Paper Citation Record · LEDGER

Generating Grounded Responses to Counter Misinformation via Learning Efficient Fine-Grained Critiques

As of 7 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2506.05924.

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

pith.paper-citation-record.v1
2506.05924 v1

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:18:31.972204Z

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

16 of 16 outbound references displayed

verified exact2
verified fuzzy6
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 663d65f3-f530-427e-b97e-6d1a77481f1d · outbound

This paper cites Explainable auto- mated fact-checking for public health claims.

Generating Grounded Responses to Counter Misinformation via Learning Efficient Fine-Grained Critiques Explainable auto- mated fact-checking for public health claims

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:33.624837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4f09492d-a70e-4260-9351-38728ffaadd6 · outbound

This paper cites RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback.

Generating Grounded Responses to Counter Misinformation via Learning Efficient Fine-Grained Critiques RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:30.464554Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1791032-9a47-4dd9-84e2-f0c6bd1d2f49 · outbound

This paper cites Mitigat- ing misinformation in online social network with top-k debunkers and evolving user opinions.

Generating Grounded Responses to Counter Misinformation via Learning Efficient Fine-Grained Critiques Mitigat- ing misinformation in online social network with top-k debunkers and evolving user opinions

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:33.071167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9ce8cdf8-1f36-48a1-ab07-de15d7f2cc71 · outbound

This paper cites REPLUG: Retrieval-Augmented Black-Box Language Models.

Generating Grounded Responses to Counter Misinformation via Learning Efficient Fine-Grained Critiques REPLUG: Retrieval-Augmented Black-Box Language Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:30.939472Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6751a688-b8bb-4f36-963d-bc39c93bc704 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Generating Grounded Responses to Counter Misinformation via Learning Efficient Fine-Grained Critiques Gemini: A Family of Highly Capable Multimodal Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:31.052916Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 440b9786-e1d7-4855-9071-6b7029b29b3a · outbound

This paper cites HuggingFace's Transformers: State-of-the-art Natural Language Processing.

Generating Grounded Responses to Counter Misinformation via Learning Efficient Fine-Grained Critiques HuggingFace's Transformers: State-of-the-art Natural Language Processing

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:31.461082Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91698c50-37c6-4985-aa96-29bac62efe06 · outbound

This paper cites Harnessing Network Effect for Fake News Mitigation: Selecting Debunkers via Self-Imitation Learning.

Generating Grounded Responses to Counter Misinformation via Learning Efficient Fine-Grained Critiques Harnessing Network Effect for Fake News Mitigation: Selecting Debunkers via Self-Imitation Learning

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-07T10:18:32.500241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 394b26aa-955b-4111-b954-b3a8e44f586c · outbound

This paper cites Improving Language Models via Plug-and-Play Retrieval Feedback.

Generating Grounded Responses to Counter Misinformation via Learning Efficient Fine-Grained Critiques Improving Language Models via Plug-and-Play Retrieval Feedback

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:31.754673Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ceaeccd6-5d69-4a51-a3cf-905db1a107d1 · outbound

This paper cites JustiLM: Few-shot Justification Generation for Explainable Fact-Checking of Real-world Claims.

Generating Grounded Responses to Counter Misinformation via Learning Efficient Fine-Grained Critiques JustiLM: Few-shot Justification Generation for Explainable Fact-Checking of Real-world Claims

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-08-07T10:18:32.247136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 21cd17f8-673f-49fc-802d-56aa4380cbe5 · outbound

This paper cites How Language Model Hallucinations Can Snowball.

Generating Grounded Responses to Counter Misinformation via Learning Efficient Fine-Grained Critiques How Language Model Hallucinations Can Snowball

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:31.972204Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f82d0388-888e-48db-b0ae-33a5f22247da · outbound

This paper cites Explainable claim verification via knowledge-grounded reasoning with large language models.

Generating Grounded Responses to Counter Misinformation via Learning Efficient Fine-Grained Critiques Explainable claim verification via knowledge-grounded reasoning with large language models

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:32.827542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 26a660d9-1cbd-42a6-9458-ac339ebf28f8 · outbound

This paper cites G-eval: Nlg evalu- ation using gpt-4 with better human alignment.

Generating Grounded Responses to Counter Misinformation via Learning Efficient Fine-Grained Critiques G-eval: Nlg evalu- ation using gpt-4 with better human alignment

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:33.348970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b6600055-5cb7-441f-91fb-b396a21ee045 · outbound

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

Generating Grounded Responses to Counter Misinformation via Learning Efficient Fine-Grained Critiques Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:31.203793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bfb3f3ce-cae2-40f7-abc9-8046ed047411 · outbound

This paper cites Re- inforcement learning-based counter-misinformation re- sponse generation: a case study of covid-19 vaccine misinformation.

Generating Grounded Responses to Counter Misinformation via Learning Efficient Fine-Grained Critiques Re- inforcement learning-based counter-misinformation re- sponse generation: a case study of covid-19 vaccine misinformation

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:33.961835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 84cf82cd-dd7d-4bb9-a064-8b9b2a30ff03 · outbound

This paper cites Overview of check- that! 2020: Automatic identification and verification of claims in social media.

Generating Grounded Responses to Counter Misinformation via Learning Efficient Fine-Grained Critiques Overview of check- that! 2020: Automatic identification and verification of claims in social media

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:34.227458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 14714384-e881-45f2-972a-54bed36d4c83 · outbound

This paper cites FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation.

Generating Grounded Responses to Counter Misinformation via Learning Efficient Fine-Grained Critiques FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:30.713344Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.