Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions | Paper Citation Record

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This paper cites GPT-4 Technical Report.

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions GPT-4 Technical Report

Reference 1

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This paper cites DeepSeek-V3 Technical Report.

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions DeepSeek-V3 Technical Report

Reference 2

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This paper cites LLaMA: Open and Efficient Foundation Language Models.

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions LLaMA: Open and Efficient Foundation Language Models

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This paper cites Constitutional AI: Harmlessness from AI Feedback.

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Constitutional AI: Harmlessness from AI Feedback

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This paper cites The ai alignment problem: why it is hard, and where to start,.

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions The ai alignment problem: why it is hard, and where to start,

Reference 5

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This paper cites Artificial intelligence, values, and alignment,.

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Artificial intelligence, values, and alignment,

Reference 6

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This paper cites Self-instruct: Aligning language models with self- generated instructions,.

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Self-instruct: Aligning language models with self- generated instructions,

Reference 7

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This paper cites Self-alignment of large language models via monopolylogue-based social scene simulation,.

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Self-alignment of large language models via monopolylogue-based social scene simulation,

Reference 8

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This paper cites Mixture of insighTful Experts (MoTE): The Synergy of Thought Chains and Expert Mixtures in Self-Alignment.

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Mixture of insighTful Experts (MoTE): The Synergy of Thought Chains and Expert Mixtures in Self-Alignment

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This paper cites Balancing differential privacy and utility: A relevance-based adaptive private fine-tuning framework for language models,.

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Balancing differential privacy and utility: A relevance-based adaptive private fine-tuning framework for language models,

Reference 10

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This paper cites Hard adversarial example mining for improving robust fairness,.

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Hard adversarial example mining for improving robust fairness,

Reference 11

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This paper cites Finetuned language models are zero-shot learners,.

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Finetuned language models are zero-shot learners,

Reference 12

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Observation 805dd60b-66fe-4299-86d3-15c85574f5ed · outbound

This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 13

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This paper cites Fine-tuning aligned language models compromises safety, even when users do not intend to!.

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Fine-tuning aligned language models compromises safety, even when users do not intend to!

Reference 14

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Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Stanford alpaca: An instruction-following llama model,

Reference 15

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

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality,

Reference 16

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This paper cites Self-instruct: Aligning language models with self- generated instructions,.

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Self-instruct: Aligning language models with self- generated instructions,

Reference 17

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This paper cites Training language models to follow instructions with human feedback,.

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Training language models to follow instructions with human feedback,

Reference 18

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This paper cites Beavertails: Towards improved safety alignment of llm via a human-preference dataset,.

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Beavertails: Towards improved safety alignment of llm via a human-preference dataset,

Reference 19

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This paper cites Poisoning language models during instruction tuning,.

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Poisoning language models during instruction tuning,

Reference 20

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This paper cites Openassistant conversations-democratizing large language model align- ment,.

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Openassistant conversations-democratizing large language model align- ment,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:07:35.994753Z

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This paper cites Principle-driven self-alignment of language models from scratch with minimal human supervision,.

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Principle-driven self-alignment of language models from scratch with minimal human supervision,

Reference 22

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This paper cites RAIN: Your Language Models Can Align Themselves without Finetuning.

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions RAIN: Your Language Models Can Align Themselves without Finetuning

Reference 23

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Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback

Reference 24

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Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Self-concept clarity development across the lifespan,

Reference 25

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This paper cites Spread Preference Annotation: Direct Preference Judgment for Efficient LLM Alignment.

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Spread Preference Annotation: Direct Preference Judgment for Efficient LLM Alignment

Reference 26

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This paper cites Secrets of RLHF in Large Language Models Part II: Reward Modeling.

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Secrets of RLHF in Large Language Models Part II: Reward Modeling

Reference 27

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Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Variational bayesian un- learning,

Reference 28

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This paper cites An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning.

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning

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Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions A Survey of Large Language Models

Reference 30

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Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions On the Opportunities and Risks of Foundation Models

Reference 31

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Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Safety-tuned llamas: Lessons from improving the safety of large language models that follow instructions,

Reference 32

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Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Fine-grained human feedback gives better rewards for language model training,

Reference 33

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Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Learning to summarize with human feedback,

Reference 34

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Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Rl4f: Generating natural language feedback with reinforcement learning for repairing model outputs,

Reference 35

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Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Gaining wisdom from setbacks: Aligning large language models via mistake analysis,

Reference 36

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This paper cites Understanding negative samples in instance dis- criminative self-supervised representation learning,.

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Understanding negative samples in instance dis- criminative self-supervised representation learning,

Reference 37

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Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Learning From Failure: Integrating Negative Examples when Fine-tuning Large Language Models as Agents

Reference 38

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Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions What makes for good views for contrastive learning?

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Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Neural text degeneration with unlikelihood training,

Reference 40

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This paper cites Openai. gpt-4v(ision) system card,.

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Openai. gpt-4v(ision) system card,

Reference 41

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Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 42

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Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned,

Reference 43

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This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Gemini: A Family of Highly Capable Multimodal Models

Reference 44

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Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Chain-of-thought prompting elicits reasoning in large language models,

Reference 45

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This paper cites The wisdom of hindsight makes language models better instruction followers,.

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions The wisdom of hindsight makes language models better instruction followers,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:07:35.855924Z

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This paper cites Koala: A dialogue model for academic research,.

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Koala: A dialogue model for academic research,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:07:35.846317Z

Source-reported events for the cited work

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Observation 61f8cb44-e1ea-464a-900f-ba2eac9be164 · outbound

This paper cites Glm-130b: An open bilingual pre-trained model,.

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Glm-130b: An open bilingual pre-trained model,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:07:35.836713Z

Source-reported events for the cited work

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Observation a4e281db-3fdd-4ff6-ada7-49ab27538497 · outbound

This paper cites The curious case of neural text degeneration,.

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions The curious case of neural text degeneration,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:07:35.826185Z

Source-reported events for the cited work

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Observation 0e9c7d08-786e-4f8e-9bac-9a6946ca5f81 · outbound

This paper cites Lora: Low-rank adaptation of large language models,.

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Lora: Low-rank adaptation of large language models,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:07:35.815745Z

Source-reported events for the cited work

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Observation 9ef50156-efc3-498c-a623-37771202d5ad · outbound

This paper cites The Llama 3 Herd of Models.

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions The Llama 3 Herd of Models

Reference 51

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Observation 3fdce5e9-8d92-4dc6-9d1d-0bf8844b6519 · outbound

This paper cites Beyond the imitation game: Quantifying and extrapolating the capabilities of language models,.

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Beyond the imitation game: Quantifying and extrapolating the capabilities of language models,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:07:35.805988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation dd711585-734e-43ee-8ba3-4e08b50fd056 · outbound

This paper cites Autodan: Generating stealthy jailbreak prompts on aligned large language models,.

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Autodan: Generating stealthy jailbreak prompts on aligned large language models,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:07:35.796019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation cf6c2c09-2ba9-4856-9d5f-4b273dcf9166 · outbound

This paper cites Harmbench: A standardized evaluation framework for automated red teaming and robust refusal,.

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Harmbench: A standardized evaluation framework for automated red teaming and robust refusal,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:07:35.786305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation cf95be37-1915-4c0a-8207-3ac6d74ec4e3 · outbound

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

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Truthfulqa: Measuring how models mimic human falsehoods,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:07:35.776407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 937b1e5a-7f3e-4358-b548-0f81431ab153 · outbound

This paper cites Measuring massive multitask language understanding,.

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Measuring massive multitask language understanding,

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-08T19:07:35.539940Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b23b4cd2-24f3-4aec-a5ae-6609841d685b · outbound

This paper cites Social iqa: Commonsense reasoning about social interactions,.

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Social iqa: Commonsense reasoning about social interactions,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:07:35.761578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5534bcea-024b-408b-bde5-a85d0ea447b1 · outbound

This paper cites Mitigating the alignment tax of rlhf,.

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Mitigating the alignment tax of rlhf,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:07:35.750924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 712610c3-5c13-4414-9edf-2fd41067a154 · outbound

This paper cites Direct preference optimization: Your language model is secretly a reward model,.

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Direct preference optimization: Your language model is secretly a reward model,

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-08T19:07:35.550161Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1bfb14e8-30c3-4297-bb0d-779945f7f404 · outbound

This paper cites KTO: Model Alignment as Prospect Theoretic Optimization.

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions KTO: Model Alignment as Prospect Theoretic Optimization

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-08T19:07:35.553049Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers