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Paper Citation Record · LEDGER

Leveraging LLMs for Predictive Insights in Food Policy and Behavioral Interventions

As of 15 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2411.08563.

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

pith.paper-citation-record.v1
2411.08563 v1

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T21:35:15.582538Z

measured 18 of 18 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

18 of 18 outbound references displayed

  • verified exact1
  • verified fuzzy3
  • unresolved14
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 68e1fd5a-1207-49c6-9245-f420fa1985d9 · outbound

This paper cites Language models are few-shot learners.

Leveraging LLMs for Predictive Insights in Food Policy and Behavioral Interventions Language models are few-shot learners

Reference 3

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no resolver link, observed 2026-08-12T21:35:15.492614Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T21:35:15.492614Z digest=sha256:3961611d071aa685ae996e6a98f6e083286cf0d99a55bc53086082053ff73a69

Observation b59d62e2-cd96-42ba-99b4-bd581e80acb3 · outbound

This paper cites What do llms know about financial markets? a case study on reddit market sentiment analysis.

Leveraging LLMs for Predictive Insights in Food Policy and Behavioral Interventions What do llms know about financial markets? a case study on reddit market sentiment analysis

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T21:35:16.036879Z

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.

source=pdf_text observed=2026-08-12T21:35:15.509738Z digest=sha256:087e692c4433e684a3ba15838d7ae7b0f388e6f3209d866a7f3d5c5ee8d17264

Observation e5f61cc9-a98b-4c2a-85ae-e0bb6275e212 · outbound

This paper cites Prefix-Tuning: Optimizing Continuous Prompts for Generation.

Leveraging LLMs for Predictive Insights in Food Policy and Behavioral Interventions Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 9

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no resolver link, observed 2026-08-12T21:35:15.525765Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T21:35:15.525765Z digest=sha256:28e9f0084b23d4ea2d5d5be6fe7db3333e738f63e355e4eab02b7dd1ebc553aa

Observation d0a2472e-c188-40e6-a626-036644dea9a3 · outbound

This paper cites Choice architecture promotes sustainable choices in online food-delivery apps.PNAS Nexus, pp.

Leveraging LLMs for Predictive Insights in Food Policy and Behavioral Interventions Choice architecture promotes sustainable choices in online food-delivery apps.PNAS Nexus, pp

Reference 10

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no resolver link, observed 2026-08-12T21:35:15.531797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T21:35:15.531797Z digest=sha256:c182fb0714a7b0c63449feaa1ab76d0f6bc8833025e25a28112bcc611d7a0911

Observation fafdab6d-6ba4-4855-aa2a-38721bc1bfc7 · outbound

This paper cites An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning.

Leveraging LLMs for Predictive Insights in Food Policy and Behavioral Interventions An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning

Reference 11

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no resolver link, observed 2026-08-12T21:35:15.536715Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T21:35:15.536715Z digest=sha256:47e0e8fe48d4cbd93c60579a8ce3e01fff862a9f75f97224033d4981ccd10b6e

Observation 8977b427-2636-4d42-82e8-255df394f354 · outbound

This paper cites Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation.

Leveraging LLMs for Predictive Insights in Food Policy and Behavioral Interventions Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation

Reference 12

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unresolved
no resolver link, observed 2026-08-12T21:35:15.541880Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T21:35:15.541880Z digest=sha256:d82e5e162e4d813ce06a398fc06a5c2001bf9cf3586cafaa9013d403557d82f5

Observation 30107294-266b-4f7c-bf73-a60acd579c31 · outbound

This paper cites Using LLMs to Model the Beliefs and Preferences of Targeted Populations.

Leveraging LLMs for Predictive Insights in Food Policy and Behavioral Interventions Using LLMs to Model the Beliefs and Preferences of Targeted Populations

Reference 13

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T21:35:15.547714Z digest=sha256:c81b276a36e9ca8732b9638da75c66edf24fc1bf26427aafbd4d05b3348cb754

Observation a67fc493-3f10-46d0-986e-09003c5a0158 · outbound

This paper cites Language Models as Knowledge Bases?.

Leveraging LLMs for Predictive Insights in Food Policy and Behavioral Interventions Language Models as Knowledge Bases?

Reference 14

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no resolver link, observed 2026-08-12T21:35:15.552919Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T21:35:15.552919Z digest=sha256:42955d6afc7dc6758659943c6becac081057d9d5128de6292734ce57089f7685

Observation 2bd364cd-bb20-4a0d-a13a-c2b9cdf97af0 · outbound

This paper cites Fine Tuning LLM for Enterprise: Practical Guidelines and Recommendations.

Leveraging LLMs for Predictive Insights in Food Policy and Behavioral Interventions Fine Tuning LLM for Enterprise: Practical Guidelines and Recommendations

Reference 17

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no resolver link, observed 2026-08-12T21:35:15.569887Z

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source=pdf_text observed=2026-08-12T21:35:15.569887Z digest=sha256:7a7926c77bcffb9f8127865540000bbaad676283431dce818634346e30e7247b

Observation 2dcd5189-2653-4560-9486-397eb26a22fe · outbound

This paper cites BloombergGPT: A Large Language Model for Finance.

Leveraging LLMs for Predictive Insights in Food Policy and Behavioral Interventions BloombergGPT: A Large Language Model for Finance

Reference 18

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no resolver link, observed 2026-08-12T21:35:15.576770Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T21:35:15.576770Z digest=sha256:e2349b11eabc5820991c0d9c73d411069272688c891185b46a68b3da8af41abc

Observation 37981224-586c-45d3-a5ef-c1b20d6277d4 · outbound

This paper cites PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance.

Leveraging LLMs for Predictive Insights in Food Policy and Behavioral Interventions PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-12T21:35:15.582538Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T21:35:15.582538Z digest=sha256:44f5c2034db8341405050a6473b73eaf4e6dce51d01a0c9f9bb68d4936927d01

Observation afc502ba-3a02-4d8f-978b-05972e225180 · outbound

This paper cites Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting.

Leveraging LLMs for Predictive Insights in Food Policy and Behavioral Interventions Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting

Reference 2013

Resolution
unresolved
no resolver link, observed 2026-08-12T21:35:15.564134Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T21:35:15.564134Z digest=sha256:78975d4975c4da08a64e0e1bfb03cbaf6b3c29520d60ba77c57aea5987581c2c

Observation 784f929f-030b-4f6a-a6d2-65dee1d4051c · outbound

This paper cites Universal Language Model Fine-tuning for Text Classification.

Leveraging LLMs for Predictive Insights in Food Policy and Behavioral Interventions Universal Language Model Fine-tuning for Text Classification

Reference 2018

Resolution
unresolved
no resolver link, observed 2026-08-12T21:35:15.520916Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T21:35:15.520916Z digest=sha256:3d1a7e93e865afddc3663052e30b091ce5af698f0177461387d03d4cb02621ff

Observation 2c16d068-4a6c-4f3b-8ee6-6dc6e698c982 · outbound

This paper cites What shapes sustainable food choices? a field ex- periment on the impact of a behaviorally informed intervention and a price variation on sustainable food choices.

Leveraging LLMs for Predictive Insights in Food Policy and Behavioral Interventions What shapes sustainable food choices? a field ex- periment on the impact of a behaviorally informed intervention and a price variation on sustainable food choices

Reference 2019

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T21:35:16.018677Z

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.

source=pdf_text observed=2026-08-12T21:35:15.558421Z digest=sha256:02e77296a8ecffe9cf06085fff45301bbd604ab6421eb218b501c2db622635ae

Observation 2c371b6e-b4b1-4b1a-961b-75f728541f7a · outbound

This paper cites Evaluating the replicability of social science experiments in nature and science between 2010 and 2015.Nature human behaviour, 2(9): 637–644,.

Leveraging LLMs for Predictive Insights in Food Policy and Behavioral Interventions Evaluating the replicability of social science experiments in nature and science between 2010 and 2015.Nature human behaviour, 2(9): 637–644,

Reference 2020

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T21:35:16.053894Z

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.

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Observation f44c80f9-46d9-4937-961a-cda91461e902 · outbound

This paper cites Template-Based Named Entity Recognition Using BART.

Leveraging LLMs for Predictive Insights in Food Policy and Behavioral Interventions Template-Based Named Entity Recognition Using BART

Reference 2021

Resolution
verified exact
local_arxiv, observed 2026-08-12T21:35:15.957141Z

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.

source=pdf_text observed=2026-08-12T21:35:15.504124Z digest=sha256:90f32f209d9e1283f738e417148cd51a77fd989e1edaf6c82243513e9a981dc2

Observation 3d65fc8d-40c9-4016-a00c-3c7dffc59892 · outbound

This paper cites Large Language Models for Mathematical Reasoning: Progresses and Challenges.

Leveraging LLMs for Predictive Insights in Food Policy and Behavioral Interventions Large Language Models for Mathematical Reasoning: Progresses and Challenges

Reference 2023

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source=pdf_text observed=2026-08-12T21:35:15.480532Z digest=sha256:c970afa387135a0ce7679cd508fd765807a76c752040ed4406d2d44e4575d55e

Observation b6803e71-1d0f-4ba2-9fb3-72d1360e374d · outbound

This paper cites The Impact of Large Language Models on Scientific Discovery: a Preliminary Study using GPT-4.

Leveraging LLMs for Predictive Insights in Food Policy and Behavioral Interventions The Impact of Large Language Models on Scientific Discovery: a Preliminary Study using GPT-4

Reference 2024

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Pith citing papers

No inbound Pith citation observations are available.