Data Swarms: Optimizable Generation of Synthetic Evaluation Data | Paper Citation Record

Observation d016e96c-6595-4339-b0cc-cb96a723844b · outbound

This paper cites Kgquiz: Evaluating the generalization of encoded knowledge in large language models.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Kgquiz: Evaluating the generalization of encoded knowledge in large language models

Reference 1

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Observation 3d0e456c-a8c9-4635-8c4b-0b332bff53bd · outbound

This paper cites AutoEval Done Right: Using Synthetic Data for Model Evaluation.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data AutoEval Done Right: Using Synthetic Data for Model Evaluation

Reference 2

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This paper cites Adaptively evaluating models with task elicitation.arXiv preprint arXiv:2503.01986, 2025.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Adaptively evaluating models with task elicitation.arXiv preprint arXiv:2503.01986, 2025

Reference 3

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Observation b921ed93-8e24-4bca-9e25-0bce68d43405 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Training Verifiers to Solve Math Word Problems

Reference 4

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This paper cites Knowledge crosswords: Geometric knowledge reasoning with large language models.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Knowledge crosswords: Geometric knowledge reasoning with large language models

Reference 5

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This paper cites Self-Boosting Large Language Models with Synthetic Preference Data.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Self-Boosting Large Language Models with Synthetic Preference Data

Reference 6

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This paper cites Alpacafarm: A simulation framework for methods that learn from human feedback.Advances in Neural Information Processing Systems, 36, 2024.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Alpacafarm: A simulation framework for methods that learn from human feedback.Advances in Neural Information Processing Systems, 36, 2024

Reference 7

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Observation d41b1c59-fd0e-4f48-afef-853faba12af0 · outbound

This paper cites Clas- sifying the classifier: dissecting the weight space of neural networks.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Clas- sifying the classifier: dissecting the weight space of neural networks

Reference 8

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Observation e987ecb4-b7f4-4c52-b6c9-3e905dfb908e · outbound

This paper cites Heterogeneous swarms: Jointly optimizing model roles and weights for multi-llm systems.arXiv preprint arXiv:2502.04510, 2025.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Heterogeneous swarms: Jointly optimizing model roles and weights for multi-llm systems.arXiv preprint arXiv:2502.04510, 2025

Reference 9

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Observation fd4fb5fa-c7bd-4b36-85ce-d6907ad443ec · outbound

This paper cites Model swarms: Collaborative search to adapt LLM experts via swarm intelligence.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Model swarms: Collaborative search to adapt LLM experts via swarm intelligence

Reference 10

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Observation 6301967c-1eb0-41d1-8804-d4b16fd9704a · outbound

This paper cites Promptbreeder: Self-referential self-improvement via prompt evolution.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Promptbreeder: Self-referential self-improvement via prompt evolution

Reference 11

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Observation 07f41ea0-74e2-4f30-ae48-8840954b843f · outbound

This paper cites Open llm leaderboard v2.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Open llm leaderboard v2

Reference 12

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Observation e1da0c0b-27ac-47ea-bb3c-1a6b8142652a · outbound

This paper cites Time travel in llms: Tracing data contamination in large language models.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Time travel in llms: Tracing data contamination in large language models

Reference 13

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Observation 8178c412-ca1b-45f4-8c1f-4987319c36c6 · outbound

This paper cites Automated evaluation of retrieval-augmented language models with task-specific exam generation.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Automated evaluation of retrieval-augmented language models with task-specific exam generation

Reference 14

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Observation 81453eb1-4329-4599-b071-66bf8072cbe2 · outbound

This paper cites Measuring massive multitask language understanding.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Measuring massive multitask language understanding

Reference 15

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Observation 5b49f598-0ae1-451f-a5fa-5464a234093a · outbound

This paper cites Datagen: Unified synthetic dataset generation via large language models.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Datagen: Unified synthetic dataset generation via large language models

Reference 16

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Observation 4f27019b-e3df-4b9c-8f00-91f772bb388f · outbound

This paper cites Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2

Reference 17

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This paper cites Wildteaming at scale: From in- the-wild jailbreaks to (adversarially) safer language models.Advances in Neural Information Processing Systems, 37:47094–47165, 2024.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Wildteaming at scale: From in- the-wild jailbreaks to (adversarially) safer language models.Advances in Neural Information Processing Systems, 37:47094–47165, 2024

Reference 18

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Observation 7002d98f-da27-43be-829b-d8edf4490dcd · outbound

This paper cites Teaching language models to hallucinate less with synthetic tasks.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Teaching language models to hallucinate less with synthetic tasks

Reference 19

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Observation 34ed43f9-c088-4ec6-9a3f-ff7c1b7f7e9b · outbound

This paper cites Au- tonomous evaluation of llms for truth maintenance and reasoning tasks.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Au- tonomous evaluation of llms for truth maintenance and reasoning tasks

Reference 20

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Observation 736059c6-e70f-43bd-9a8a-6dc29d8e1b03 · outbound

This paper cites Realtime qa: What’s the answer right now? Advances in neural information processing systems, 36:49025–49043, 2023.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Realtime qa: What’s the answer right now? Advances in neural information processing systems, 36:49025–49043, 2023

Reference 21

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This paper cites Particle swarm optimization.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Particle swarm optimization

Reference 22

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This paper cites Openassistant conversations-democratizing large language model alignment.Advances in Neural Information Processing Systems, 36:47669–47681, 2023.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Openassistant conversations-democratizing large language model alignment.Advances in Neural Information Processing Systems, 36:47669–47681, 2023

Reference 23

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Observation e40cc949-e6b2-43c0-b393-e6d8927bf100 · outbound

This paper cites Eliciting Language Model Behaviors with Investigator Agents.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Eliciting Language Model Behaviors with Investigator Agents

Reference 24

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This paper cites Autobencher: Towards declarative benchmark construction.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Autobencher: Towards declarative benchmark construction

Reference 25

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This paper cites Gen- dataagent: On-the-fly dataset augmentation with synthetic data.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Gen- dataagent: On-the-fly dataset augmentation with synthetic data

Reference 26

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Observation 8da932b7-31ef-4c50-8c38-e62277f8c458 · outbound

This paper cites Hemm: Holistic evaluation of multimodal foundation models.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Hemm: Holistic evaluation of multimodal foundation models

Reference 27

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Observation a89be644-4aab-44c0-92d4-34e813ff1c33 · outbound

This paper cites Holistic evaluation of language models.Transactions on Machine Learning Research, 2022.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Holistic evaluation of language models.Transactions on Machine Learning Research, 2022

Reference 28

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Observation 50e6751b-e4e9-4fd8-b1ca-b844c8542f33 · outbound

This paper cites Truthfulqa: Measuring how models mimic human falsehoods.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Truthfulqa: Measuring how models mimic human falsehoods

Reference 29

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Observation 34406dc8-cbb5-4a5b-a9e9-46bc8580c585 · outbound

This paper cites Best practices and lessons learned on synthetic data.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Best practices and lessons learned on synthetic data

Reference 30

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This paper cites Fictitious Synthetic Data Can Improve LLM Factuality via Prerequisite Learning.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Fictitious Synthetic Data Can Improve LLM Factuality via Prerequisite Learning

Reference 31

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This paper cites Adaptive labeling for efficient out-of-distribution model evaluation.Advances in Neural Information Processing Systems, 37:70981–71003, 2024.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Adaptive labeling for efficient out-of-distribution model evaluation.Advances in Neural Information Processing Systems, 37:70981–71003, 2024

Reference 32

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Observation 93bb72f4-c315-4cbb-9533-827ac636cf70 · outbound

This paper cites Unearthing Skill-Level Insights for Understanding Trade-Offs of Foundation Models.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Unearthing Skill-Level Insights for Understanding Trade-Offs of Foundation Models

Reference 33

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This paper cites Enhancing reason- ing capabilities of llms via principled synthetic logic corpus.Advances in Neural Information Processing Systems, 37:73572–73604, 2024.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Enhancing reason- ing capabilities of llms via principled synthetic logic corpus.Advances in Neural Information Processing Systems, 37:73572–73604, 2024

Reference 34

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Observation 68588ff6-2b66-43e7-8058-4dc7072c5046 · outbound

This paper cites Caps: Collaborative and private synthetic data generation from distributed sources.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Caps: Collaborative and private synthetic data generation from distributed sources

Reference 35

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Observation c185db6c-5de2-4728-b983-20224c9e5697 · outbound

This paper cites Humanity's Last Exam.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Humanity's Last Exam

Reference 36

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This paper cites Quantifying language models’ sensitivity to spurious features in prompt design or: How i learned to start worrying about prompt formatting.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Quantifying language models’ sensitivity to spurious features in prompt design or: How i learned to start worrying about prompt formatting

Reference 37

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This paper cites Explore Theory of Mind: Program-guided adversarial data generation for theory of mind reasoning.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Explore Theory of Mind: Program-guided adversarial data generation for theory of mind reasoning

Reference 38

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This paper cites Rl on incorrect synthetic data scales the efficiency of llm math reasoning by eight-fold.Advances in Neural Information Processing Systems, 37:43000–43031, 2024.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Rl on incorrect synthetic data scales the efficiency of llm math reasoning by eight-fold.Advances in Neural Information Processing Systems, 37:43000–43031, 2024

Reference 39

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Observation 2d635b6f-aa87-4f42-b0eb-f251f2f356b5 · outbound

This paper cites Is your benchmark truly adversarial? AdvScore: Evaluating Human-Grounded Adversarialness.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Is your benchmark truly adversarial? AdvScore: Evaluating Human-Grounded Adversarialness

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Observation a4ceafbe-6bbf-488b-b76a-aeffe7a8c783 · outbound

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

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Gemma 2: Improving Open Language Models at a Practical Size

Reference 41

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This paper cites Measuring general intelligence with generated games, 2025.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Measuring general intelligence with generated games, 2025

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This paper cites Cuts: Customizable tabular synthetic data generation.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Cuts: Customizable tabular synthetic data generation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:03:45.994917Z

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Observation fa9c1b34-5dae-480e-8558-4dea15fe50b3 · outbound

This paper cites The Power of LLM-Generated Synthetic Data for Stance Detection in Online Political Discussions.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data The Power of LLM-Generated Synthetic Data for Stance Detection in Online Political Discussions

Reference 44

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This paper cites Glue: A multi-task benchmark and analysis platform for natural language understanding.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Glue: A multi-task benchmark and analysis platform for natural language understanding

Reference 45

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Observation e5220f4a-7129-497a-b745-4f8170da5201 · outbound

This paper cites Can language models solve graph problems in natural language?Advances in Neural Information Processing Systems, 36:30840–30861, 2023.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Can language models solve graph problems in natural language?Advances in Neural Information Processing Systems, 36:30840–30861, 2023

Reference 46

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Observation b7cc40a9-d296-4b25-ac97-2c099f57c4c4 · outbound

This paper cites Pandalm: An automatic evaluation benchmark for llm instruction tuning optimization.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Pandalm: An automatic evaluation benchmark for llm instruction tuning optimization

Reference 47

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Observation 1064b364-68fa-45f1-9753-a3f9176832e9 · outbound

This paper cites Self-instruct: Aligning language models with self-generated in- structions.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Self-instruct: Aligning language models with self-generated in- structions

Reference 48

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Observation f672e44b-eda7-4f67-8007-da3b6ac579dc · outbound

This paper cites Pre-training with synthetic data helps offline reinforcement learning.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Pre-training with synthetic data helps offline reinforcement learning

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:03:45.214965Z

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Observation 2ae4d87d-0c52-43f4-8558-51801c4ca646 · outbound

This paper cites Rocketeval: Efficient automated llm evaluation via grading checklist.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Rocketeval: Efficient automated llm evaluation via grading checklist

Reference 50

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Observation a5a81aa4-3368-492c-8936-94765473778d · outbound

This paper cites Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

Reference 51

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This paper cites Differentially private synthetic data via foundation model apis 2: Text.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Differentially private synthetic data via foundation model apis 2: Text

Reference 52

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Observation d570d6b4-12d0-49a0-b1d7-6ca0dab74574 · outbound

This paper cites Automating dataset updates towards reliable and timely evaluation of large language models.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Automating dataset updates towards reliable and timely evaluation of large language models

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:03:44.651043Z

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Observation 22db04ed-bee3-4fd8-a2eb-5c6de61b3b6e · outbound

This paper cites xfinder: Large language models as automated evaluators for reliable evaluation.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data xfinder: Large language models as automated evaluators for reliable evaluation

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:03:44.481634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 36e675ce-81da-4eaf-a022-33e188051eeb · outbound

This paper cites Hellaswag: Can a machine really finish your sentence? InProceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4791–4800, 2019.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Hellaswag: Can a machine really finish your sentence? InProceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4791–4800, 2019

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T12:03:41.541623Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5df9015-eeba-493d-a863-5c1d497aa6a3 · outbound

This paper cites EvalTree: Profiling Language Model Weaknesses via Hierarchical Capability Trees.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data EvalTree: Profiling Language Model Weaknesses via Hierarchical Capability Trees

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-07T12:03:41.647357Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 421897cc-4d5d-4cfe-90cd-ce34ec6505a1 · outbound

This paper cites Bidirectional LMs are Better Knowledge Memorizers? A Benchmark for Real-world Knowledge Injection.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Bidirectional LMs are Better Knowledge Memorizers? A Benchmark for Real-world Knowledge Injection

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T12:03:41.749831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7fff0ab4-4543-462f-9808-82a749400c7f · outbound

This paper cites Wildchat: 1m chatgpt interaction logs in the wild.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Wildchat: 1m chatgpt interaction logs in the wild

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:03:44.304019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 523b8033-85f6-4e45-b746-d0638c7ad679 · outbound

This paper cites Judging llm-as-a-judge with mt-bench and chatbot arena.Advances in Neural Information Processing Systems, 36:46595–46623, 2023.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Judging llm-as-a-judge with mt-bench and chatbot arena.Advances in Neural Information Processing Systems, 36:46595–46623, 2023

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T12:03:41.967706Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c2b40608-3786-4bce-bc64-3ffb03f02354 · outbound

This paper cites Lima: Less is more for alignment.Advances in Neural Information Processing Systems, 36:55006–55021, 2023.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Lima: Less is more for alignment.Advances in Neural Information Processing Systems, 36:55006–55021, 2023

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:03:44.147466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3f59b8ae-51c8-4f18-bccd-539b6abfc1b2 · outbound

This paper cites Sotopia: Interactive evaluation for social intelligence in language agents.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Sotopia: Interactive evaluation for social intelligence in language agents

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:03:43.981981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 49d8ee22-76eb-4750-a158-8d9a5de1bc00 · outbound

This paper cites Dyval: Dynamic evaluation of large language models for reasoning tasks.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Dyval: Dynamic evaluation of large language models for reasoning tasks

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:03:43.812704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 170741c2-449c-4e06-b467-adda07e6e7b4 · outbound

This paper cites Dynamic evaluation of large language models by meta probing agents.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Dynamic evaluation of large language models by meta probing agents

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:03:43.645456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 09208314-afdc-414f-8d4a-dc9ae8757df8 · outbound

This paper cites Top Secret.

Data Swarms: Optimizable Generation of Synthetic Evaluation Data Top Secret

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:03:43.487172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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