Efficient Alignment of Large Language Models via Data Sampling | Paper Citation Record

Observation 50eea434-db13-4bbd-9d87-7a132f3a0a46 · outbound

This paper cites Llama 3 model card.

Efficient Alignment of Large Language Models via Data Sampling Llama 3 model card

Reference 1

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This paper cites Data pruning and neural scaling laws: fundamental limitations of score-based algorithms.

Efficient Alignment of Large Language Models via Data Sampling Data pruning and neural scaling laws: fundamental limitations of score-based algorithms

Reference 2

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

Efficient Alignment of Large Language Models via Data Sampling Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 3

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This paper cites Pythia: A suite for analyzing large language models across training and scaling, 2023.

Efficient Alignment of Large Language Models via Data Sampling Pythia: A suite for analyzing large language models across training and scaling, 2023

Reference 4

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This paper cites ULMA: Unified Language Model Alignment with Human Demonstration and Point-wise Preference.

Efficient Alignment of Large Language Models via Data Sampling ULMA: Unified Language Model Alignment with Human Demonstration and Point-wise Preference

Reference 5

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This paper cites Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback.

Efficient Alignment of Large Language Models via Data Sampling Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 6

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This paper cites Deep reinforcement learning from human preferences.

Efficient Alignment of Large Language Models via Data Sampling Deep reinforcement learning from human preferences

Reference 7

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This paper cites Hello dolly: Democratizing the magic of chat- gpt with open models, 2023.

Efficient Alignment of Large Language Models via Data Sampling Hello dolly: Democratizing the magic of chat- gpt with open models, 2023

Reference 8

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Observation 14a7b2b9-51b7-4ae3-a9fb-b383efba0e82 · outbound

This paper cites Ultrafeedback: Boosting language models with high-quality feedback, 2023.

Efficient Alignment of Large Language Models via Data Sampling Ultrafeedback: Boosting language models with high-quality feedback, 2023

Reference 9

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

Efficient Alignment of Large Language Models via Data Sampling KTO: Model Alignment as Prospect Theoretic Optimization

Reference 10

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This paper cites Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned,.

Efficient Alignment of Large Language Models via Data Sampling Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned,

Reference 11

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This paper cites Scaling laws for reward model overoptimization.

Efficient Alignment of Large Language Models via Data Sampling Scaling laws for reward model overoptimization

Reference 12

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This paper cites Deepcore: A comprehensive library for coreset selection in deep learning.

Efficient Alignment of Large Language Models via Data Sampling Deepcore: A comprehensive library for coreset selection in deep learning

Reference 13

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Observation aa0dc51c-0600-4a4d-a675-2a285b067eca · outbound

This paper cites Don't Stop Pretraining: Adapt Language Models to Domains and Tasks.

Efficient Alignment of Large Language Models via Data Sampling Don't Stop Pretraining: Adapt Language Models to Domains and Tasks

Reference 14

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This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Efficient Alignment of Large Language Models via Data Sampling LoRA: Low-Rank Adaptation of Large Language Models

Reference 15

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This paper cites Mistral 7B.

Efficient Alignment of Large Language Models via Data Sampling Mistral 7B

Reference 16

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This paper cites OpenAssistant Conversations -- Democratizing Large Language Model Alignment.

Efficient Alignment of Large Language Models via Data Sampling OpenAssistant Conversations -- Democratizing Large Language Model Alignment

Reference 17

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

Efficient Alignment of Large Language Models via Data Sampling What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning

Reference 18

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This paper cites Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models.

Efficient Alignment of Large Language Models via Data Sampling Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models

Reference 19

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

Efficient Alignment of Large Language Models via Data Sampling Training language models to follow instructions with human feedback

Reference 20

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This paper cites Improving language understanding by generative pre-training.

Efficient Alignment of Large Language Models via Data Sampling Improving language understanding by generative pre-training

Reference 21

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Observation 3ed7ad6e-b5db-4026-8e0b-6a6ffabe27d5 · outbound

This paper cites Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms.

Efficient Alignment of Large Language Models via Data Sampling Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms

Reference 22

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

Efficient Alignment of Large Language Models via Data Sampling Direct preference optimization: Your language model is secretly a reward model

Reference 23

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This paper cites How to Train Data-Efficient LLMs.

Efficient Alignment of Large Language Models via Data Sampling How to Train Data-Efficient LLMs

Reference 24

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This paper cites Proximal Policy Optimization Algorithms.

Efficient Alignment of Large Language Models via Data Sampling Proximal Policy Optimization Algorithms

Reference 25

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This paper cites Hashimoto.

Efficient Alignment of Large Language Models via Data Sampling Hashimoto

Reference 26

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This paper cites Fine-tuning Language Models for Factuality.

Efficient Alignment of Large Language Models via Data Sampling Fine-tuning Language Models for Factuality

Reference 27

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

Efficient Alignment of Large Language Models via Data Sampling LLaMA: Open and Efficient Foundation Language Models

Reference 28

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

Efficient Alignment of Large Language Models via Data Sampling Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 29

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This paper cites Zephyr: Direct Distillation of LM Alignment.

Efficient Alignment of Large Language Models via Data Sampling Zephyr: Direct Distillation of LM Alignment

Reference 30

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This paper cites GPT-RE: In-context Learning for Relation Extraction using Large Language Models.

Efficient Alignment of Large Language Models via Data Sampling GPT-RE: In-context Learning for Relation Extraction using Large Language Models

Reference 31

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This paper cites Pandora's White-Box: Precise Training Data Detection and Extraction in Large Language Models.

Efficient Alignment of Large Language Models via Data Sampling Pandora's White-Box: Precise Training Data Detection and Extraction in Large Language Models

Reference 32

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This paper cites GPT-NER: Named Entity Recognition via Large Language Models.

Efficient Alignment of Large Language Models via Data Sampling GPT-NER: Named Entity Recognition via Large Language Models

Reference 33

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

Efficient Alignment of Large Language Models via Data Sampling Finetuned Language Models Are Zero-Shot Learners

Reference 34

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Efficient Alignment of Large Language Models via Data Sampling ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPT

Reference 35

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Efficient Alignment of Large Language Models via Data Sampling Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena

Reference 36

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This paper cites Yes" if the interaction contains an informative signal for alignment and.

Efficient Alignment of Large Language Models via Data Sampling Yes" if the interaction contains an informative signal for alignment and

Reference 37

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This paper cites Drinking on an empty stomach can lead to faster absorption of alcohol and more severe hangover symptoms the next day.

Efficient Alignment of Large Language Models via Data Sampling Drinking on an empty stomach can lead to faster absorption of alcohol and more severe hangover symptoms the next day

Reference 39

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This paper cites Different types of alcohol can have varying levels of congeners, which are impurities that can worsen hangover symptoms.

Efficient Alignment of Large Language Models via Data Sampling Different types of alcohol can have varying levels of congeners, which are impurities that can worsen hangover symptoms

Reference 40

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This paper cites Drinking water between alcoholic drinks is a good start, but it’s also important to hydrate throughout the day and night.

Efficient Alignment of Large Language Models via Data Sampling Drinking water between alcoholic drinks is a good start, but it’s also important to hydrate throughout the day and night

Reference 41

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This paper cites Alcohol can disrupt your sleep, so getting a good night’s rest can help your body recover and reduce the severity of hangover symptoms.

Efficient Alignment of Large Language Models via Data Sampling Alcohol can disrupt your sleep, so getting a good night’s rest can help your body recover and reduce the severity of hangover symptoms

Reference 42

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This paper cites It’s worth noting that everyone’s tolerance for alcohol and their hangover symptoms can vary, so it’s important to pay attention to how your body reacts and adjust accordingly.

Efficient Alignment of Large Language Models via Data Sampling It’s worth noting that everyone’s tolerance for alcohol and their hangover symptoms can vary, so it’s important to pay attention to how your body reacts and adjust accordingly

Reference 43

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This paper cites Try to drink at least 8-10 glasses of water a day.

Efficient Alignment of Large Language Models via Data Sampling Try to drink at least 8-10 glasses of water a day

Reference 44

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This paper cites Try to eat a meal rich in protein and complex carbohydrates before you start drinking.

Efficient Alignment of Large Language Models via Data Sampling Try to eat a meal rich in protein and complex carbohydrates before you start drinking

Reference 45

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Efficient Alignment of Large Language Models via Data Sampling Unresolved cited work

Reference 46

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This paper cites Try to drink a glass of water for every alcoholic beverage you consume.

Efficient Alignment of Large Language Models via Data Sampling Try to drink a glass of water for every alcoholic beverage you consume

Reference 47

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Efficient Alignment of Large Language Models via Data Sampling Unresolved cited work

Reference 48

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Efficient Alignment of Large Language Models via Data Sampling This will help prevent dehydration which can contribute to nausea and headaches

Reference 49

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Efficient Alignment of Large Language Models via Data Sampling This can help prevent dehydration and reduce the severity of hangover symptoms

Reference 50

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Efficient Alignment of Large Language Models via Data Sampling Unresolved cited work

Reference 51

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This paper cites Aim for at least 8-10 glasses of water per day, including during your party.

Efficient Alignment of Large Language Models via Data Sampling Aim for at least 8-10 glasses of water per day, including during your party

Reference 52

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Efficient Alignment of Large Language Models via Data Sampling Unresolved cited work

Reference 53

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This paper cites You can do this at a Ministry of Interior office or by mail.

Efficient Alignment of Large Language Models via Data Sampling You can do this at a Ministry of Interior office or by mail

Reference 54

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This paper cites This can be done online or at a local municipal office.

Efficient Alignment of Large Language Models via Data Sampling This can be done online or at a local municipal office

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:40:16.501745Z

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This paper cites This can be done online or at a local municipal office.

Efficient Alignment of Large Language Models via Data Sampling This can be done online or at a local municipal office

Reference 56

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Efficient Alignment of Large Language Models via Data Sampling Unresolved cited work

Reference 57

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This paper cites You can register for National Insurance at a local municipal office or online.

Efficient Alignment of Large Language Models via Data Sampling You can register for National Insurance at a local municipal office or online

Reference 58

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Observation 30ce098b-8555-4036-8a61-7bc9d91ba0c8 · outbound

This paper cites If you do not have an Israeli passport, apply for one through the Ministry of Foreign Affairs website or at an Israeli embassy/consulate near you.

Efficient Alignment of Large Language Models via Data Sampling If you do not have an Israeli passport, apply for one through the Ministry of Foreign Affairs website or at an Israeli embassy/consulate near you

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:40:16.440154Z

Source-reported events for the cited work

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

Observation a6fc2173-e7d3-4f80-843f-ae11305e3985 · outbound

This paper cites an unresolved cited work.

Efficient Alignment of Large Language Models via Data Sampling Unresolved cited work

Reference 60

Resolution: unresolved; raw_fallback, observed 2026-08-12T19:40:16.424759Z

Source-reported events for the cited work

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

Observation b00c602e-eaa7-4779-8224-6c6142c85cf8 · outbound

This paper cites However, with the right guidance and preparation, it can be a straightforward journey back home.

Efficient Alignment of Large Language Models via Data Sampling However, with the right guidance and preparation, it can be a straightforward journey back home

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:40:16.408698Z

Source-reported events for the cited work

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

Observation ef4747ec-e8c3-4d10-add7-8000d9d99e85 · outbound

This paper cites an unresolved cited work.

Efficient Alignment of Large Language Models via Data Sampling Unresolved cited work

Reference 62

Resolution: unresolved; raw_fallback, observed 2026-08-12T19:40:16.393335Z

Source-reported events for the cited work

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

Observation f57d3ad0-b2c7-45ee-8892-2c796a3dc544 · outbound

This paper cites This type of visa grants new immigrants certain benefits such as subsidized housing options and tax exemptions during their first 10 years in the country [2].

Efficient Alignment of Large Language Models via Data Sampling This type of visa grants new immigrants certain benefits such as subsidized housing options and tax exemptions during their first 10 years in the country [2]

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:40:16.377845Z

Source-reported events for the cited work

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

Observation d15c8625-2fcc-493f-8bc1-289c6046cb8c · outbound

This paper cites Israeli Citizenship and Residence: If you are an Israeli citizen, you do not need to apply for a visa or residence permit to enter Israel.

Efficient Alignment of Large Language Models via Data Sampling Israeli Citizenship and Residence: If you are an Israeli citizen, you do not need to apply for a visa or residence permit to enter Israel

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:40:16.361008Z

Source-reported events for the cited work

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

Observation ea6819b9-94e9-44a5-a388-b41918adbcec · outbound

This paper cites If necessary, renew your passport before traveling back to Israel by contacting the relevant embassy or consulate of your country of citizenship abroad.

Efficient Alignment of Large Language Models via Data Sampling If necessary, renew your passport before traveling back to Israel by contacting the relevant embassy or consulate of your country of citizenship abroad

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:40:16.345629Z

Source-reported events for the cited work

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

Observation 01c87a4c-b554-43ed-8be2-1b4c50a7ae23 · outbound

This paper cites If you do not have an Israeli identity card, you may need to apply for one at an embassy or consulate in your country of residence.

Efficient Alignment of Large Language Models via Data Sampling If you do not have an Israeli identity card, you may need to apply for one at an embassy or consulate in your country of residence

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:40:16.329587Z

Source-reported events for the cited work

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

Observation bbb12f5a-73a2-49da-b4d5-de8427c4570f · outbound

This paper cites Make sure your travel documents are valid and up-to-date before booking the flight.

Efficient Alignment of Large Language Models via Data Sampling Make sure your travel documents are valid and up-to-date before booking the flight

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:40:16.314106Z

Source-reported events for the cited work

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

Observation 3416ef40-d7aa-4e59-9e0b-cf58d0e15d17 · outbound

This paper cites an unresolved cited work.

Efficient Alignment of Large Language Models via Data Sampling Unresolved cited work

Reference 68

Resolution: unresolved; raw_fallback, observed 2026-08-12T19:40:16.298279Z

Source-reported events for the cited work

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

Observation f2c60ebb-fa66-4f6e-af0d-73047a896881 · outbound

This paper cites Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned.

Efficient Alignment of Large Language Models via Data Sampling Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-12T19:40:15.605644Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers