Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Winning Big with Small Models: Knowledge Distillation vs. Self-Training for Reducing Hallucination in Product QA Agents
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2502.19545.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T22:45:01.734181Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 1f40c54b-8e47-4293-9125-d763d5183b0d · inbound
Enhancing Reasoning Capabilities in SLMs with Reward Guided Dataset Distillation cites this paper.
Enhancing Reasoning Capabilities in SLMs with Reward Guided Dataset Distillation Winning Big with Small Models: Knowledge Distillation vs. Self-Training for Reducing Hallucination in Product QA Agents
Reference 27
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T22:45:01.734181Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fc0a8228-3235-4b12-9449-eb8e668b21f0 · inbound
Quantization Undoes Alignment: Bias Emergence in Compressed LLMs Across Models and Precision Levels cites this paper.
Quantization Undoes Alignment: Bias Emergence in Compressed LLMs Across Models and Precision Levels Winning Big with Small Models: Knowledge Distillation vs. Self-Training for Reducing Hallucination in Product QA Agents
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T17:57:42.512107Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 0c55dd85-6df3-4122-ac1f-901bbdb548d0 · inbound
Understanding Knowledge Distillation in Post-Training: When It Helps and When It Fails cites this paper.
Understanding Knowledge Distillation in Post-Training: When It Helps and When It Fails Winning Big with Small Models: Knowledge Distillation vs. Self-Training for Reducing Hallucination in Product QA Agents
Reference 1
- Resolution
- verified exact
- arxiv_id, observed 2026-06-26T08:39:15.105056Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.