95597503279 · outbound

This paper cites Axiomatic attribution for deep networks.

Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models Axiomatic attribution for deep networks

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:31:55.187002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T05:31:51.686664Z digest=sha256:2454675fd88083d075ede67893ee100234efb356b2b2eb08cec87d816eb35ad0

Observation 0a8f3b63-2968-47ff-98b6-a2dec677041b · outbound

This paper cites SmoothGrad: removing noise by adding noise.

Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models SmoothGrad: removing noise by adding noise

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-07T05:31:51.789088Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:31:51.789088Z digest=sha256:7be3f883f70e76fcc7f226e795236510b1267a607bf04644552afc1ce8e99dd0

Observation 03d2b5f1-1aaa-4c82-8cb4-5f497f00d6f7 · outbound

This paper cites Quantifying attention flow in transformers.

Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models Quantifying attention flow in transformers

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:31:55.043952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T05:31:51.887643Z digest=sha256:1967448c0d482ac9f8d12679dedb148ee715dc0f3ed9edef7ece635650e22666

Observation 6ac6f594-cb48-4528-8d52-7c735742f2fe · outbound

This paper cites an unresolved cited work.

Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models Unresolved cited work

Reference 23

Resolution
unresolved
raw_fallback, observed 2026-08-07T05:31:54.916931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T05:31:52.014755Z digest=sha256:c3216db6016867159f3f2554ee164c1e26ca69cd600ae782c9e47670eaea730d

Observation 05941d96-703e-4298-9545-7f134d8218a6 · outbound

This paper cites Transformer interpretability beyond attention visualization.

Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models Transformer interpretability beyond attention visualization

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-07T05:31:52.081039Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:31:52.081039Z digest=sha256:c3066fa1e7d2a3e4d51d0ac1a0b33ffb8578f9bde580f651a951931282d3ef3d

Observation 676f16b6-9d68-4595-82c5-30213ecc561a · outbound

This paper cites an unresolved cited work.

Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models Unresolved cited work

Reference 25

Resolution
unresolved
raw_fallback, observed 2026-08-07T05:31:54.793653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T05:31:52.170314Z digest=sha256:62c7b6f738024390cc9762a115ea07826997743123af31729e7675d2b9ed8fcc

Observation aa0759a2-3815-4780-a733-9d29ec8e95a4 · outbound

This paper cites HerBERT: Efficiently pretrained transformer-based language model for Polish.

Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models HerBERT: Efficiently pretrained transformer-based language model for Polish

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:31:54.577546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T05:31:52.241785Z digest=sha256:6e5cac463beac7e3650df60f9f47389f1d0ecc298eff8d86823fa1957ecb93c8

Observation 134639a1-038d-493a-acf8-5c0540e5a903 · outbound

This paper cites Polbert: Attacking polish nlp tasks with transformers.

Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models Polbert: Attacking polish nlp tasks with transformers

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:31:54.436921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T05:31:52.344804Z digest=sha256:6390f388966bfc6f80368e00cfefcab32dc90e63d645a2335f9bebdb7bc22444

Observation 084fd28a-2ccb-4c01-a893-002d81e697cd · outbound

This paper cites Assessing generalization capability of text ranking models in polish,.

Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models Assessing generalization capability of text ranking models in polish,

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:31:54.295432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T05:31:52.411660Z digest=sha256:80e913dc9e36621326973113e22a33cf7953282e56360d4e8f00618f31278ff0

Observation 0d9afd97-688d-428c-bb26-f0933bb5f6f5 · outbound

This paper cites Bielik 7b v0.

Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models Bielik 7b v0

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-07T05:31:52.563257Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:31:52.563257Z digest=sha256:495aed1c08b9ebc2cbec798854c5b981a6db859834ce4757466d81bd53802675

Observation c949ec37-2fa7-4bd4-84cd-846400d902ad · outbound

This paper cites OpenChat: Advancing Open-source Language Models with Mixed-Quality Data.

Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models OpenChat: Advancing Open-source Language Models with Mixed-Quality Data

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-07T05:31:52.627591Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:31:52.627591Z digest=sha256:c12b7e2718d3b6fa88032a47bd6ba91be435639614fdd4c528984c6b1c97f36b

Observation a07116a8-6252-46a8-9d49-de7c94e931c5 · outbound

This paper cites PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts.

Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-07T05:31:52.719510Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:31:52.719510Z digest=sha256:23f076d91b0e5170647d76d4bc44fa78bf1dde9dbce5f80e3e11272932509743

Observation 031ca908-b744-490f-b050-7b0ccbfccc79 · outbound

This paper cites On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective.

Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-07T05:31:52.805313Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:31:52.805313Z digest=sha256:7370230f28fa8477653bd02a18f57751d1c90caa2972de62cc0866f016c4f47e

Observation 1e2795de-e445-400b-868b-4dcd724c71fa · outbound

This paper cites Maziarz, Maciej Piasecki, and Ewa K.

Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models Maziarz, Maciej Piasecki, and Ewa K

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:31:54.178357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T05:31:52.881842Z digest=sha256:edb188b299cbb82d6f1d1fc39c9cca8dea6b4d99f9a8388404253024e836c4dc

Observation b526d6b0-99d6-4bc3-aaf6-2db5d5d1ecf5 · outbound

This paper cites KLEJ: Comprehensive Benchmark for Polish Language Understanding.

Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models KLEJ: Comprehensive Benchmark for Polish Language Understanding

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-07T05:31:52.962319Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:31:52.962319Z digest=sha256:666e2ea2ce6221eaa5bc4456eec94c533f2dd8f9831f848cc24699bed450a0cb

Observation b77522c8-dffb-4987-8e05-59dedae8ba07 · outbound

This paper cites This is the way: designing and compiling lepiszcze, a comprehensive nlp benchmark for polish.

Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models This is the way: designing and compiling lepiszcze, a comprehensive nlp benchmark for polish

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:31:54.021129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models Unresolved cited work

Reference 36

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Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models Multi-level sentiment analysis of PolEmo 2.0: Extended corpus of multi-domain consumer reviews

Reference 37

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This paper cites Mistral 7B.

Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models Mistral 7B

Reference 38

Resolution
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Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models Unresolved cited work

Reference 2019

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models Assessing generalization capability of text ranking models in Polish

Reference 2024

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Pith citing papers

Observation 7daf2794-dfba-4877-a328-33cb345afc63 · inbound

PL-Guard: Benchmarking Language Model Safety for Polish cites this paper.

PL-Guard: Benchmarking Language Model Safety for Polish Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models

Reference 2025

Resolution
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