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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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+00:00.

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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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T05:31:50.403795Z digest=sha256:07fe1da23c35336dc87fe898d7191fad96534c768b302668e451ac40ab2cc403

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T05:31:50.451885Z digest=sha256:8a7198981576f9f2d6060399c12244ece1bbb1752e6c0c2f080190fa7738070b

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T05:31:50.580504Z digest=sha256:77a950158ce723fbc312cd6091e983ba5e8b5bc5204f8fc1f5f512e19314e563

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T05:31:50.620493Z digest=sha256:23cf1bd984c45d6df6541f92f61d3d256e77af80c3d7c8f32bad0698d3657e3d

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T05:31:50.709481Z digest=sha256:55c05fffc205c706b46b2d2913213dbe6fc47bab65ddd011c46cd3a23845ad3d

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T05:31:50.811244Z digest=sha256:97f71adf16a90cc179971170fe2e43e6eedc055c5e425a7b9ad764ab071a18e4

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T05:31:50.913750Z digest=sha256:0b01890172e641ee9a1ec81fc2642969e381367cd909fa1570cda79b8af59a33

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

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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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T05:31:51.039595Z digest=sha256:bfa574e9858a8289580bdf16fe8c5bac4346320b7dc8d9f478e8c9471b20632b

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T05:31:51.210837Z digest=sha256:bfe8af3b8b56d6972f6eee38d2e7ff64d3fe5fe8963db65ead9f606562ce29a1

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T05:31:51.274895Z digest=sha256:a1091cbc580cc69d409df9f78dbb89d7e79cc64a5f4902a0da66671ed299d7b2

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T05:31:51.353581Z digest=sha256:1f1ec025fbef97053bf9d16732417ad993bc16a4de9bb4a6922156250e9b92b1

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T05:31:51.419557Z digest=sha256:52e34348d47da801e8db4cf29b3152e251e054f0f24c663c3106c0d95ce5a8ec

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T05:31:51.511760Z digest=sha256:27415eb8c52e239e8c8edab6860feee306ed5140bf4a0abae6009d53c8a62bf6

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T05:31:51.686664Z digest=sha256:8a68d781992442f4c9d3b1629dc9173a803769764349fc1af8684b8b60330c7f

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T05:31:51.887643Z digest=sha256:f4a94d4d4ec2d681f15e323109732654e192cb8c4a1e3530814e4506b6edb289

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

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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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T05:31:52.014755Z digest=sha256:429c329845297ccdfd5e1f163b92936f8612cb460da535d5f7c8590e6df50fd2

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

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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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T05:31:52.170314Z digest=sha256:091aee106bd747534ff566b034b513c71dee422573b5a5bb32b2a63a204bcd0e

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T05:31:52.241785Z digest=sha256:c39f99e0a6a0f95c8ad89c8bab17ddee7693d43171cb94fee05c2b5ab1ea04b8

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T05:31:52.344804Z digest=sha256:8359e6ac4ff30bfd93a525bba7c63d47d44c9b6171b07910a82080fb676b3523

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T05:31:52.411660Z digest=sha256:84b9202882d215de0edea5e7ac2690aefe4abb8e19b85aba756e6593bf4e2625

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=sha256:96a62cf31c58bd6c19e3416512ebaa71cf77385d29f9037fde5544f66f4bddca

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:c1ee73309501132d2bcbfa350df0f20427a34be3bf986578c80443965df7180d

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T05:31:52.881842Z digest=sha256:76180051eff5d56774c5897131d318cb71105cfda1cc3f28c4a9aaac06326296

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 2026-08-17T06:30:58.91139+00:00.

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Observation b1548104-7518-4f9b-a2ee-2d03d8fbad11 · outbound

This paper cites an unresolved cited work.

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

Reference 36

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Observation 0dfcd451-078c-4945-8501-ab3a93d71f72 · outbound

This paper cites Multi-level sentiment analysis of PolEmo 2.0: Extended corpus of multi-domain consumer reviews.

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

Resolution
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Source-reported events for the cited work

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Observation 6bbc474a-cffd-4379-9cb7-697953d4241e · outbound

This paper cites Mistral 7B.

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

Reference 38

Resolution
unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 58f40129-988c-4afd-a6af-d427c95ca895 · outbound

This paper cites an unresolved cited work.

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

Reference 2019

Resolution
unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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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 2024

Resolution
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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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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