Paper Citation Record · LEDGER

Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting

As of 15 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 3 inbound Pith citation observations for arXiv:2508.20401.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2508.20401 v2

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T15:11:47.496942Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T17:43:03.327967Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-06-30T14:54:45.430884Z

Reference resolution

32 of 32 outbound references displayed

verified exact0
verified fuzzy26
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 51adabc2-14d8-434e-8ca8-42141b434c9e · outbound

This paper cites Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M.

Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:51.786717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 436b2d0e-9a36-4080-a1e3-7141592a93d8 · outbound

This paper cites an unresolved cited work.

Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting Unresolved cited work

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-05T15:11:46.374743Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7fb1745e-87b1-4d9b-b697-7164ab9b0b6d · outbound

This paper cites Text2playlist: Generating personalized playlists from text on deezer, 2025.

Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting Text2playlist: Generating personalized playlists from text on deezer, 2025

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:51.607285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ee94956d-8b22-4b2e-9b48-a01f061b80d6 · outbound

This paper cites Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5), 2023.

Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5), 2023

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:51.516567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7a208a4c-c758-4a09-b57a-14434249007b · outbound

This paper cites Is chatgpt fair for recommenda- tion? evaluating fairness in large language model recommendation.

Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting Is chatgpt fair for recommenda- tion? evaluating fairness in large language model recommendation

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:51.465003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0a7f5d67-2a7c-481f-8f9f-23622d1350c9 · outbound

This paper cites Faireval: A benchmark for evaluating user-level fairness in large language model-based recommender systems.

Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting Faireval: A benchmark for evaluating user-level fairness in large language model-based recommender systems

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:51.397526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c20b3270-8928-406a-abaa-efbbeb1d276e · outbound

This paper cites Cfairllm: Controlling consumer fairness in large language model-based recommender systems.

Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting Cfairllm: Controlling consumer fairness in large language model-based recommender systems

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:51.328828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4639e145-8d3e-4c21-bf0c-14688a0ba858 · outbound

This paper cites Large language models are competitive near cold-start recommenders for language- and item-based preferences, 07 2023.

Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting Large language models are competitive near cold-start recommenders for language- and item-based preferences, 07 2023

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:51.223162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation aa981cea-26d5-48a1-a536-addabe6227cb · outbound

This paper cites Tallrec: An effective and efficient tuning framework to align large language model with recommendation.

Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting Tallrec: An effective and efficient tuning framework to align large language model with recommendation

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:51.113092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4c8cf5e5-23d9-4e9b-b341-00b62d8e2033 · outbound

This paper cites Filterllm: Text-to-distribution llm for billion-scale cold-start recommendation, 2025.

Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting Filterllm: Text-to-distribution llm for billion-scale cold-start recommendation, 2025

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:50.994745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 49fef78a-49a8-4324-8298-8709f037fab8 · outbound

This paper cites Stereoset: Measuring stereotypical bias in pretrained language models.

Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting Stereoset: Measuring stereotypical bias in pretrained language models

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:50.873191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9b6d173b-0554-42f8-9838-02750d049dcc · outbound

This paper cites an unresolved cited work.

Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-08-05T15:11:50.765447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 24d3b7ad-eb0f-4834-9c03-8e86f1efc6d4 · outbound

This paper cites Realtoxicityprompts: Evaluating neural toxic degeneration in language models.

Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting Realtoxicityprompts: Evaluating neural toxic degeneration in language models

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:50.625593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 56fbc1ac-19b7-4ab7-9df2-222a64126e1e · outbound

This paper cites ToxiGen: A large-scale machine-generated dataset for adversarial and implicit hate speech detection.

Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting ToxiGen: A large-scale machine-generated dataset for adversarial and implicit hate speech detection

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:50.474734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4242f4ef-f981-4262-95fd-08dda1ff732e · outbound

This paper cites Ethics and governance of artificial intelligence: Evidence from a survey of machine learning researchers.

Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting Ethics and governance of artificial intelligence: Evidence from a survey of machine learning researchers

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:50.401017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation dc16793e-5920-4ba1-ab46-716bd0240a75 · outbound

This paper cites Eagle: Ethical dataset given from real interactions, 2024.

Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting Eagle: Ethical dataset given from real interactions, 2024

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:50.276108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 85e346a7-7014-4a81-8d02-8d2b090bd8c3 · outbound

This paper cites Towards understanding and mitigating social biases in language models.

Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting Towards understanding and mitigating social biases in language models

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:50.174744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d6f89796-a021-43a8-913a-586f8ee4d6be · outbound

This paper cites Certifying counterfactual bias in llms, 2025.

Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting Certifying counterfactual bias in llms, 2025

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:50.004742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f4dbdd0a-fb0a-43cf-b624-631eb5090b27 · outbound

This paper cites Controlling popularity bias in recommender systems.

Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting Controlling popularity bias in recommender systems

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:49.714018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9a726480-be16-4a08-ad4a-5baff6acc695 · outbound

This paper cites Evaluating the impact of interaction sparsity on machine learning for exposure bias mitigation in recommender systems.

Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting Evaluating the impact of interaction sparsity on machine learning for exposure bias mitigation in recommender systems

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:49.393350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4331736a-193f-49a2-a6a5-76a2ccdc94b3 · outbound

This paper cites Multisided Fairness for Recommendation.

Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting Multisided Fairness for Recommendation

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-05T15:11:47.106719Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71aa21bf-cd00-401d-a610-8aafdca1997f · outbound

This paper cites Beyond accuracy: evaluating recommender systems by coverage and serendipity.

Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting Beyond accuracy: evaluating recommender systems by coverage and serendipity

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:49.118967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 65dc457a-1cbb-49c8-90ce-358326e5dd1d · outbound

This paper cites Calibrated recommendations.

Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting Calibrated recommendations

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:48.924497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ca86e6cf-40c8-4cf8-9215-552edd833909 · outbound

This paper cites Beyond parity: Fairness objectives for collaborative filtering.

Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting Beyond parity: Fairness objectives for collaborative filtering

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:48.755429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4cb2d883-13e6-4d4c-b773-ac4a3acf9900 · outbound

This paper cites A survey on fairness-aware recommender systems, 2023.

Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting A survey on fairness-aware recommender systems, 2023

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:48.634745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f5219695-fe4c-4ab9-bbd1-c9a91a1e09ee · outbound

This paper cites Gonzalez, Hao Zhang, and Ion Stoica.

Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting Gonzalez, Hao Zhang, and Ion Stoica

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-05T15:11:47.294735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 671859c7-ef91-4e5b-a481-763b7265a1a2 · outbound

This paper cites Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei.

Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-05T15:11:47.324333Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ebd73c70-fb60-4dc1-ade1-53d50eba0ef9 · outbound

This paper cites Unmasking gender bias in recommendation systems and enhancing category-aware fairness.

Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting Unmasking gender bias in recommendation systems and enhancing category-aware fairness

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:48.244820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5ee5884d-447d-4fdf-8299-387f8a124fe2 · outbound

This paper cites Challenging fairness: A comprehensive exploration of bias in llm-based recommendations.

Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting Challenging fairness: A comprehensive exploration of bias in llm-based recommendations

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:48.107873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 99ce1f35-cc3a-4c79-aeec-0ebc53930772 · outbound

This paper cites Lian Xiaoli, and Muhammad Mirajul Islam.

Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting Lian Xiaoli, and Muhammad Mirajul Islam

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:48.014738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation baf5e018-bb08-40ec-b613-c2bbeb832a5e · outbound

This paper cites an unresolved cited work.

Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting Unresolved cited work

Reference 31

Resolution: unresolved; raw_fallback, observed 2026-08-05T15:11:47.914743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5e7a3a83-4543-41c2-8f1c-184d97a27666 · outbound

This paper cites Unveiling and mitigating bias in large language model recommendations: A path to fairness, 2024.

Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting Unveiling and mitigating bias in large language model recommendations: A path to fairness, 2024

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:47.799792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

Observation e889d463-69d0-41a1-ab66-9cb02f87041d · inbound

Prominence-Stratified Failure Modes in Retrieval-Augmented Commercial Recommendation: A 37,000-Run Audit cites this paper.

Prominence-Stratified Failure Modes in Retrieval-Augmented Commercial Recommendation: A 37,000-Run Audit Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-06-30T14:54:45.432322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c217dc3b-ce0f-4950-9e4e-5fd95a9e9dc8 · inbound

Critical Acclaim Orientation in Large Language Models: Evidence from Film Preference Elicitation cites this paper.

Critical Acclaim Orientation in Large Language Models: Evidence from Film Preference Elicitation Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T17:43:03.327967Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c8564698-4458-47f3-b31d-d606339fc4b3 · inbound

Invisible to the Machine: Auditing AI Restaurant, Cafe, and Bar Recommendation Against a Complete Market Census cites this paper.

Invisible to the Machine: Auditing AI Restaurant, Cafe, and Bar Recommendation Against a Complete Market Census Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting

Reference 2012

Resolution: unresolved; no resolver link, observed 2026-08-10T15:31:55.494679Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.