Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-08T11:57:12.088561Z
Paper Citation Record · LEDGER
As of 9 August 2026, this Paper Citation Record lists 94 of 94 outbound references and 5 inbound Pith citation observations for arXiv:2502.09650.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-08T11:57:12.088561Z
One-hop event checks from named stored sources.
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Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T18:55:17.631077Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T00:37:30.066482Z
94 of 94 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 0a933d5a-ad80-4ede-bab5-ed53bf83892e · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples write newline
Reference 1
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Observation 655266a0-cd06-4946-add8-dca8dff3a089 · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Llama 3 model card
Reference 2
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Observation 57bbada3-4c13-49f8-afac-91e6e5a6ed21 · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples M., Longpre, S., Lambert, N., Wang, X., Muennighoff, N., Hou, B., Pan, L., Jeong, H., Raffel, C., Chang, S., Hashimoto, T., and Wang, W
Reference 3
Source-reported events for the cited work
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Observation 6e6e59b9-9d2e-404c-ac45-9fdc6889f447 · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Perplexed by Perplexity: Perplexity-Based Data Pruning With Small Reference Models
Reference 4
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Observation 605a9ffb-e7c1-4037-9535-20a6c4ab1b3f · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Ultrafeedback binarized preferences-cleaned
Reference 5
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Observation 4dcbc8d6-dd6e-4c34-8d7a-76acbff3d966 · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples A General Language Assistant as a Laboratory for Alignment
Reference 6
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Observation f0859dfe-08ef-4b56-ad86-17e0e915f779 · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Teaching by examples: Implications for the process of category acquisition
Reference 7
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Observation 9da6ea09-c173-49e0-8e8d-8d975e47b590 · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples G., Guo, Z
Reference 8
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Observation b71b82f7-5985-4a1c-afcd-07679dda7d93 · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback
Reference 9
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Observation e261bedc-56bf-4bcc-9069-07ce8a9f60ab · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Unresolved cited work
Reference 10
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Observation 32f02130-c072-4f41-ad63-87d146a7684f · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Open LLM leaderboard
Reference 11
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Observation 07777237-1d3c-4473-aa2d-ea91f2173d2d · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Curriculum learning
Reference 12
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Observation cc0cf199-6327-40dd-b7f4-a3b1c1db935c · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al
Reference 13
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Observation 5bb97408-e321-487e-9c3b-d58ac09da152 · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Curriculum learning for language modeling
Reference 14
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Observation 4fe5805a-6445-47aa-b221-09d44d6ab5f2 · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Instruction Mining: Instruction Data Selection for Tuning Large Language Models
Reference 15
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Observation 62fe3a44-be84-43aa-a070-2c43626dc536 · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Does the order of training samples matter? improving neural data-to-text generation with curriculum learning
Reference 16
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Observation ee77afaa-5587-488d-9163-7eb5f02abd9e · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Alpagasus: Training a better alpaca with fewer data
Reference 17
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Observation 031b1471-498f-4f13-afa7-740b6b25be23 · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Skill-it! a data-driven skills framework for understanding and training language models
Reference 18
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Observation 5a9e2a0a-dfd6-4f37-ba10-92c534bb9e07 · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Sdformer: Similarity-driven discrete transformer for time series generation
Reference 19
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Observation 73615786-de88-418e-a2d8-f9e4bfe2ee1f · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples R., Kini, A., and Natarajan, N
Reference 20
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Observation d01213d0-5a6f-4594-a656-f9104ba1c249 · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples F., Leike, J., Brown, T., Martic, M., Legg, S., and Amodei, D
Reference 21
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Observation a5c8f22b-a6b8-4bf2-afed-ea171b7136f6 · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Training Verifiers to Solve Math Word Problems
Reference 22
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Observation 04942a48-e175-4873-892f-abc86e3a6375 · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Reverse Stable Diffusion: What prompt was used to generate this image?
Reference 23
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Observation dd1ed5de-a3ed-4067-a9a1-abb6b5d77eed · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Curriculum Direct Preference Optimization for Diffusion and Consistency Models
Reference 24
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Observation 2ef54133-80ae-4ec1-a897-b8bae6146a04 · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples MoDS: Model-oriented Data Selection for Instruction Tuning
Reference 25
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Observation 995355fc-80ec-40fa-8905-d823662788d0 · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples The Llama 3 Herd of Models
Reference 26
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Observation 175afe08-8912-4b17-96bd-099c513ca4ee · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Length-controlled alpacaeval: A simple debiasing of automatic evaluators
Reference 27
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Observation 75bfca09-7ec5-4c8a-9aeb-da243389824f · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Dsdm: Model-aware dataset selection with datamodels
Reference 28
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Observation 5c6ec3db-1c06-45e6-a387-f3711f9458b9 · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Understanding dataset difficulty with \ V \ -usable information
Reference 29
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Observation 2eebdb39-6605-40ea-b02c-2819b49dbc07 · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Model alignment as prospect theoretic optimization
Reference 30
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Observation a14c0fb6-3e55-43db-afe2-d1243e18a279 · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Impact of preference noise on the alignment performance of generative language models
Reference 31
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Observation f3e08ea9-aae9-4812-9fae-d47368c74181 · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Understanding finetuning for factual knowledge extraction
Reference 32
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Observation 00099db9-8739-4b1c-813b-d2136ec4efc6 · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples ORPO: Monolithic Preference Optimization without Reference Model
Reference 33
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Observation 1ddbfca8-8e12-4ce2-ab49-3c98d34eabd9 · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples J., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W., et al
Reference 34
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Observation 6793c83e-ec25-47ae-86ab-202543295f95 · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples R., and Kwon, D
Reference 35
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Observation 95d7a382-e5e9-4146-b24d-fda38f6883ba · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Mistral 7B
Reference 36
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Observation 922a2571-2e4d-46d1-8fa7-ff119a734921 · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Unresolved cited work
Reference 37
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Observation c2e50a66-9019-4125-8d4a-f49120d02f91 · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples FastText.zip: Compressing text classification models
Reference 38
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Observation 22f798a6-2699-4fbd-a2c0-01765da5c2ad · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Strategic Data Ordering: Enhancing Large Language Model Performance through Curriculum Learning
Reference 39
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Observation 407c4005-7e06-40a9-ad05-bd90e5f34dfc · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Unresolved cited work
Reference 40
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Observation 6c5f8b8d-a28b-42e0-a1f5-5f11f87b7054 · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples The bigscience roots corpus: A 1.6 tb composite multilingual dataset
Reference 41
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Observation a73ca80c-d3fb-4b66-b229-20d104962919 · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Deduplicating Training Data Makes Language Models Better
Reference 42
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Observation 3fd1f474-239e-468d-8eca-f904ba2e90a2 · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples From quantity to quality: Boosting llm performance with self-guided data selection for instruction tuning
Reference 43
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Observation 6bdd99c2-b198-4483-a296-4380124dc056 · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline
Reference 44
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Observation f3ac5268-774e-4fca-8653-f613abf7228d · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Not all tokens are what you need for pretraining
Reference 45
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Observation 9c87cbab-1c7c-45b6-82ff-9fc427a1c648 · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples High dimensional robust m-estimation: Arbitrary corruption and heavy tails
Reference 46
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Observation a70c0d2d-c428-4ac4-987a-b2fe3b1531d2 · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples What makes good data for alignment? a comprehensive study of automatic data selection in instruction tuning
Reference 47
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Observation 08b2976a-b413-4734-9fab-b0c9f6b01ff6 · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Simpo: Simple preference optimization with a reference-free reward
Reference 48
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Observation c85bd4a2-e299-4a9c-8397-10f4a19d2911 · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples and Sachdeva, B
Reference 49
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Observation e7d6aa9d-decf-4b25-9720-4277ed638ab2 · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples A note on dpo with noisy preferences and relationship to ipo, 2023
Reference 50
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Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Orca: Progressive Learning from Complex Explanation Traces of GPT-4
Reference 51
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Observation 392909e0-5bac-496e-aa51-7cebca0482c2 · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Length-based curriculum learning for efficient pre-training of language models
Reference 52
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Observation ec70e3df-60ad-476f-b01a-f56379d30e89 · outbound
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Reference 53
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Observation 69a027fa-09de-4856-a636-9c3d356ad18b · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Disentangling length from quality in direct preference optimization
Reference 54
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Observation 011012fb-a26c-4fd3-b3be-7af78c817c61 · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Curry-DPO: Enhancing Alignment using Curriculum Learning & Ranked Preferences
Reference 55
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Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples The refinedweb dataset for falcon llm: Outperforming curated corpora with web data only
Reference 56
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Observation ae81375a-4ac7-491b-aacf-0eac71313965 · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Competence-based Curriculum Learning for Neural Machine Translation
Reference 57
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Observation 9a191d7d-59e8-4d31-80bb-c97fca1aaa28 · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Unleashing the power of data tsunami: A comprehensive survey on data assessment and selection for instruction tuning of language models
Reference 58
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Observation 083b9713-f2ce-498c-8666-2674a89f69b8 · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples D., Ermon, S., and Finn, C
Reference 59
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Observation 08e846eb-3a94-4287-ad2b-72e4dabb1f8e · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Does the Definition of Difficulty Matter? Scoring Functions and their Role for Curriculum Learning
Reference 60
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Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Unintentional unalignment: Likelihood displacement in direct preference optimization
Reference 61
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Reference 62
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Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples and Sanghavi, S
Reference 63
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Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples less is more
Reference 64
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Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics
Reference 65
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Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Txt360: A top-quality llm pre-training dataset requires the perfect blend, 2024
Reference 66
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Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples T., Fu, J., Phan, M
Reference 67
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Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Gemma 2: Improving Open Language Models at a Practical Size
Reference 68
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Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples P., and Ferrari, V
Reference 69
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Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Zephyr: Direct Distillation of LM Alignment
Reference 70
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Reference 71
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Observation 90a7941f-47b3-48c6-a163-0442e9a72dc6 · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Step-on-feet tuning: Scaling self-alignment of LLM s via bootstrapping
Reference 72
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Reference 73
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Observation 49a71b7f-cfeb-401b-9744-2c979fcd276a · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples A., Beltagy, I., et al
Reference 74
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Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Ethical and social risks of harm from Language Models
Reference 75
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Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data
Reference 76
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Reference 77
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Observation ca3dbf38-26b2-4c2f-8cf0-69da62b1bb1e · outbound
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Reference 78
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Observation f622c6a1-b7c3-4f5a-8aca-07d4679662ef · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Self-Evolved Diverse Data Sampling for Efficient Instruction Tuning
Reference 79
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Observation 8f2607de-027a-4b4c-b720-0a72b82dd6ed · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples When do curricula work? In ICLR, 2021
Reference 80
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Observation 6df0da23-b153-4c5a-8ac2-fa80700025b4 · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Rethinking Data Selection at Scale: Random Selection is Almost All You Need
Reference 81
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Observation 7e51ab70-6814-4151-b81d-0f1e62d84023 · outbound
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Unresolved cited work
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Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Rethinking the instruction quality: Lift is what you need
Reference 83
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Reference 84
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Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Mates: Model-aware data selection for efficient pretraining with data influence models
Reference 85
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Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Rrhf: Rank responses to align language models with human feedback
Reference 86
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Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Learning to Execute
Reference 87
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Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples A Survey of Large Language Models
Reference 88
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Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples SLiC-HF: Sequence Likelihood Calibration with Human Feedback
Reference 89
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Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Judging llm-as-a-judge with mt-bench and chatbot arena
Reference 90
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Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Lima: Less is more for alignment
Reference 91
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Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples R., Zhao, S., Song, K., Xu, S., and Zhu, C
Reference 92
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Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Iterative data smoothing: Mitigating reward overfitting and overoptimization in rlhf
Reference 93
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Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Exploring the learning difficulty of data: Theory and measure
Reference 94
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CheXPO: Preference Optimization for Chest X-ray VLMs with Counterfactual Rationale Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples
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Reference 20
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Reference 54
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The Hidden Bias of Process Reward Models:PRISM for Rewarding the Right Reasoning Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples
Reference 16
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