Paper Citation Record · LEDGER

Don't Just Demo, Teach Me the Principles: A Principle-Based Multi-Agent Prompting Strategy for Text Classification

As of 9 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2502.07165.

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

pith.paper-citation-record.v1
2502.07165 v1

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T13:39:04.494165Z

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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 exact0
verified fuzzy2
unresolved14
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation eb565871-cbd2-4159-94e7-39ae737b685c · outbound

This paper cites Chung, H.

Don't Just Demo, Teach Me the Principles: A Principle-Based Multi-Agent Prompting Strategy for Text Classification Chung, H

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:39:04.668384Z

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 ad469335-f9e1-4ca1-bb13-9762c0acdf0a · outbound

This paper cites In Proceedings of the 2023 Conference on Empirical Methods in Natural Lan- guage Processing, 7654–7680.

Don't Just Demo, Teach Me the Principles: A Principle-Based Multi-Agent Prompting Strategy for Text Classification In Proceedings of the 2023 Conference on Empirical Methods in Natural Lan- guage Processing, 7654–7680

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:39:04.657924Z

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 7699f3a4-a11c-4eb7-bb52-9f86f87aa25e · outbound

This paper cites Improving Factuality and Reasoning in Language Models through Multiagent Debate.

Don't Just Demo, Teach Me the Principles: A Principle-Based Multi-Agent Prompting Strategy for Text Classification Improving Factuality and Reasoning in Language Models through Multiagent Debate

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-08T13:39:04.453607Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0cad91d4-b88b-4f43-9da3-d729fdd15386 · outbound

This paper cites Mistral 7B.

Don't Just Demo, Teach Me the Principles: A Principle-Based Multi-Agent Prompting Strategy for Text Classification Mistral 7B

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-08T13:39:04.458099Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d932875-a9b1-4da4-ac3a-be3d9ba9b328 · outbound

This paper cites In-Context Learning Learns Label Relationships but Is Not Conventional Learning.

Don't Just Demo, Teach Me the Principles: A Principle-Based Multi-Agent Prompting Strategy for Text Classification In-Context Learning Learns Label Relationships but Is Not Conventional Learning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-08T13:39:04.462810Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2bd92777-7cf4-4f4f-8da7-224045fa89fe · outbound

This paper cites Zero-Shot Listwise Document Reranking with a Large Language Model.

Don't Just Demo, Teach Me the Principles: A Principle-Based Multi-Agent Prompting Strategy for Text Classification Zero-Shot Listwise Document Reranking with a Large Language Model

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-08T13:39:04.469878Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46fa5ba4-07be-4fe4-b10c-c3620237a5f2 · outbound

This paper cites Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?.

Don't Just Demo, Teach Me the Principles: A Principle-Based Multi-Agent Prompting Strategy for Text Classification Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-08T13:39:04.473233Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b45c7894-61c3-4b03-b761-5fce8ea192e9 · outbound

This paper cites Distilling Reasoning Capabilities into Smaller Language Models.

Don't Just Demo, Teach Me the Principles: A Principle-Based Multi-Agent Prompting Strategy for Text Classification Distilling Reasoning Capabilities into Smaller Language Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-08T13:39:04.476069Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1085bb91-28bc-4be5-bedf-0df833ea6a79 · outbound

This paper cites UL2: Unifying Language Learning Paradigms.

Don't Just Demo, Teach Me the Principles: A Principle-Based Multi-Agent Prompting Strategy for Text Classification UL2: Unifying Language Learning Paradigms

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-08T13:39:04.479498Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e24e2a4-63b7-4ffb-aeac-220de7871c43 · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

Don't Just Demo, Teach Me the Principles: A Principle-Based Multi-Agent Prompting Strategy for Text Classification Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-08T13:39:04.483079Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a89b0f53-7739-4f9e-90de-8b9dc9dcbcb6 · outbound

This paper cites Self-Adaptive In-Context Learning: An Information Compression Perspective for In-Context Example Selection and Ordering.

Don't Just Demo, Teach Me the Principles: A Principle-Based Multi-Agent Prompting Strategy for Text Classification Self-Adaptive In-Context Learning: An Information Compression Perspective for In-Context Example Selection and Ordering

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-08T13:39:04.486749Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7dd22778-e937-45f8-8b8c-25ec876de622 · outbound

This paper cites Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via Debate.

Don't Just Demo, Teach Me the Principles: A Principle-Based Multi-Agent Prompting Strategy for Text Classification Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via Debate

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-08T13:39:04.490242Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1cc21dba-20e9-4191-ab69-50361d04441c · outbound

This paper cites Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models.

Don't Just Demo, Teach Me the Principles: A Principle-Based Multi-Agent Prompting Strategy for Text Classification Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-08T13:39:04.494165Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa8f0e2f-610d-47a7-bc92-e5709b283afa · outbound

This paper cites TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification.

Don't Just Demo, Teach Me the Principles: A Principle-Based Multi-Agent Prompting Strategy for Text Classification TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-08T13:39:04.437282Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 858152c6-646a-40a3-931f-4164230d2875 · outbound

This paper cites Diverse Demonstrations Improve In-context Compositional Generalization.

Don't Just Demo, Teach Me the Principles: A Principle-Based Multi-Agent Prompting Strategy for Text Classification Diverse Demonstrations Improve In-context Compositional Generalization

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-08T13:39:04.466585Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e1350bb-a3e2-4c6b-98e6-28fc48709c94 · outbound

This paper cites ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate.

Don't Just Demo, Teach Me the Principles: A Principle-Based Multi-Agent Prompting Strategy for Text Classification ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-08T13:39:04.442084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.