Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T10:18:31.972204Z
Paper Citation Record · LEDGER
Generating Grounded Responses to Counter Misinformation via Learning Efficient Fine-Grained Critiques
As of 10 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.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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.