Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T10:50:52.345987Z
Paper Citation Record · LEDGER
Aligning Large Language Models with Implicit Preferences from User-Generated Content
As of 19 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 3 inbound Pith citation observations for arXiv:2506.04463.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-02T23:53:05.505034Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-16T10:17:43.784553Z
Reference resolution
34 of 34 outbound references displayed
- verified exact0
- verified fuzzy16
- unresolved16
- parse uncertain0
- malformed identifier2
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 20e9c87c-ce56-4677-9742-c706490a0ef5 · outbound
This paper cites an unresolved cited work.
Aligning Large Language Models with Implicit Preferences from User-Generated Content Unresolved cited work
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:50:52.205294Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 39390a59-a504-4a6e-b61f-eea5a4807d65 · outbound
This paper cites You should refer to the score rubric.
Aligning Large Language Models with Implicit Preferences from User-Generated Content You should refer to the score rubric
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:50:52.217284Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2db296eb-72cf-4157-9048-f5ad6698a968 · outbound
This paper cites (write a feedback for criteria) [RESULT] (an integer number between 1 and 5).
Aligning Large Language Models with Implicit Preferences from User-Generated Content (write a feedback for criteria) [RESULT] (an integer number between 1 and 5)
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:50:52.240558Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7f203460-4aaa-480c-b2a4-809e2a7c1634 · outbound
This paper cites Does the response meet the criteria of quality, considering factors such as helpfulness, relevance, accuracy, depth, creativity, and level of detail?.
Aligning Large Language Models with Implicit Preferences from User-Generated Content Does the response meet the criteria of quality, considering factors such as helpfulness, relevance, accuracy, depth, creativity, and level of detail?
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:50:53.613546Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 2f7fa853-9edd-4b9b-b9f2-82d2ba362b9c · outbound
This paper cites The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Models.
Aligning Large Language Models with Implicit Preferences from User-Generated Content The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Models
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:50:52.001931Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9d8987f5-dcae-4534-872a-cbc62898758f · outbound
This paper cites Self-Alignment with Instruction Backtranslation.
Aligning Large Language Models with Implicit Preferences from User-Generated Content Self-Alignment with Instruction Backtranslation
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:50:52.045091Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a15b8fae-668a-404e-937b-86750aa2b2ce · outbound
This paper cites Let's Verify Step by Step.
Aligning Large Language Models with Implicit Preferences from User-Generated Content Let's Verify Step by Step
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:50:52.075625Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e1b6d9c6-e1fe-4a0a-a63f-f42a2a325baa · outbound
This paper cites SimPO: Simple Preference Optimization with a Reference-Free Reward.
Aligning Large Language Models with Implicit Preferences from User-Generated Content SimPO: Simple Preference Optimization with a Reference-Free Reward
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:50:52.094128Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ef4479f0-cb24-4db8-a1dd-270a815a3ae6 · outbound
This paper cites Disentangling Length from Quality in Direct Preference Optimization.
Aligning Large Language Models with Implicit Preferences from User-Generated Content Disentangling Length from Quality in Direct Preference Optimization
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:50:52.110646Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fb625c15-5994-4293-911c-4a98e22c47b9 · outbound
This paper cites Efficient RLHF: Reducing the Memory Usage of PPO.
Aligning Large Language Models with Implicit Preferences from User-Generated Content Efficient RLHF: Reducing the Memory Usage of PPO
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:50:52.123936Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation db21c847-98cd-4099-bc2e-45cfe1470f2e · outbound
This paper cites Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks.
Aligning Large Language Models with Implicit Preferences from User-Generated Content Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:50:52.145367Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 738674b3-804c-466f-8cb8-2176cf94dc03 · outbound
This paper cites Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint.
Aligning Large Language Models with Implicit Preferences from User-Generated Content Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint
Reference 12
- Resolution
- malformed identifier
- no resolver link, observed 2026-08-07T10:50:52.162823Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7d100f6a-4283-4c51-9754-7f317766cd0d · outbound
This paper cites instruction.
Aligning Large Language Models with Implicit Preferences from User-Generated Content instruction
Reference 14
- Resolution
- malformed identifier
- raw_fallback, observed 2026-08-07T10:50:53.697170Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 88ae97d3-9062-4a4e-9e90-d3bff24fa7ee · outbound
This paper cites According to a study by Pew Research Center, 62% of US adults get news on social media.
Aligning Large Language Models with Implicit Preferences from User-Generated Content According to a study by Pew Research Center, 62% of US adults get news on social media
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:50:53.587132Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation cfe84d48-c03a-4a7f-aa35-c8a6967417df · outbound
This paper cites According to a report by Cisco, video will account for 82% of all internet traffic by 2022.
Aligning Large Language Models with Implicit Preferences from User-Generated Content According to a report by Cisco, video will account for 82% of all internet traffic by 2022
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:50:53.556467Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation a6c24015-6732-4728-b112-de952da9dc06 · outbound
This paper cites According to a report by eMarketer, 24.5 million US adults will use a voice assistant for news in 2022.
Aligning Large Language Models with Implicit Preferences from User-Generated Content According to a report by eMarketer, 24.5 million US adults will use a voice assistant for news in 2022
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:50:53.530251Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 073fab2c-ebda-4910-8946-85a18eafbcc9 · outbound
This paper cites According to a report by Pew Research Center, 43% of US adults get local news daily.
Aligning Large Language Models with Implicit Preferences from User-Generated Content According to a report by Pew Research Center, 43% of US adults get local news daily
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:50:53.244125Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation ad6f6ecb-e2f7-4695-a8ff-8103c66a7e25 · outbound
This paper cites This trend challenges traditional media companies’ monopoly on news production and distribution.
Aligning Large Language Models with Implicit Preferences from User-Generated Content This trend challenges traditional media companies’ monopoly on news production and distribution
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:50:52.977977Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 2c8ca22c-ad56-40bb-8cb3-7d1c2432bc8f · outbound
This paper cites This trend provides an opportunity for media companies to explore new revenue streams through podcast advertising and sponsorships.
Aligning Large Language Models with Implicit Preferences from User-Generated Content This trend provides an opportunity for media companies to explore new revenue streams through podcast advertising and sponsorships
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:50:52.886750Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation a9850e22-ecaf-4237-9fe5-d65a8e7dedcb · outbound
This paper cites This trend can lead to cost savings for media companies and increased efficiency, but it also raises ethical concerns regarding accuracy and fact-checking.
Aligning Large Language Models with Implicit Preferences from User-Generated Content This trend can lead to cost savings for media companies and increased efficiency, but it also raises ethical concerns regarding accuracy and fact-checking
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:50:52.862821Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation fba9b660-7366-4801-a471-6a3843293e77 · outbound
This paper cites This trend creates new opportunities for media companies to generate revenue through advertising and subscription models.
Aligning Large Language Models with Implicit Preferences from User-Generated Content This trend creates new opportunities for media companies to generate revenue through advertising and subscription models
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:50:52.833972Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation b3c65bc9-600b-4d9b-b1d5-8eaaf78b2a91 · outbound
This paper cites This trend provides opportunities for media companies to generate revenue through targeted advertising and subscription models based on user data.
Aligning Large Language Models with Implicit Preferences from User-Generated Content This trend provides opportunities for media companies to generate revenue through targeted advertising and subscription models based on user data
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:50:52.810087Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 3a12e501-8fc2-4682-af6e-75eeb8d222bd · outbound
This paper cites This trend provides opportunities for media companies to generate revenue through targeted advertising based on user data.
Aligning Large Language Models with Implicit Preferences from User-Generated Content This trend provides opportunities for media companies to generate revenue through targeted advertising based on user data
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:50:52.787034Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation f917b136-1fb0-492d-baa1-8a84e082be5c · outbound
This paper cites This trend creates new opportunities for revenue generation through advertising and subscription models based on user engagement and experience.
Aligning Large Language Models with Implicit Preferences from User-Generated Content This trend creates new opportunities for revenue generation through advertising and subscription models based on user engagement and experience
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:50:52.754204Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 2d91e6f7-5afc-4e8c-a8cb-5290df8e6a74 · outbound
This paper cites supposed.
Aligning Large Language Models with Implicit Preferences from User-Generated Content supposed
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:50:52.723165Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation de38f14c-84b7-46ca-a766-43cf5430a8d2 · outbound
This paper cites an unresolved cited work.
Aligning Large Language Models with Implicit Preferences from User-Generated Content Unresolved cited work
Reference 31
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T10:50:52.696827Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 718f2146-bd06-4679-8122-c90233c4c5b7 · outbound
This paper cites Here are some key elements to consider:.
Aligning Large Language Models with Implicit Preferences from User-Generated Content Here are some key elements to consider:
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:50:52.674736Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 510f96c0-e46f-4d6c-b835-45fed0a4c56e · outbound
This paper cites an unresolved cited work.
Aligning Large Language Models with Implicit Preferences from User-Generated Content Unresolved cited work
Reference 33
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T10:50:52.650344Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 4c6a32a4-de99-4c91-be28-b403ebee3715 · outbound
This paper cites an unresolved cited work.
Aligning Large Language Models with Implicit Preferences from User-Generated Content Unresolved cited work
Reference 34
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T10:50:52.624509Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 2cf6572b-77e0-4201-b7db-5003cf94bdfa · outbound
This paper cites Our results show that the prompts generated by PUGC are more closely aligned with those from the Alpaca Eval test set, while the UltraFeedback prompts exhibit greater diversity.
Aligning Large Language Models with Implicit Preferences from User-Generated Content Our results show that the prompts generated by PUGC are more closely aligned with those from the Alpaca Eval test set, while the UltraFeedback prompts exhibit greater diversity
Reference 2021
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:50:53.722967Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 6a1eb151-1653-40bf-9715-819c8ac0382e · outbound
This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.
Aligning Large Language Models with Implicit Preferences from User-Generated Content Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback
Reference 2022
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:50:51.781247Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fdeac280-12ee-4a34-8d95-e1bd082d96a6 · outbound
This paper cites Safe RLHF: Safe Reinforcement Learning from Human Feedback.
Aligning Large Language Models with Implicit Preferences from User-Generated Content Safe RLHF: Safe Reinforcement Learning from Human Feedback
Reference 2023
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:50:51.936330Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6888e78f-1733-4ee2-98df-5ad0618b5cab · outbound
This paper cites Training Verifiers to Solve Math Word Problems.
Aligning Large Language Models with Implicit Preferences from User-Generated Content Training Verifiers to Solve Math Word Problems
Reference 2024
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:50:51.843515Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2147f909-2c58-400f-8ab1-00b208bbf768 · outbound
This paper cites Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, et al.
Aligning Large Language Models with Implicit Preferences from User-Generated Content Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, et al
Reference 4455
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:50:53.748118Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Pith citing papers
Observation 0e53463c-0758-48cf-afa3-1bbdcf2aee10 · inbound
MoCo: A One-Stop Shop for Model Collaboration Research cites this paper.
MoCo: A One-Stop Shop for Model Collaboration Research Aligning Large Language Models with Implicit Preferences from User-Generated Content
Reference 23
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T10:17:43.787411Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation e7d4dfa5-656e-4245-863a-53d83f596bb8 · inbound
Synthetic Interaction Data for Scalable Personalization in Large Language Models cites this paper.
Synthetic Interaction Data for Scalable Personalization in Large Language Models Aligning Large Language Models with Implicit Preferences from User-Generated Content
Reference 50
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T23:53:05.505034Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 823e3322-ecd1-4909-a979-6619d5fdf142 · inbound
Meet Dynamic Individual Preferences: Resolving Conflicting Human Value with Paired Fine-Tuning cites this paper.
Meet Dynamic Individual Preferences: Resolving Conflicting Human Value with Paired Fine-Tuning Aligning Large Language Models with Implicit Preferences from User-Generated Content
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T11:11:05.281778Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.