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Paper Citation Record · LEDGER

LLMs for Customized Marketing Content Generation and Evaluation at Scale

As of 17 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 3 inbound Pith citation observations for arXiv:2506.17863.

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

pith.paper-citation-record.v1
2506.17863 v1

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:04:00.349808Z

measured 53 of 53 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T00:49:50.824947Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T06:41:36.486229Z

Reference resolution

50 of 50 outbound references displayed

  • verified exact2
  • verified fuzzy12
  • unresolved36
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f2902817-5d44-4b41-927a-40913217b786 · outbound

This paper cites Llm based generation of item-description for recommendation system.

LLMs for Customized Marketing Content Generation and Evaluation at Scale Llm based generation of item-description for recommendation system

Reference 1

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T19:04:00.190915Z digest=sha256:9b2789c4fec8741f540a9c40ab0430de0921f04f27f763a6dd2c6ea69750ba6e

Observation fb289813-f7a2-45b8-b28a-1cbaf030aecc · outbound

This paper cites The claude 3 model family: Opus, sonnet, haiku.Claude-3 Model Card, 1, 2024.

LLMs for Customized Marketing Content Generation and Evaluation at Scale The claude 3 model family: Opus, sonnet, haiku.Claude-3 Model Card, 1, 2024

Reference 2

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verified fuzzy
raw_fallback, observed 2026-08-15T19:04:00.828178Z

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.

source=pdf_text observed=2026-08-15T19:04:00.194941Z digest=sha256:06b4e464a319d8b9d959a0cd0e563ac9d6c8262a2c51852e808f89f8f6e82a36

Observation e13fc6c2-45e2-4665-a22f-6aead3c5c8a1 · outbound

This paper cites Aligning Human and LLM Judgments: Insights from EvalAssist on Task-Specific Evaluations and AI-assisted Assessment Strategy Preferences.

LLMs for Customized Marketing Content Generation and Evaluation at Scale Aligning Human and LLM Judgments: Insights from EvalAssist on Task-Specific Evaluations and AI-assisted Assessment Strategy Preferences

Reference 3

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source=pdf_text observed=2026-08-15T19:04:00.198197Z digest=sha256:e72ef436ca06f8571865f7c45c7480df99d0d66039c43f4ae7b2ba34942ed92d

Observation 48422f63-435e-44b1-aac4-b949a4ecd575 · outbound

This paper cites ReasoningRec: Bridging Personalized Recommendations and Human-Interpretable Explanations through LLM Reasoning.

LLMs for Customized Marketing Content Generation and Evaluation at Scale ReasoningRec: Bridging Personalized Recommendations and Human-Interpretable Explanations through LLM Reasoning

Reference 4

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source=pdf_text observed=2026-08-15T19:04:00.201815Z digest=sha256:d92746787781cd8023249de1b834cfe1a168487c6a5130f14c62363cfd0bc6d4

Observation 6d935abc-bfb9-45e7-80c1-320203547296 · outbound

This paper cites Language models are few-shot learners.Advances in neural infor- mation processing systems, 33:1877–1901, 2020.

LLMs for Customized Marketing Content Generation and Evaluation at Scale Language models are few-shot learners.Advances in neural infor- mation processing systems, 33:1877–1901, 2020

Reference 5

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source=pdf_text observed=2026-08-15T19:04:00.205457Z digest=sha256:090de388009575d24e63cd2fac67a8825aefd81ef26825883ef0266bbaf6b0e1

Observation 09485893-69b7-4a06-a2e2-7b39ca0fac08 · outbound

This paper cites Evaluation of Text Generation: A Survey.

LLMs for Customized Marketing Content Generation and Evaluation at Scale Evaluation of Text Generation: A Survey

Reference 6

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source=pdf_text observed=2026-08-15T19:04:00.208794Z digest=sha256:a8595c6963089e49153387f6c1ab481eebd945a549905d0ffcf4df54166e59ae

Observation 886f2f91-eec4-49d9-b221-903d5e288a87 · outbound

This paper cites GraphCheck: Breaking Long-Term Text Barriers with Extracted Knowledge Graph-Powered Fact-Checking.

LLMs for Customized Marketing Content Generation and Evaluation at Scale GraphCheck: Breaking Long-Term Text Barriers with Extracted Knowledge Graph-Powered Fact-Checking

Reference 7

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source=pdf_text observed=2026-08-15T19:04:00.212063Z digest=sha256:d7a9254ebbc7e0ec24a792e87544aabb504cbe61611686335e62b718dc3038da

Observation 58d0ead0-efc1-476d-bbe6-4417d85f9918 · outbound

This paper cites Palm: Scaling language modeling with pathways.Journal of Machine Learning Research, 24(240):1–113, 2023.

LLMs for Customized Marketing Content Generation and Evaluation at Scale Palm: Scaling language modeling with pathways.Journal of Machine Learning Research, 24(240):1–113, 2023

Reference 8

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no resolver link, observed 2026-08-15T19:04:00.215293Z

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source=pdf_text observed=2026-08-15T19:04:00.215293Z digest=sha256:0d1cb16080edda828723bd22468b7fb1bf5100a221fe9c0340dc2d185198717a

Observation a9eb15c1-d43c-4f83-90e8-30d967870b8d · outbound

This paper cites an unresolved cited work.

LLMs for Customized Marketing Content Generation and Evaluation at Scale Unresolved cited work

Reference 9

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source=pdf_text observed=2026-08-15T19:04:00.218149Z digest=sha256:f463450ff574e77269b051e5309280e998d1e889d40d3db67ac6bb254aea3680

Observation 1a70ded3-8ff1-47a1-9c2b-8de2a70e5b0a · outbound

This paper cites A Survey on LLM Inference-Time Self-Improvement.

LLMs for Customized Marketing Content Generation and Evaluation at Scale A Survey on LLM Inference-Time Self-Improvement

Reference 10

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source=pdf_text observed=2026-08-15T19:04:00.222054Z digest=sha256:a9ce6bf79a32b7547b30b58db04d4639810c371668f74599bd12d196e18f2730

Observation ee6cc25d-6635-4786-94e4-256da8d332a1 · outbound

This paper cites Disclosure and Mitigation of Gender Bias in LLMs.

LLMs for Customized Marketing Content Generation and Evaluation at Scale Disclosure and Mitigation of Gender Bias in LLMs

Reference 11

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source=pdf_text observed=2026-08-15T19:04:00.225711Z digest=sha256:699d01fbc4573af86d2d4659dfe1bc02c3bec31c661450bfeddd3dcd39442415

Observation 3ba7461c-5ffb-4c42-ba9d-847c1eeaca20 · outbound

This paper cites The faiss library.

LLMs for Customized Marketing Content Generation and Evaluation at Scale The faiss library

Reference 12

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source=pdf_text observed=2026-08-15T19:04:00.228721Z digest=sha256:b76d55bf67f414fc8ff6f6b0d54f7501b6a9327473a8345b67b702f933b93938

Observation 139ec512-ac2c-462b-b83d-dee952c01f06 · outbound

This paper cites all-minilm-l6-v2, 2020.

LLMs for Customized Marketing Content Generation and Evaluation at Scale all-minilm-l6-v2, 2020

Reference 13

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raw_fallback, observed 2026-08-15T19:04:00.808068Z

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.

source=pdf_text observed=2026-08-15T19:04:00.231848Z digest=sha256:4343910d00038b5d84d902352892849d8cbad6b14a115f199d72894fffdc6d37

Observation 9b975808-0baa-4d1c-a7a5-db489685025e · outbound

This paper cites Detecting hallucinations in large language models using semantic entropy.Nature, 630 (8017):625–630, 2024.

LLMs for Customized Marketing Content Generation and Evaluation at Scale Detecting hallucinations in large language models using semantic entropy.Nature, 630 (8017):625–630, 2024

Reference 14

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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.

source=pdf_text observed=2026-08-15T19:04:00.234987Z digest=sha256:29bf4e50076641b06d6dbb437c29d34cba77c7fffe076f1cc0525ddc4fd78d72

Observation e8e5d86d-bea3-4bcb-8a4a-538b2e02e82f · outbound

This paper cites Retrieval-Augmented Generation for Large Language Models: A Survey.

LLMs for Customized Marketing Content Generation and Evaluation at Scale Retrieval-Augmented Generation for Large Language Models: A Survey

Reference 15

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source=pdf_text observed=2026-08-15T19:04:00.238270Z digest=sha256:a7a44e51995fca13ab0b3d5ce6cadf82774249045f7a65eb279b01473cbd0661

Observation 5d7e9b3e-9bad-46e6-89b1-bc7366599455 · outbound

This paper cites A Survey on LLM-as-a-Judge.

LLMs for Customized Marketing Content Generation and Evaluation at Scale A Survey on LLM-as-a-Judge

Reference 16

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source=pdf_text observed=2026-08-15T19:04:00.242144Z digest=sha256:f8c2ba83e569672a7f6ac1a1e8595eba9511390faf1492ff5601112d04cbc69c

Observation 7e5e3a40-8fad-4060-afa3-46d5ab866d07 · outbound

This paper cites Pcr-chain: Partial code reuse assisted by hierarchical chaining of prompts on frozen copilot.

LLMs for Customized Marketing Content Generation and Evaluation at Scale Pcr-chain: Partial code reuse assisted by hierarchical chaining of prompts on frozen copilot

Reference 17

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raw_fallback, observed 2026-08-15T19:04:00.792143Z

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.

source=pdf_text observed=2026-08-15T19:04:00.245387Z digest=sha256:50c15aab65e709a344599018de36fae7aab9e60e812ee48401e25edc0e1b67d5

Observation ac8530fb-cbf7-4148-96c5-8d9a923e6a7e · outbound

This paper cites Serp interference network and its applications in search advertising.

LLMs for Customized Marketing Content Generation and Evaluation at Scale Serp interference network and its applications in search advertising

Reference 18

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raw_fallback, observed 2026-08-15T19:04:00.783959Z

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.

source=pdf_text observed=2026-08-15T19:04:00.248209Z digest=sha256:0713e1a6ad387d4cd7b99fca38fcf9b5cb2ad0a0665d59aee3505c351954d9ff

Observation 077ece95-d15a-4299-bd12-5a78c243f155 · outbound

This paper cites Copyright Violations and Large Language Models.

LLMs for Customized Marketing Content Generation and Evaluation at Scale Copyright Violations and Large Language Models

Reference 19

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source=pdf_text observed=2026-08-15T19:04:00.250646Z digest=sha256:12462ae3b0ebe58d4d4cf676862d9e93f4d345157b6ac2f9a605ff3c1389c82d

Observation d899342f-4051-407d-8fad-d3ac864d58a4 · outbound

This paper cites Studying Large Language Model Behaviors Under Context-Memory Conflicts With Real Documents.

LLMs for Customized Marketing Content Generation and Evaluation at Scale Studying Large Language Model Behaviors Under Context-Memory Conflicts With Real Documents

Reference 20

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source=pdf_text observed=2026-08-15T19:04:00.253525Z digest=sha256:b97732056f26d998ceaad01a3543170e0679901eb94ac7cb090328f9409cf0cc

Observation 95f5c8e0-1b11-4e6c-a3d5-d7abddb19128 · outbound

This paper cites Retrieval-augmented generation for knowledge- intensive nlp tasks.

LLMs for Customized Marketing Content Generation and Evaluation at Scale Retrieval-augmented generation for knowledge- intensive nlp tasks

Reference 21

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raw_fallback, observed 2026-08-15T19:04:00.775018Z

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.

source=pdf_text observed=2026-08-15T19:04:00.256726Z digest=sha256:101786af3b11bbb9b61cffff01baae97edcf0f52d4a5f413aaa82af55cc4bb9e

Observation 6df10408-bc5c-4995-94e2-24db50c6af48 · outbound

This paper cites IQA-EVAL: Automatic Evaluation of Human-Model Interactive Question Answering.

LLMs for Customized Marketing Content Generation and Evaluation at Scale IQA-EVAL: Automatic Evaluation of Human-Model Interactive Question Answering

Reference 22

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no resolver link, observed 2026-08-15T19:04:00.259694Z

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source=pdf_text observed=2026-08-15T19:04:00.259694Z digest=sha256:5f6dbd5afa83e0d15c916012b7cf96409e4db8656a97ccc2908bda07f788f4ab

Observation de6fca96-3302-4d70-80d2-9283c2a80f3e · outbound

This paper cites Controllable Text Generation for Large Language Models: A Survey.

LLMs for Customized Marketing Content Generation and Evaluation at Scale Controllable Text Generation for Large Language Models: A Survey

Reference 23

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source=pdf_text observed=2026-08-15T19:04:00.262861Z digest=sha256:1dc715dbe90e37b76a307ce4b5720b1c76323ccd7de74e5a6322312e9ccc916a

Observation 02557b3d-51c9-421b-8e9d-cb05e0a82131 · outbound

This paper cites I-SHEEP: Self-Alignment of LLM from Scratch through an Iterative Self-Enhancement Paradigm.

LLMs for Customized Marketing Content Generation and Evaluation at Scale I-SHEEP: Self-Alignment of LLM from Scratch through an Iterative Self-Enhancement Paradigm

Reference 24

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source=pdf_text observed=2026-08-15T19:04:00.266293Z digest=sha256:15276e7101bec2c9b1756a7b0f75149b5f4d3ea009858ed3f3b352baac353041

Observation f95f7b87-ad43-466e-b69c-e97346b74224 · outbound

This paper cites G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment.

LLMs for Customized Marketing Content Generation and Evaluation at Scale G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment

Reference 25

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source=pdf_text observed=2026-08-15T19:04:00.269275Z digest=sha256:70ac5f39e1844541732912c36f1161ccf33874c7a7814402ccc960990dd30de1

Observation ceacf50a-7c32-4ef5-95bb-bf0f6d53d398 · outbound

This paper cites Can LLMs Follow Simple Rules?.

LLMs for Customized Marketing Content Generation and Evaluation at Scale Can LLMs Follow Simple Rules?

Reference 26

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source=pdf_text observed=2026-08-15T19:04:00.272942Z digest=sha256:2200e327a0ac089b3b8e825da642e51431f930cda67195539b2ad0e19a73b4db

Observation 65e37da5-a50c-4f8c-b369-5e86e67d0984 · outbound

This paper cites Privacy Issues in Large Language Models: A Survey.

LLMs for Customized Marketing Content Generation and Evaluation at Scale Privacy Issues in Large Language Models: A Survey

Reference 27

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source=pdf_text observed=2026-08-15T19:04:00.276689Z digest=sha256:77ea482ebcfc8fb56e0dbaab2eeaa967d9333cfe8f2a34eda9b1ed7fabf898df

Observation d7932f75-fad7-4f0e-bad1-d05cd9a0faaa · outbound

This paper cites Detecting and Mitigating Hallucinations in Multilingual Summarisation.

LLMs for Customized Marketing Content Generation and Evaluation at Scale Detecting and Mitigating Hallucinations in Multilingual Summarisation

Reference 28

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verified exact
local_arxiv, observed 2026-08-15T19:04:00.460697Z

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.

source=pdf_text observed=2026-08-15T19:04:00.279804Z digest=sha256:989e60fa82ac3eecddc4b4cac0947fac311509f16da46c9735363e493c8dc623

Observation 51ded7b6-fb25-4a1e-98ff-18e72a549713 · outbound

This paper cites Applying large language models to sponsored search advertising.URL: https://www.

LLMs for Customized Marketing Content Generation and Evaluation at Scale Applying large language models to sponsored search advertising.URL: https://www

Reference 29

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raw_fallback, observed 2026-08-15T19:04:00.766491Z

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.

source=pdf_text observed=2026-08-15T19:04:00.284057Z digest=sha256:729a9af7ae4fd3bb1d4ca89958e63f6ddac13e74ee3c85b5e8eb01966e9dbb63

Observation 39e72395-1558-4edc-b94e-87487b779188 · outbound

This paper cites Learning to Plan & Reason for Evaluation with Thinking-LLM-as-a-Judge.

LLMs for Customized Marketing Content Generation and Evaluation at Scale Learning to Plan & Reason for Evaluation with Thinking-LLM-as-a-Judge

Reference 30

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source=pdf_text observed=2026-08-15T19:04:00.287318Z digest=sha256:d14b0abf00577053f5503432cc4df3bb77979a2f7ec5af02d4e9526c7e942e42

Observation 0ac23857-1394-4ec9-b1c0-22bbbb60454e · outbound

This paper cites Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning.

LLMs for Customized Marketing Content Generation and Evaluation at Scale Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning

Reference 31

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source=pdf_text observed=2026-08-15T19:04:00.290893Z digest=sha256:d4fbd1e29170ecb89e40108b74aeb47ad5a9824f6f8fbcb9dd8b713ef2addd28

Observation c7a8e4b0-dea2-4c04-a8dc-1dc9bcf45d4b · outbound

This paper cites Who validates the validators? aligning llm-assisted evaluation of llm outputs with human preferences.

LLMs for Customized Marketing Content Generation and Evaluation at Scale Who validates the validators? aligning llm-assisted evaluation of llm outputs with human preferences

Reference 32

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raw_fallback, observed 2026-08-15T19:04:00.758969Z

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.

source=pdf_text observed=2026-08-15T19:04:00.294094Z digest=sha256:1a3a2b625084906bc49470c4111f9325880d54e817aeade4e14bf9ffc9bdf13d

Observation d9977b53-dbfa-4a20-80e4-e8be69543975 · outbound

This paper cites Beyond Instruction Following: Evaluating Inferential Rule Following of Large Language Models.

LLMs for Customized Marketing Content Generation and Evaluation at Scale Beyond Instruction Following: Evaluating Inferential Rule Following of Large Language Models

Reference 33

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source=pdf_text observed=2026-08-15T19:04:00.296919Z digest=sha256:54e0edcea2ca58a167a6926ea0169b878201591aebfab1de99c9f30138ef91fb

Observation 01e14f6d-7c1f-4600-8683-da0b9b8037a5 · outbound

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

LLMs for Customized Marketing Content Generation and Evaluation at Scale Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 34

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source=pdf_text observed=2026-08-15T19:04:00.300773Z digest=sha256:49cdade74cd002f0ad40193a46ef12a9d504ad3862e306929b0328e49e6b350f

Observation 04a39957-aa6e-4a67-9390-10b6b13318e9 · outbound

This paper cites Can ChatGPT Defend its Belief in Truth? Evaluating LLM Reasoning via Debate.

LLMs for Customized Marketing Content Generation and Evaluation at Scale Can ChatGPT Defend its Belief in Truth? Evaluating LLM Reasoning via Debate

Reference 35

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source=pdf_text observed=2026-08-15T19:04:00.303690Z digest=sha256:8d7abb9c2757f694241c0276de812f4ea5c6241f887735c780e10a44a3768e4f

Observation 84bc63b1-4480-4058-9980-859ae81b2f42 · outbound

This paper cites Truong, Simran Arora, Mantas Mazeika, Dan Hendrycks, Zinan Lin, Yu Cheng, Sanmi Koyejo, Dawn Song, and Bo Li.

LLMs for Customized Marketing Content Generation and Evaluation at Scale Truong, Simran Arora, Mantas Mazeika, Dan Hendrycks, Zinan Lin, Yu Cheng, Sanmi Koyejo, Dawn Song, and Bo Li

Reference 36

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verified fuzzy
raw_fallback, observed 2026-08-15T19:04:00.749886Z

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.

source=pdf_text observed=2026-08-15T19:04:00.306442Z digest=sha256:0e5d26f621fed05622ab371391da28e564ffe89f3837b195d508dfbd58629bf3

Observation f71128b2-a442-4fd3-9792-5a09b1b875ba · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.Advances in neural information processing systems, 35: 24824–24837, 2022.

LLMs for Customized Marketing Content Generation and Evaluation at Scale Chain-of-thought prompting elicits reasoning in large language models.Advances in neural information processing systems, 35: 24824–24837, 2022

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:04:00.741780Z

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.

source=pdf_text observed=2026-08-15T19:04:00.309732Z digest=sha256:a48a8c5f70809033402a6ad7350e34e8974763ae9bea1981a7d4654504a24ab7

Observation d284d9da-2a27-48e1-9714-0c2723e0a18f · outbound

This paper cites an unresolved cited work.

LLMs for Customized Marketing Content Generation and Evaluation at Scale Unresolved cited work

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-15T19:04:00.312758Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T19:04:00.312758Z digest=sha256:605f529f6cce1585d038bde2fb420f7f859fa5c801a90842b258d1f3e0227595

Observation dd2ee5cc-6081-4cda-b982-a0bdc535eeb7 · outbound

This paper cites KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniques.

LLMs for Customized Marketing Content Generation and Evaluation at Scale KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniques

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-15T19:04:00.316407Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T19:04:00.316407Z digest=sha256:43b4a80d0a5e2f1a5a56dce05548956536fad1500d5546b4a9a0872433d0d268

Observation 571f540a-363e-4b17-a74c-363e2147e9ca · outbound

This paper cites Retrieval- augmented multimodal language modeling, 2023.

LLMs for Customized Marketing Content Generation and Evaluation at Scale Retrieval- augmented multimodal language modeling, 2023

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:04:00.733230Z

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.

source=pdf_text observed=2026-08-15T19:04:00.319697Z digest=sha256:4b9ac600a4389bd92ccd2e11a14228eb8ea5608cad656cdcbb8724a5e4056e7c

Observation bb1a4eba-0ebc-4705-9609-ed0f5c7a806a · outbound

This paper cites Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-Evaluation.

LLMs for Customized Marketing Content Generation and Evaluation at Scale Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-Evaluation

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-15T19:04:00.322161Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T19:04:00.322161Z digest=sha256:45037f54e93f6fc7e46724c505813cd359dd62bfa9ea8ce327321ac53f300371

Observation 99dce5f7-f1fd-4ea3-8c7c-ba644fabed11 · outbound

This paper cites Judging llm-as-a-judge with mt-bench and chatbot arena.Advances in Neural Information Processing Systems, 36:46595–46623, 2023.

LLMs for Customized Marketing Content Generation and Evaluation at Scale Judging llm-as-a-judge with mt-bench and chatbot arena.Advances in Neural Information Processing Systems, 36:46595–46623, 2023

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-15T19:04:00.325405Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T19:04:00.325405Z digest=sha256:55ac6ab3df2463452853364a6c868a5cdf3e0e512fa64b5fb698cd7126ee7457

Observation a8d9931f-fae5-4ac9-8958-c0014d384f49 · outbound

This paper cites GCOF: Self-iterative Text Generation for Copywriting Using Large Language Model.

LLMs for Customized Marketing Content Generation and Evaluation at Scale GCOF: Self-iterative Text Generation for Copywriting Using Large Language Model

Reference 43

Resolution
verified exact
local_arxiv, observed 2026-08-15T19:04:00.396549Z

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.

source=pdf_text observed=2026-08-15T19:04:00.328171Z digest=sha256:8fa8871714ae55a513ba7f0beb803ce00165bcc281074096c2f85f20b5b7297d

Observation 4eb16f10-c343-4373-ba2f-85acbb9595a7 · outbound

This paper cites Self-Discover: Large Language Models Self-Compose Reasoning Structures.

LLMs for Customized Marketing Content Generation and Evaluation at Scale Self-Discover: Large Language Models Self-Compose Reasoning Structures

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-15T19:04:00.331399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T19:04:00.331399Z digest=sha256:13588df8aef50e9ae681e7a9f5aa601e928e4c525b457d7e0f2d164e21f526cc

Observation 95414dae-2964-4107-a450-78b0d398f103 · outbound

This paper cites an unresolved cited work.

LLMs for Customized Marketing Content Generation and Evaluation at Scale Unresolved cited work

Reference 45

Resolution
unresolved
raw_fallback, observed 2026-08-15T19:04:00.720163Z

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.

source=pdf_text observed=2026-08-15T19:04:00.334878Z digest=sha256:da9f26dacc9bf2e3640e70a747801f2a3cfddaa4dedfd4c885a029b98a067589

Observation 775dc634-2dba-44e1-8b8b-78c933521eb1 · outbound

This paper cites an unresolved cited work.

LLMs for Customized Marketing Content Generation and Evaluation at Scale Unresolved cited work

Reference 46

Resolution
unresolved
raw_fallback, observed 2026-08-15T19:04:00.712162Z

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.

source=pdf_text observed=2026-08-15T19:04:00.338291Z digest=sha256:1e10b7f1a23cf131db333dda4ba1c204a3bcf39ed9e941f179772ebe28a23b24

Observation 0606d7f5-e4e0-47ed-90fd-7940dfe70261 · outbound

This paper cites an unresolved cited work.

LLMs for Customized Marketing Content Generation and Evaluation at Scale Unresolved cited work

Reference 47

Resolution
unresolved
raw_fallback, observed 2026-08-15T19:04:00.703842Z

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.

source=pdf_text observed=2026-08-15T19:04:00.341726Z digest=sha256:78ec1d34867b0e6051c53ddaefb0c4c42db3e317feb793f49697de35c3889c54

Observation deb9cc6a-b85a-40ab-8b3e-86fc4a50a2d8 · outbound

This paper cites an unresolved cited work.

LLMs for Customized Marketing Content Generation and Evaluation at Scale Unresolved cited work

Reference 48

Resolution
unresolved
raw_fallback, observed 2026-08-15T19:04:00.694798Z

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.

source=pdf_text observed=2026-08-15T19:04:00.344077Z digest=sha256:4908f0a4de78c1da1ccd00844a4ea4670812ee26bf184e6443e767cfda499553

Observation dbbfecfc-b0b0-4af1-b318-3f2348f687a1 · outbound

This paper cites an unresolved cited work.

LLMs for Customized Marketing Content Generation and Evaluation at Scale Unresolved cited work

Reference 49

Resolution
unresolved
raw_fallback, observed 2026-08-15T19:04:00.685077Z

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.

source=pdf_text observed=2026-08-15T19:04:00.346775Z digest=sha256:a5e3fce53cbc3816ef42e63b8722c5e46d630a6fd0617e2e6eb830d1f0329366

Observation b5f8483c-fa4e-4da0-8949-f7bced01a35c · outbound

This paper cites Be more flexible with headlines compared to descriptions.

LLMs for Customized Marketing Content Generation and Evaluation at Scale Be more flexible with headlines compared to descriptions

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:04:00.675465Z

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.

source=pdf_text observed=2026-08-15T19:04:00.349808Z digest=sha256:778a79a9fda7bf51c178d9251f3aab032dc36f80503690084f4f2ae658bc3048

Pith citing papers

Observation 02e9e19c-83fd-4f90-8e25-17b45c8ba3eb · inbound

Self-Reasoning Agentic Framework for Narrative Product Grid-Collage Generation cites this paper.

Self-Reasoning Agentic Framework for Narrative Product Grid-Collage Generation LLMs for Customized Marketing Content Generation and Evaluation at Scale

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-05-10T06:41:36.487570Z

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.

source=pdf_text observed=2026-05-10T06:40:02.167172Z digest=sha256:3c9e1a740565053d045154140d066eb7b35dc2fe4a579cd3292c2b221be25911

Observation 452f2bec-d6b3-4545-bcde-596fd92a5793 · inbound

Frontier AI performance across the business disciplines: a case-grounded benchmark of knowledge work and analytical reasoning cites this paper.

Frontier AI performance across the business disciplines: a case-grounded benchmark of knowledge work and analytical reasoning LLMs for Customized Marketing Content Generation and Evaluation at Scale

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-01T21:31:51.839206Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T21:31:51.839206Z digest=sha256:f144fd6e428aea96478006e6559c44157110aeb35f7d1e1cebbb183bc24f1a46

Observation 5e70ae73-351f-4122-94e7-8eb3abb4f9aa · inbound

Frontier AI performance across the business disciplines: a case-grounded benchmark of knowledge work and analytical reasoning cites this paper.

Frontier AI performance across the business disciplines: a case-grounded benchmark of knowledge work and analytical reasoning LLMs for Customized Marketing Content Generation and Evaluation at Scale

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-03T00:49:50.824947Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T00:49:50.824947Z digest=sha256:0c0d19b582a93b132d7a60f71c89f68514d34477fb2fa920179e7bd78563757b