Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning | Paper Citation Record

Observation 199db1fe-006d-42b0-9cae-3f91a61953a0 · outbound

This paper cites Qwen Technical Report.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning Qwen Technical Report

Reference 1

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This paper cites Pythia: A suite for analyzing large language models across training and scaling.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning Pythia: A suite for analyzing large language models across training and scaling

Reference 2

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Observation d4f9737d-fbc4-4ef3-81f8-52aa064d5cd2 · outbound

This paper cites Yu, Qiang Yang, and Xing Xie.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning Yu, Qiang Yang, and Xing Xie

Reference 3

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This paper cites MAGDi: Structured Distillation of Multi-Agent Interaction Graphs Improves Reasoning in Smaller Language Models.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning MAGDi: Structured Distillation of Multi-Agent Interaction Graphs Improves Reasoning in Smaller Language Models

Reference 4

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This paper cites AlpaGasus: Training A Better Alpaca with Fewer Data.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning AlpaGasus: Training A Better Alpaca with Fewer Data

Reference 5

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This paper cites Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality

Reference 6

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Observation 38c96e23-1651-4199-9f7f-c625c2653cf3 · outbound

This paper cites Free dolly: Introducing the world’s first truly open instruction-tuned llm.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning Free dolly: Introducing the world’s first truly open instruction-tuned llm

Reference 7

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Observation b758bef4-0d93-4689-84ee-6d4fb99dab5a · outbound

This paper cites Enhancing Chat Language Models by Scaling High-quality Instructional Conversations.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning Enhancing Chat Language Models by Scaling High-quality Instructional Conversations

Reference 8

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This paper cites GLM: General language model pretraining with autoregressive blank infilling.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning GLM: General language model pretraining with autoregressive blank infilling

Reference 9

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Observation fbb7778b-83db-4141-b825-2edcf7f93bfb · outbound

This paper cites Evaluating Large Language Models: A Comprehensive Survey.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning Evaluating Large Language Models: A Comprehensive Survey

Reference 10

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Observation 48c05bd9-d8a9-42da-ab69-deda9c42281e · outbound

This paper cites Phi-2: The surprising power of small language models.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning Phi-2: The surprising power of small language models

Reference 11

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Observation ff91aa08-9316-4b46-8e0b-9408f2ccd0d6 · outbound

This paper cites Mistral 7B.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning Mistral 7B

Reference 12

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Observation f4b9315f-31c9-4413-9674-87f6bab00d78 · outbound

This paper cites Lion: Adversarial Distillation of Proprietary Large Language Models.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning Lion: Adversarial Distillation of Proprietary Large Language Models

Reference 13

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Observation c4e7a19c-635a-40a0-a39a-778b70b12314 · outbound

This paper cites UNIFIEDQA: Crossing format boundaries with a single QA system.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning UNIFIEDQA: Crossing format boundaries with a single QA system

Reference 14

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Observation 604aa171-6fb2-4fbd-a3c6-bc46b64e1586 · outbound

This paper cites Openassistant conversations- democratizing large language model alignment.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning Openassistant conversations- democratizing large language model alignment

Reference 15

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Observation e93aefa8-283e-4309-96a0-f2771877d1f4 · outbound

This paper cites Camel: Communicative agents for" mind" exploration of large scale language model society.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning Camel: Communicative agents for" mind" exploration of large scale language model society

Reference 16

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Observation f2d72888-d2e9-4b7b-87b5-70d3c717aacc · outbound

This paper cites Selective Reflection-Tuning: Student-Selected Data Recycling for LLM Instruction-Tuning.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning Selective Reflection-Tuning: Student-Selected Data Recycling for LLM Instruction-Tuning

Reference 17

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Observation aa7e2b39-0e95-425d-8487-4c9d22518a73 · outbound

This paper cites Reflection-Tuning: Data Recycling Improves LLM Instruction-Tuning.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning Reflection-Tuning: Data Recycling Improves LLM Instruction-Tuning

Reference 18

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Observation 3dc11b88-fc57-468f-b3d1-2ffc3cfee250 · outbound

This paper cites Superfiltering: Weak-to-strong data filtering for fast instruction-tuning, 2024.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning Superfiltering: Weak-to-strong data filtering for fast instruction-tuning, 2024

Reference 19

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Observation 06e5d5b4-6403-4720-b35d-f0979fd130fd · outbound

This paper cites From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

Reference 20

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Observation 9de6171a-7de3-46c2-92c4-97b6d075b6df · outbound

This paper cites OpenEval: Benchmarking Chinese LLMs across Capability, Alignment and Safety.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning OpenEval: Benchmarking Chinese LLMs across Capability, Alignment and Safety

Reference 21

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Observation 8fdf9a15-9b37-4a3c-b16a-4663db41252b · outbound

This paper cites What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning

Reference 22

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Observation db7f2206-fea3-4d36-9092-c09a0e4d5349 · outbound

This paper cites The Flan Collection: Designing Data and Methods for Effective Instruction Tuning.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning The Flan Collection: Designing Data and Methods for Effective Instruction Tuning

Reference 23

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Observation 015f3ebd-e785-4479-8a93-560038e00441 · outbound

This paper cites # instag: Instruction tagging for analyzing supervised fine-tuning of large language models.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning # instag: Instruction tagging for analyzing supervised fine-tuning of large language models

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:58:04.294858Z

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Observation dcd53dab-0c7e-4d5e-b9aa-546a71955440 · outbound

This paper cites A Comprehensive Overview of Large Language Models.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning A Comprehensive Overview of Large Language Models

Reference 25

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Observation e584c61d-5cef-4a32-a59e-310db05797ce · outbound

This paper cites XGen-7B Technical Report.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning XGen-7B Technical Report

Reference 26

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This paper cites Large Language Model Alignment: A Survey.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning Large Language Model Alignment: A Survey

Reference 27

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This paper cites RoleEval: A Bilingual Role Evaluation Benchmark for Large Language Models.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning RoleEval: A Bilingual Role Evaluation Benchmark for Large Language Models

Reference 28

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Observation 4194a8a6-9ee8-460d-8738-cc56bcb5207c · outbound

This paper cites On the exploitability of instruction tuning, 2023.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning On the exploitability of instruction tuning, 2023

Reference 29

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This paper cites FuxiTranyu: A Multilingual Large Language Model Trained with Balanced Data.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning FuxiTranyu: A Multilingual Large Language Model Trained with Balanced Data

Reference 30

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This paper cites PanGu-$\pi$ Pro:Rethinking Optimization and Architecture for Tiny Language Models.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning PanGu-$\pi$ Pro:Rethinking Optimization and Architecture for Tiny Language Models

Reference 31

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This paper cites Hashimoto.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning Hashimoto

Reference 32

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This paper cites Stanford alpaca: An instruction-following llama model, 2023.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning Stanford alpaca: An instruction-following llama model, 2023

Reference 33

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This paper cites Gemma: Open Models Based on Gemini Research and Technology.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning Gemma: Open Models Based on Gemini Research and Technology

Reference 34

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

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 35

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This paper cites Self-Instruct: Aligning Language Models with Self-Generated Instructions.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning Self-Instruct: Aligning Language Models with Self-Generated Instructions

Reference 36

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This paper cites Smith, Daniel Khashabi, and Hannaneh Hajishirzi.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning Smith, Daniel Khashabi, and Hannaneh Hajishirzi

Reference 37

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Observation d7a40cf1-7a48-4fc0-8e29-f83627a77d9f · outbound

This paper cites Super- NaturalInstructions: Generalization via declarative instructions on 1600+ NLP tasks.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning Super- NaturalInstructions: Generalization via declarative instructions on 1600+ NLP tasks

Reference 38

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Observation e3fdc165-5bd9-481e-bcda-f226ab17c7fc · outbound

This paper cites PanGu-$\pi$: Enhancing Language Model Architectures via Nonlinearity Compensation.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning PanGu-$\pi$: Enhancing Language Model Architectures via Nonlinearity Compensation

Reference 39

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This paper cites Dai, and Quoc V Le.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning Dai, and Quoc V Le

Reference 40

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Observation 3290dec4-3f66-4d78-8faf-29bd5796dcd3 · outbound

This paper cites Finetuned Language Models Are Zero-Shot Learners.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning Finetuned Language Models Are Zero-Shot Learners

Reference 41

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This paper cites Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning

Reference 42

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This paper cites LESS: Selecting Influential Data for Targeted Instruction Tuning.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning LESS: Selecting Influential Data for Targeted Instruction Tuning

Reference 43

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Observation 8599d1e4-e2b9-4795-bc7c-5d311ad2c00a · outbound

This paper cites WizardLM: Empowering large pre-trained language models to follow complex instructions.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning WizardLM: Empowering large pre-trained language models to follow complex instructions

Reference 44

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Observation f398ef56-5fb4-4466-90d8-bc9a0c5fdcb7 · outbound

This paper cites Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data

Reference 45

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Observation 7f534d61-ba28-484e-a1a4-ce63fdeeea07 · outbound

This paper cites Rethinking the instruction quality: Lift is what you need, 2023.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning Rethinking the instruction quality: Lift is what you need, 2023

Reference 46

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Observation 361128d0-55aa-425d-ab6d-65939adc8bb1 · outbound

This paper cites Backdooring instruction-tuned large language models with virtual prompt injection.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning Backdooring instruction-tuned large language models with virtual prompt injection

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:58:04.208457Z

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Observation 602b3b0d-0e7c-4b72-8cf7-7786b68a522e · outbound

This paper cites Virtual prompt injection for instruction-tuned large language models, 2023.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning Virtual prompt injection for instruction-tuned large language models, 2023

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:58:04.194207Z

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Observation 9a0a7d4e-0188-4ef1-a0cc-d063398fffed · outbound

This paper cites Harnessing the power of llms in practice: A survey on chatgpt and beyond, 2023.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning Harnessing the power of llms in practice: A survey on chatgpt and beyond, 2023

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:58:04.180116Z

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Observation f64b69be-8daf-44c5-8572-e8ea4112026c · outbound

This paper cites CrossFit: A few-shot learning challenge for cross-task generalization in NLP.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning CrossFit: A few-shot learning challenge for cross-task generalization in NLP

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:58:04.166277Z

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Observation c487b405-3968-4159-b6da-c3384a2d4802 · outbound

This paper cites GLM-130B: An Open Bilingual Pre-trained Model.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning GLM-130B: An Open Bilingual Pre-trained Model

Reference 51

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Observation 280e74d4-288f-4a44-8fe8-cb62c935c0e4 · outbound

This paper cites OPT: Open Pre-trained Transformer Language Models.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning OPT: Open Pre-trained Transformer Language Models

Reference 52

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Observation 8f3bc009-9f96-46fa-b168-74b8aae10557 · outbound

This paper cites A survey of large language models, 2023.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning A survey of large language models, 2023

Reference 53

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Observation 660c457b-f6ba-4d86-89b9-f179cd2bd993 · outbound

This paper cites [[A]]” if assistant A is the bset, “[[B]].

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning [[A]]” if assistant A is the bset, “[[B]]

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:58:04.143414Z

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Observation 56d366db-e567-4bbd-bf6b-892a388af1f8 · outbound

This paper cites It is a relatively simple and interpretable model that works well with linearly separable datasets.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning It is a relatively simple and interpretable model that works well with linearly separable datasets

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:58:04.129815Z

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Observation 0362ba54-7f52-4c1d-82f6-af5a11c54004 · outbound

This paper cites SVMs can handle high-dimensional data and work well with datasets that are not linearly separable.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning SVMs can handle high-dimensional data and work well with datasets that are not linearly separable

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:58:04.115034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 851c7ba9-5f05-4ce1-a05a-d10a9370c110 · outbound

This paper cites an unresolved cited work.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning Unresolved cited work

Reference 57

Resolution: unresolved; raw_fallback, observed 2026-08-12T15:58:04.101141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2e645562-9589-4f06-9900-1f98e2c41432 · outbound

This paper cites Neural networks can handle non-linear relationships between variables and are capable of learning complex patterns in the data.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning Neural networks can handle non-linear relationships between variables and are capable of learning complex patterns in the data

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:58:04.086556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ca0da12d-bf8b-479a-9805-27ca0fcf9fa9 · outbound

This paper cites It works by estimating the probability of the positive class and using it to make predictions.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning It works by estimating the probability of the positive class and using it to make predictions

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:58:04.073174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e86f8f5d-d462-498c-8d86-810752633b7c · outbound

This paper cites They work by recursively splitting the data into subsets based on the values of the input features.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning They work by recursively splitting the data into subsets based on the values of the input features

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:58:04.058706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1a63f130-0bb3-4109-bff6-a9aa3fa17751 · outbound

This paper cites It works by creating a set of decision trees and then averaging their predictions.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning It works by creating a set of decision trees and then averaging their predictions

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:58:04.045402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation afa3f0cf-359a-4951-9496-fda66e99d10e · outbound

This paper cites It works by finding the hyperplane that maximally separates the classes in the feature space.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning It works by finding the hyperplane that maximally separates the classes in the feature space

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:58:04.031668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 184b5bce-8c0b-4985-aebd-17e3c9a18783 · outbound

This paper cites They can handle complex and nonlinear relationships between the input features and the output labels.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning They can handle complex and nonlinear relationships between the input features and the output labels

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:58:04.017368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7b959a4e-6c25-4064-847f-3e5384337763 · outbound

This paper cites It’s linear and can handle both categorical and continuous features.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning It’s linear and can handle both categorical and continuous features

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:58:04.002846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 07058485-4eae-4681-979c-6301da10b8aa · outbound

This paper cites Random Forest is an ensemble method that combines multiple decision trees, reducing overfitting.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning Random Forest is an ensemble method that combines multiple decision trees, reducing overfitting

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:58:03.988016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c3154dd5-fdba-4e20-9456-33da4f340732 · outbound

This paper cites SVMs are robust to outliers and can be used for both linear and non-linear classification.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning SVMs are robust to outliers and can be used for both linear and non-linear classification

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:58:03.973867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation cd583ffc-846c-480c-9f36-a191a7009f1b · outbound

This paper cites It’s fast, easy to implement, and works well for text classification or when features are not highly correlated.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning It’s fast, easy to implement, and works well for text classification or when features are not highly correlated

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:58:03.959549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6a558432-a31e-4394-994b-93af38ccc58b · outbound

This paper cites It’s non-parametric and can be useful for small datasets.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning It’s non-parametric and can be useful for small datasets

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:58:03.943216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 90fe250c-0b4f-4290-bb8d-3ac65dd0ae94 · outbound

This paper cites They are powerful but require more data and computational resources.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning They are powerful but require more data and computational resources

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:58:03.929442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e945a490-3927-47c7-a6f5-a4ee52b8cae1 · outbound

This paper cites They are often used for structured data and can handle high dimensionality.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning They are often used for structured data and can handle high dimensionality

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:58:03.915475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 725fc435-b7d1-4fd8-afc8-b4789b6cfeb6 · outbound

This paper cites It’s particularly useful when dealing with imbalanced datasets.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning It’s particularly useful when dealing with imbalanced datasets

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:58:03.901543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b3bbb453-45d8-4d7c-9163-d9b559bf12f4 · outbound

This paper cites an unresolved cited work.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning Unresolved cited work

Reference 72

Resolution: unresolved; raw_fallback, observed 2026-08-12T15:58:03.887631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Reference resolution

Outbound references

Pith citing papers