Paper Citation Record · LEDGER

Aligning Large Language Models with Implicit Preferences from User-Generated Content

As of 9 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.

pith.paper-citation-record.v1
2506.04463 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:50:52.345987Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T23:53:05.505034Z

measured 0 of 1 external citation measurements

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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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.

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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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

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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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+00:00.