Pith. sign in

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 2026-08-14T06:32:32.682623+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

Resolution
unresolved
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=sha256:2a396fb5d50ea5524a82c884246277fbd8aa3d8933727e5045b5637b31694d84

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T21:35:15.509738Z digest=sha256:58c7fcc4db3c0a61fc8e5c17e5350f71be618741cf2b2ac245fe839d77745d5e

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

Resolution
unresolved
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:a2d830babd88409618ae950aac33dc01924841e251c272d285f857e056f2b257

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

Resolution
unresolved
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=sha256:645beae567831f9677af8b16bc69f054566b5e5f7c79412d75a4b836e51f274f

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

Resolution
unresolved
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:f8e69f5b0b718d761879d56939e48ef2477aa38825c8639c50ba98b465cee226

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

Resolution
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=sha256:82e15e515f9c026e7ee5aca2bab68c3c2ba0c9f107f8c30d63586e8216e516f7

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

Resolution
unresolved
no resolver link, observed 2026-08-12T21:35:15.547714Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T21:35:15.547714Z digest=sha256:1fb425598caabe7c3070e0635305e9505397cddfd8692ce426bac27728a44c99

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T21:35:15.552919Z digest=sha256:2b44e4fd5eecdb8dc084ce8ded5d927544ecc07b822ed03f3872de0772273697

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T21:35:15.569887Z digest=sha256:90b2fd57ac917588a5fb74e6ae57d464db22ff0a0d53117ca1247426465584c0

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

Resolution
unresolved
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:a25c641eaadde5ce4e32a3e14e6f38c2a1ceaa81ae8eba898281c462f7277566

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:a1ecf6c8444ee13759ed60d32f0b294156006d490987d27680377bf807f20570

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=sha256:292b440d8a9fa59f55b7d9de89d13c693e192f3a0c7c902ee6de6de3fb36e1b7

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=sha256:80f49e6f25af065b4a84f8236a08c1ec2996e89da994d1b45b592a74c17cbea0

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T21:35:15.558421Z digest=sha256:b69989846be8684e5282a74ab3540d943d16a4723e12fcfe84104de39d5ffa9a

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T21:35:15.498164Z digest=sha256:4b3896d3fe588a89be4f2d7d15eb7416c9ee90b1c7fa2dcd3bb90e20c8770fa7

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T21:35:15.504124Z digest=sha256:598ceba082e10ba6a6a3e7011a8bb604f24db23517eb26a37cd26acec6ff9a61

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

Resolution
unresolved
no resolver link, observed 2026-08-12T21:35:15.480532Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T21:35:15.480532Z digest=sha256:24a36cc335cd3daf002f0633385015151ec3d4585103a83be3942ef955929ad1

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

Resolution
unresolved
no resolver link, observed 2026-08-12T21:35:15.487158Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T21:35:15.487158Z digest=sha256:0f4660b9267f79fb39a416a52c2f26437dda3e85ec66bdc6a04fbc311c6e58de

Pith citing papers

No inbound Pith citation observations are available.