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Paper Citation Record · LEDGER

Knowledge Augmented Finetuning Matters in both RAG and Agent Based Dialog Systems

As of 17 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2506.22852.

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

pith.paper-citation-record.v1
2506.22852 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:00:01.588301Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

31 of 31 outbound references displayed

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  • verified fuzzy21
  • unresolved10
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7b74b61b-a026-4c0f-aa3a-d2f0974d86a2 · outbound

This paper cites GPT-4 Technical Report.

Knowledge Augmented Finetuning Matters in both RAG and Agent Based Dialog Systems GPT-4 Technical Report

Reference 1

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Observation 558424f3-5c35-45c1-ab33-666b63298b5e · outbound

This paper cites Palm: Scaling language modeling with pathways,.

Knowledge Augmented Finetuning Matters in both RAG and Agent Based Dialog Systems Palm: Scaling language modeling with pathways,

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 3eb2bd1f-de21-465f-b8a8-e436c555614e · outbound

This paper cites Survey of hallucination in natural language generation,.

Knowledge Augmented Finetuning Matters in both RAG and Agent Based Dialog Systems Survey of hallucination in natural language generation,

Reference 3

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 9ad4a417-8458-49d0-9bc2-a100090de106 · outbound

This paper cites Retrieval-augmented generation for knowledge-intensive NLP tasks,.

Knowledge Augmented Finetuning Matters in both RAG and Agent Based Dialog Systems Retrieval-augmented generation for knowledge-intensive NLP tasks,

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation aaa40be8-0d4f-4171-a922-dc9dd1e5bb87 · outbound

This paper cites Atlas: Few-shot Learning with Retrieval Augmented Language Models.

Knowledge Augmented Finetuning Matters in both RAG and Agent Based Dialog Systems Atlas: Few-shot Learning with Retrieval Augmented Language Models

Reference 5

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 58c594ed-1588-452f-a95a-12284cf41a7e · outbound

This paper cites Knowledge-retrieval task-oriented dialog systems with semi- supervision,.

Knowledge Augmented Finetuning Matters in both RAG and Agent Based Dialog Systems Knowledge-retrieval task-oriented dialog systems with semi- supervision,

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation dc21dc1a-9e6e-4c3e-a094-64b198148138 · outbound

This paper cites Generative agents: Interactive simulacra of human behavior,.

Knowledge Augmented Finetuning Matters in both RAG and Agent Based Dialog Systems Generative agents: Interactive simulacra of human behavior,

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 45e877b2-bb02-49ee-b663-9240f81fafad · outbound

This paper cites Toolformer: Language models can teach themselves to use tools,.

Knowledge Augmented Finetuning Matters in both RAG and Agent Based Dialog Systems Toolformer: Language models can teach themselves to use tools,

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 437e70dc-3371-415b-aa45-6c592aaba4ac · outbound

This paper cites React: Synergizing reasoning and acting in language models,.

Knowledge Augmented Finetuning Matters in both RAG and Agent Based Dialog Systems React: Synergizing reasoning and acting in language models,

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation e6cd44f3-eb7c-4740-8564-9cdf7933fda2 · outbound

This paper cites Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine.

Knowledge Augmented Finetuning Matters in both RAG and Agent Based Dialog Systems Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine

Reference 10

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 57d5bf82-b0ed-40e6-a1f0-041237f1d47e · outbound

This paper cites FireAct: Toward Language Agent Fine-tuning.

Knowledge Augmented Finetuning Matters in both RAG and Agent Based Dialog Systems FireAct: Toward Language Agent Fine-tuning

Reference 11

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Unavailable: canonical work link unavailable.

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Observation bc88e3d1-0cd3-49c2-96b4-98088858cd67 · outbound

This paper cites Efficient Tool Use with Chain-of-Abstraction Reasoning.

Knowledge Augmented Finetuning Matters in both RAG and Agent Based Dialog Systems Efficient Tool Use with Chain-of-Abstraction Reasoning

Reference 12

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Unavailable: canonical work link unavailable.

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Observation da0d8e46-fa56-44b5-acd8-d7927eeee0ab · outbound

This paper cites RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture.

Knowledge Augmented Finetuning Matters in both RAG and Agent Based Dialog Systems RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture

Reference 13

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Unavailable: canonical work link unavailable.

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Observation c9d4b8d7-97ee-4220-91ed-40c6102b3773 · outbound

This paper cites Fine tuning vs. retrieval augmented generation for less popular knowledge,.

Knowledge Augmented Finetuning Matters in both RAG and Agent Based Dialog Systems Fine tuning vs. retrieval augmented generation for less popular knowledge,

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation b011a3c0-6de0-4f25-aa98-aa04e753e510 · outbound

This paper cites Lora: Low-rank adaptation of large language models.,.

Knowledge Augmented Finetuning Matters in both RAG and Agent Based Dialog Systems Lora: Low-rank adaptation of large language models.,

Reference 15

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Unavailable: canonical work link unavailable.

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Observation 8de27fb2-1ed0-4ba6-bf4a-991f9b5e9d97 · outbound

This paper cites Scaling instruction-finetuned language models,.

Knowledge Augmented Finetuning Matters in both RAG and Agent Based Dialog Systems Scaling instruction-finetuned language models,

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 00566b73-dfeb-4020-b607-a491905a0110 · outbound

This paper cites The 2nd FutureDial challenge: Dialog systems with retrieval aug- mented generation (FutureDial-RAG),.

Knowledge Augmented Finetuning Matters in both RAG and Agent Based Dialog Systems The 2nd FutureDial challenge: Dialog systems with retrieval aug- mented generation (FutureDial-RAG),

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation debc8ba0-29f6-44a2-90e7-a4a22fcf98c6 · outbound

This paper cites Language models are few-shot learners,.

Knowledge Augmented Finetuning Matters in both RAG and Agent Based Dialog Systems Language models are few-shot learners,

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 0bf0d3e4-47f0-4907-93f8-ab94dd10092d · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models,.

Knowledge Augmented Finetuning Matters in both RAG and Agent Based Dialog Systems Chain-of-thought prompting elicits reasoning in large language models,

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation aad72ad9-c131-4b4a-a3c0-33cea74b6dac · outbound

This paper cites Training language models to follow instructions with human feedback,.

Knowledge Augmented Finetuning Matters in both RAG and Agent Based Dialog Systems Training language models to follow instructions with human feedback,

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 5b946932-a4df-496d-82f6-9f597873828b · outbound

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

Knowledge Augmented Finetuning Matters in both RAG and Agent Based Dialog Systems Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 21

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 0444a69e-0e8d-49b9-9862-545732bd975d · outbound

This paper cites Dense passage retrieval for open-domain question answering,.

Knowledge Augmented Finetuning Matters in both RAG and Agent Based Dialog Systems Dense passage retrieval for open-domain question answering,

Reference 22

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation b241e9c3-4690-44b5-9ec8-62894263c61e · outbound

This paper cites Re2G: Retrieve, rerank, generate,.

Knowledge Augmented Finetuning Matters in both RAG and Agent Based Dialog Systems Re2G: Retrieve, rerank, generate,

Reference 23

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raw_fallback, observed 2026-08-06T22:00:02.848300Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 06fb1dde-4eb7-46a1-89bd-573f96669d09 · outbound

This paper cites Unsupervised dense information retrieval with contrastive learning,.

Knowledge Augmented Finetuning Matters in both RAG and Agent Based Dialog Systems Unsupervised dense information retrieval with contrastive learning,

Reference 24

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 189f216c-643e-4a57-91ca-5daed9540beb · outbound

This paper cites REALM: retrieval-augmented language model pre-training,.

Knowledge Augmented Finetuning Matters in both RAG and Agent Based Dialog Systems REALM: retrieval-augmented language model pre-training,

Reference 25

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raw_fallback, observed 2026-08-06T22:00:02.534197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation f36d4c9e-4bf4-4d16-aa0d-d0353c3562b2 · outbound

This paper cites RAFT: Adapting Language Model to Domain Specific RAG.

Knowledge Augmented Finetuning Matters in both RAG and Agent Based Dialog Systems RAFT: Adapting Language Model to Domain Specific RAG

Reference 26

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Unavailable: canonical work link unavailable.

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Observation 680ea7d2-5198-4d9c-aa1b-9eb36cd1d814 · outbound

This paper cites Self-RAG: Learning to retrieve, generate, and critique through self-reflection,.

Knowledge Augmented Finetuning Matters in both RAG and Agent Based Dialog Systems Self-RAG: Learning to retrieve, generate, and critique through self-reflection,

Reference 27

Resolution
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raw_fallback, observed 2026-08-06T22:00:02.369581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 3c8e2f0e-c677-477c-aa9a-5acea88329e4 · outbound

This paper cites Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP.

Knowledge Augmented Finetuning Matters in both RAG and Agent Based Dialog Systems Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

Reference 28

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:00:01.153882Z digest=sha256:5b6f8152c1bad620a405754bc37431b77bf443e7d4a245b415350ff7154ccff2

Observation d46e3b29-0c5a-4c28-ac31-4adaafd33904 · outbound

This paper cites BERT: Pre-training of deep bidirectional transformers for language understanding,.

Knowledge Augmented Finetuning Matters in both RAG and Agent Based Dialog Systems BERT: Pre-training of deep bidirectional transformers for language understanding,

Reference 29

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raw_fallback, observed 2026-08-06T22:00:02.237694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-06T22:00:01.294288Z digest=sha256:1dff1df6f6e353970a0b55af481ae2ea9c3f9cfe7d797050d31c236cdb38fcc6

Observation d592b990-360c-432c-ba5e-736bc6e3fd93 · outbound

This paper cites Language models are unsupervised multitask learners,.

Knowledge Augmented Finetuning Matters in both RAG and Agent Based Dialog Systems Language models are unsupervised multitask learners,

Reference 30

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raw_fallback, observed 2026-08-06T22:00:02.070769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-06T22:00:01.442311Z digest=sha256:664389957ae7d7acbf30ecd836b182624425dbf3bff63c5551d8e912942867a2

Observation c99b4d6e-7c3a-4031-8aaf-d731761e473d · outbound

This paper cites Bertscore: Evaluating text generation with bert,.

Knowledge Augmented Finetuning Matters in both RAG and Agent Based Dialog Systems Bertscore: Evaluating text generation with bert,

Reference 31

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raw_fallback, observed 2026-08-06T22:00:01.910054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Pith citing papers

No inbound Pith citation observations are available.