Assessing the Robustness of LLM-based NLP Software via Automated Testing | Paper Citation Record

Observation 33674812-ffab-4a95-976f-72c92510a7b6 · outbound

This paper cites Llm-based multi-agent systems for software engineering: Literature review, vision and the road ahead,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Llm-based multi-agent systems for software engineering: Literature review, vision and the road ahead,

Reference 1

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This paper cites Fuzz4all: Universal fuzzing with large language models,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Fuzz4all: Universal fuzzing with large language models,

Reference 2

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This paper cites Promptrobust: Towards evaluating the robustness of large language models on adversarial prompts,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Promptrobust: Towards evaluating the robustness of large language models on adversarial prompts,

Reference 3

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This paper cites Sentiment analysis for software engineering: How far can we go?.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Sentiment analysis for software engineering: How far can we go?

Reference 4

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This paper cites Mttm: Metamorphic testing for textual content modera- tion software,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Mttm: Metamorphic testing for textual content modera- tion software,

Reference 5

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This paper cites Unilog: Automatic logging via llm and in- context learning,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Unilog: Automatic logging via llm and in- context learning,

Reference 6

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This paper cites Managing extreme ai risks amid rapid progress,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Managing extreme ai risks amid rapid progress,

Reference 7

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This paper cites Chatgpt incorrectness detection in software reviews,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Chatgpt incorrectness detection in software reviews,

Reference 8

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This paper cites Development in times of hype: How freelancers explore generative ai?.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Development in times of hype: How freelancers explore generative ai?

Reference 9

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Observation 837bd9ab-c39e-446e-981c-43f9c694c2be · outbound

This paper cites How far are we? the triumphs and trials of generative ai in learning soft- ware engineering,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing How far are we? the triumphs and trials of generative ai in learning soft- ware engineering,

Reference 10

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This paper cites Text classification via large language models,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Text classification via large language models,

Reference 11

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Observation 64384c7c-b40e-418f-b1d4-c50c6e9032ae · outbound

This paper cites Chatgpt outperforms crowd workers for text-annotation tasks,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Chatgpt outperforms crowd workers for text-annotation tasks,

Reference 12

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Observation c055efff-a4c3-4010-baa4-bae9e7b02262 · outbound

This paper cites Can llms replace manual annotation of software engineering artifacts?.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Can llms replace manual annotation of software engineering artifacts?

Reference 13

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Observation a84229fe-463e-4dbd-8c3b-ac72a8d2a595 · outbound

This paper cites Assessing the robustness of llm-based nlp software via automated testing,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Assessing the robustness of llm-based nlp software via automated testing,

Reference 14

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This paper cites Nanofuzz: A usable tool for automatic test generation,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Nanofuzz: A usable tool for automatic test generation,

Reference 15

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This paper cites Toward stealthy backdoor attacks against speech recognition via elements of sound,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Toward stealthy backdoor attacks against speech recognition via elements of sound,

Reference 16

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Observation f0cc6a6b-bf70-4b63-abc5-598523c33f69 · outbound

This paper cites Multitest: Physical-aware object insertion for testing multi-sensor fusion perception systems,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Multitest: Physical-aware object insertion for testing multi-sensor fusion perception systems,

Reference 17

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This paper cites Glue-x: Evaluating natural language understanding models from an out-of-distribution generalization perspective,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Glue-x: Evaluating natural language understanding models from an out-of-distribution generalization perspective,

Reference 18

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Observation 7ffae674-a0e3-4f5f-9391-f2cc5313b7ee · outbound

This paper cites Black box adversarial prompting for foundation models,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Black box adversarial prompting for foundation models,

Reference 19

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This paper cites On the robustness of chatgpt: An adversarial and out-of-distribution perspective,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing On the robustness of chatgpt: An adversarial and out-of-distribution perspective,

Reference 20

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This paper cites Software testing with large language models: Survey, landscape, and vision,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Software testing with large language models: Survey, landscape, and vision,

Reference 21

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This paper cites Llmeffichecker:understanding and testing efficiency degradation of large language models,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Llmeffichecker:understanding and testing efficiency degradation of large language models,

Reference 22

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This paper cites Deepatash: Focused test generation for deep learning systems,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Deepatash: Focused test generation for deep learning systems,

Reference 23

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This paper cites Atom: Automated black-box testing of multi-label image classification systems,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Atom: Automated black-box testing of multi-label image classification systems,

Reference 24

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This paper cites Repairing failure-inducing inputs with input reflection,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Repairing failure-inducing inputs with input reflection,

Reference 25

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This paper cites Generating natural language adversarial examples through probability weighted word saliency,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Generating natural language adversarial examples through probability weighted word saliency,

Reference 26

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This paper cites Good debt or bad debt: Detecting semantic orientations in economic texts,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Good debt or bad debt: Detecting semantic orientations in economic texts,

Reference 27

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This paper cites Character-level convolutional networks for text classification,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Character-level convolutional networks for text classification,

Reference 28

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This paper cites Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales,

Reference 29

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This paper cites Beam search: faster and monotonic,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Beam search: faster and monotonic,

Reference 30

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Assessing the Robustness of LLM-based NLP Software via Automated Testing Chatgpt- resistant screening instrument for identifying non-programmers,

Reference 31

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This paper cites Uncovering the causes of emotions in software developer communication using zero-shot llms,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Uncovering the causes of emotions in software developer communication using zero-shot llms,

Reference 32

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Observation fdffdce0-926e-474c-a83d-34b258b5b19b · outbound

This paper cites Can automated text classification improve content analysis of software project data?.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Can automated text classification improve content analysis of software project data?

Reference 33

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This paper cites Hqa-attack: toward high quality black-box hard-label adversarial attack on text,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Hqa-attack: toward high quality black-box hard-label adversarial attack on text,

Reference 34

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This paper cites Limeattack: Local explainable method for textual hard-label adversarial attack,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Limeattack: Local explainable method for textual hard-label adversarial attack,

Reference 35

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This paper cites Texthacker: Learning based hybrid local search algorithm for text hard-label adversarial attack,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Texthacker: Learning based hybrid local search algorithm for text hard-label adversarial attack,

Reference 36

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This paper cites Natural language adversarial defense through synonym encoding,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Natural language adversarial defense through synonym encoding,

Reference 37

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This paper cites Leap: Efficient and automated test method for nlp software,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Leap: Efficient and automated test method for nlp software,

Reference 38

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This paper cites Beyond accuracy: Behavioral testing of nlp models with checklist (extended abstract),.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Beyond accuracy: Behavioral testing of nlp models with checklist (extended abstract),

Reference 39

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This paper cites Understanding the value of software engineering technologies,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Understanding the value of software engineering technologies,

Reference 40

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Observation db34cc07-6585-4a53-bc40-589bf21f99ba · outbound

This paper cites Enhancing speaker diarization with large language models: A contextual beam search approach,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Enhancing speaker diarization with large language models: A contextual beam search approach,

Reference 41

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Observation d54dc19c-591e-428f-9eb5-5f4ed2d8e374 · outbound

This paper cites Conformal au- toregressive generation: Beam search with coverage guarantees,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Conformal au- toregressive generation: Beam search with coverage guarantees,

Reference 42

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Observation aef45b66-761c-4b47-b4a6-59e01dafb0df · outbound

This paper cites Hybrid filtered beam search algorithm for the optimization of monitoring patrols,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Hybrid filtered beam search algorithm for the optimization of monitoring patrols,

Reference 43

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Observation 94c062c3-9f67-4575-86b2-44ed789ac295 · outbound

This paper cites Large- scale language model rescoring on long-form data,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Large- scale language model rescoring on long-form data,

Reference 44

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Observation 4b0f0987-71b3-4720-8c34-8720c6a882d5 · outbound

This paper cites Beamqa: Multi-hop knowledge graph question answering with sequence-to-sequence prediction and beam search,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Beamqa: Multi-hop knowledge graph question answering with sequence-to-sequence prediction and beam search,

Reference 45

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This paper cites Iso/iec,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Iso/iec,

Reference 46

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Observation 8e89aa7e-b2b6-4294-97dc-df6a2c1acaf5 · outbound

This paper cites Intriguing properties of neural networks,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Intriguing properties of neural networks,

Reference 47

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Observation 9a19dcab-594a-416e-ae87-c45b2e6a2933 · outbound

This paper cites Textattack: A framework for adversarial attacks, data augmentation, and adversarial training in nlp,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Textattack: A framework for adversarial attacks, data augmentation, and adversarial training in nlp,

Reference 48

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Observation b548f926-232c-4463-8ccf-99137dfced67 · outbound

This paper cites Fast adversarial attacks on language models in one GPU minute,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Fast adversarial attacks on language models in one GPU minute,

Reference 49

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Observation 3456b88d-b2b2-461e-ac4e-bc8292936efa · outbound

This paper cites Hotflip: White-box adversarial examples for text classification,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Hotflip: White-box adversarial examples for text classification,

Reference 50

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Observation 1cb91ef3-1283-438c-b482-d6067edec186 · outbound

This paper cites Towards improving adversarial training of nlp models,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Towards improving adversarial training of nlp models,

Reference 51

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Observation a4fc34d3-11d1-403c-a56b-dc14ae58942a · outbound

This paper cites A recovering beam search algorithm for the one-machine dynamic total completion time scheduling problem,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing A recovering beam search algorithm for the one-machine dynamic total completion time scheduling problem,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:09:22.303326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3d2f3aa9-135a-4f03-91ea-e5a519594840 · outbound

This paper cites Backtrack beam search for multiobjective scheduling problem,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Backtrack beam search for multiobjective scheduling problem,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:09:22.289417Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c4591ce7-dbcf-48ba-b0b5-40512ade96cd · outbound

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

Assessing the Robustness of LLM-based NLP Software via Automated Testing Is bert really robust? a strong baseline for natural language attack on text classification and entailment,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:09:22.274372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9f2216c0-d92e-4a72-b0db-469daffdd1b9 · outbound

This paper cites Understanding Neural Networks through Representation Erasure.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Understanding Neural Networks through Representation Erasure

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-10T23:09:21.391726Z

Source-reported events for the cited work

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Observation c37a065d-974a-4ff8-a19e-5fab79d23ab7 · outbound

This paper cites Wordnet: A lexical database for english,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Wordnet: A lexical database for english,

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-10T23:09:21.397121Z

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Observation 56b72cdf-06b7-4f4b-ae7a-3322a7ef246c · outbound

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

Assessing the Robustness of LLM-based NLP Software via Automated Testing Word-level textual adversarial attacking as combinatorial optimization,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:09:22.261596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 16f620f4-327d-4154-b507-affe5b7aed6e · outbound

This paper cites Mistral 7B.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Mistral 7B

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-10T23:09:21.405749Z

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Observation 64be3a36-ae2d-4a4c-b8a5-3dddb085b5d8 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-10T23:09:21.411075Z

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Unavailable: canonical work link unavailable.

Observation 9d9c93eb-3fec-47a4-ba23-e26e00ff5fc9 · outbound

This paper cites Internlm2 technical report,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Internlm2 technical report,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:09:22.247527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ba88e5d4-4337-467b-9f14-0d807046f222 · outbound

This paper cites Yi: Open Foundation Models by 01.AI.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Yi: Open Foundation Models by 01.AI

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-10T23:09:21.419919Z

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Observation 6f53a992-0b25-4b12-a40b-0fee021bb096 · outbound

This paper cites Stress test evaluation for natural language inference,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Stress test evaluation for natural language inference,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:09:22.233591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b502ec63-34fc-469d-838b-49a584c0565c · outbound

This paper cites Testing the limits: Unusual text inputs generation for mobile app crash detection with large language model,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Testing the limits: Unusual text inputs generation for mobile app crash detection with large language model,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:09:22.218959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 70d22f26-bd95-44d1-be22-723f9ab587f2 · outbound

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

Assessing the Robustness of LLM-based NLP Software via Automated Testing Textbugger: Generating adversarial text against real-world applications,

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-10T23:09:21.433205Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation abe60e63-e933-42f6-a14e-1903770c4391 · outbound

This paper cites Hallucination is Inevitable: An Innate Limitation of Large Language Models.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Hallucination is Inevitable: An Innate Limitation of Large Language Models

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-10T23:09:21.436910Z

Source-reported events for the cited work

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Observation e9bf3aa8-a061-45a8-bc64-1c5f44069dbd · outbound

This paper cites Augmenting LLMs with Knowledge: A survey on hallucination prevention.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Augmenting LLMs with Knowledge: A survey on hallucination prevention

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-10T23:09:21.441235Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2eb9e727-e280-4086-93dd-3f36968b2241 · outbound

This paper cites Coophance: Cooperative enhancement for robustness of deep learning systems,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Coophance: Cooperative enhancement for robustness of deep learning systems,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:09:22.196783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 326b48ef-266d-4ae1-b114-cff7df47625a · outbound

This paper cites Black-box testing of deep neural networks through test case diversity,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Black-box testing of deep neural networks through test case diversity,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:09:22.184012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a852b0f6-d2d1-4547-9ea1-828af8349649 · outbound

This paper cites Dialtest: automated testing for recurrent- neural-network-driven dialogue systems,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Dialtest: automated testing for recurrent- neural-network-driven dialogue systems,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:09:22.169558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3b9024e0-ef21-40ed-baff-0411a4256ad7 · outbound

This paper cites Keeper: Automated testing and fixing of machine learning software,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Keeper: Automated testing and fixing of machine learning software,

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-10T23:09:21.460248Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be0407e1-9bf2-4f16-88f5-666ea11fda5f · outbound

This paper cites Automated testing and improvement of named entity recognition systems,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Automated testing and improvement of named entity recognition systems,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:09:22.156062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7721979d-88a7-452c-b106-0bc9ea327962 · outbound

This paper cites Keeping llms aligned after fine-tuning: The crucial role of prompt templates,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Keeping llms aligned after fine-tuning: The crucial role of prompt templates,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:09:22.142585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a004e29f-f548-4591-95f4-3e135459b092 · outbound

This paper cites Look before you leap: An exploratory study of uncertainty analysis for large language models,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Look before you leap: An exploratory study of uncertainty analysis for large language models,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:09:22.129508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 753f0aea-8d64-4766-82db-6ce0c996a62e · outbound

This paper cites Imperceptible content poisoning in llm-powered applications,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Imperceptible content poisoning in llm-powered applications,

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-10T23:09:21.482667Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9456c30-064f-4ae7-971b-6105118bb1b8 · outbound

This paper cites Revisiting out-of-distribution robustness in nlp: Bench- marks, analysis, and llms evaluations,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Revisiting out-of-distribution robustness in nlp: Bench- marks, analysis, and llms evaluations,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:09:22.116338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation cf59aa33-b82f-4c3d-9b43-7b11c05dd111 · outbound

This paper cites Revisit input perturbation problems for llms: A unified robustness evaluation framework for noisy slot filling task,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Revisit input perturbation problems for llms: A unified robustness evaluation framework for noisy slot filling task,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:09:22.102241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0ab261ff-e313-4c36-a2d3-e989f5cca9cd · outbound

This paper cites Ro- bustness over time: Understanding adversarial examples’ effectiveness 16 on longitudinal versions of large language models,.

Assessing the Robustness of LLM-based NLP Software via Automated Testing Ro- bustness over time: Understanding adversarial examples’ effectiveness 16 on longitudinal versions of large language models,

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-10T23:09:21.497519Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f7e4454-ddea-4549-97f0-3c53ed3ba49e · outbound

This paper cites degree in computer science and technology with the College of Computer Science and Software Engineering, Hohai University.

Assessing the Robustness of LLM-based NLP Software via Automated Testing degree in computer science and technology with the College of Computer Science and Software Engineering, Hohai University

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:09:22.088502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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