Paper Citation Record · LEDGER

Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning

As of 24 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 1 inbound Pith citation observation for arXiv:2506.03035.

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

pith.paper-citation-record.v1
2506.03035 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:14:14.167277Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:14:10.534131Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-07T11:14:14.593411Z

Reference resolution

37 of 37 outbound references displayed

verified exact2
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 88d1dd60-ae83-48c0-b14c-949549a0bbec · outbound

This paper cites What is the weather like in Abu Dhabi tomorrow?.

Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning What is the weather like in Abu Dhabi tomorrow?

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:21.217153Z

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Observation 968c0b69-3cfb-4753-9aed-4bf8a3f8bd0e · outbound

This paper cites Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning.

Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning

Reference 2

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Observation 116aa7a7-19d3-4ff8-89f8-155e299c792b · outbound

This paper cites We present the datasets used, describe the chosen language model, detail the retrieval mechanisms em- ployed, and specify the baseline and comparison methods.

Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning We present the datasets used, describe the chosen language model, detail the retrieval mechanisms em- ployed, and specify the baseline and comparison methods

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:21.014731Z

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Observation 3e4de186-bba4-4616-90aa-66beeb74b463 · outbound

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Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning Unresolved cited work

Reference 4

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Observation 848670ac-c427-4a97-acde-b55c04545977 · outbound

This paper cites By integrating IR methods, specifically BM25, into the ex- ample selection process, we have addressed the challenges of overlapping intents and slots in complex datasets.

Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning By integrating IR methods, specifically BM25, into the ex- ample selection process, we have addressed the challenges of overlapping intents and slots in complex datasets

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:20.363318Z

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Observation 7f716832-929f-4f2b-9352-f638bdd549b6 · outbound

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Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning Unresolved cited work

Reference 6

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Observation 6d8a5f13-d6eb-46a5-b2fd-5d8e605c18f9 · outbound

This paper cites This work was performed using HPC resources from GENCI-IDRIS (Grant 2023-AD011014242).

Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning This work was performed using HPC resources from GENCI-IDRIS (Grant 2023-AD011014242)

Reference 7

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Observation b65fd369-32db-4ce6-8476-884585aea7ea · outbound

This paper cites End-to-end sequence labeling via bi- directional LSTM-CNNs-CRF,.

Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning End-to-end sequence labeling via bi- directional LSTM-CNNs-CRF,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:17.597057Z

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Observation 240b04b2-9fa3-4a35-8fb9-c594c240ab5e · outbound

This paper cites Tur and R.

Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning Tur and R

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:19.680280Z

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Observation 2b1f210e-931d-476a-aba9-f955a85af141 · outbound

This paper cites Results of the French Evalda-Media evaluation campaign for literal understanding,.

Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning Results of the French Evalda-Media evaluation campaign for literal understanding,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:19.468867Z

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Observation e248ef04-7d45-400f-a94a-ff4e6365ca30 · outbound

This paper cites The ATIS spoken language systems pilot corpus,.

Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning The ATIS spoken language systems pilot corpus,

Reference 11

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Observation 05f5bff6-1959-4e02-b793-0a13e0106704 · outbound

This paper cites Semantic annotation of the French media dialog cor- pus,.

Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning Semantic annotation of the French media dialog cor- pus,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:18.882088Z

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Observation 04ce3793-3725-4a4f-a7c8-c286b00f06b2 · outbound

This paper cites SLURP: A spoken language understanding resource package,.

Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning SLURP: A spoken language understanding resource package,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:18.611170Z

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Observation d37c07ee-7923-4218-b650-e4bd7c0275af · outbound

This paper cites Conceptual decoding from word lattices: application to the spoken dialogue corpus MEDIA,.

Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning Conceptual decoding from word lattices: application to the spoken dialogue corpus MEDIA,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:18.292356Z

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Observation 999d8e06-4e47-4719-a0ee-a30b12a07fde · outbound

This paper cites Compar- ing stochastic approaches to spoken language understanding in multiple languages,.

Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning Compar- ing stochastic approaches to spoken language understanding in multiple languages,

Reference 15

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Observation ffbdaba6-b47e-430f-a5d1-09c5bc11f7e7 · outbound

This paper cites BM25→Intent.

Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning BM25→Intent

Reference 16

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Observation 98ce09ea-f087-4041-a3cc-a7525dd57f52 · outbound

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

Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning BERT: Pre-training of deep bidirectional transformers for language understanding,

Reference 17

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Observation 3e9d336f-a2df-4fbc-b6a2-b9835e69a75c · outbound

This paper cites Neural Networks ap- proaches focused on French Spoken Language Understanding: application to the MEDIA Evaluation Task,.

Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning Neural Networks ap- proaches focused on French Spoken Language Understanding: application to the MEDIA Evaluation Task,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:17.149567Z

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Observation 812968e3-f9ec-43bc-a94c-77d307f0be82 · outbound

This paper cites Multi-lingual Intent Detection and Slot Filling in a Joint BERT-based Model.

Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning Multi-lingual Intent Detection and Slot Filling in a Joint BERT-based Model

Reference 19

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Observation d8093b08-b936-4639-820e-fb359ba8267d · outbound

This paper cites BERT for Joint Intent Classification and Slot Filling.

Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning BERT for Joint Intent Classification and Slot Filling

Reference 20

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Observation 9864bb12-ad59-4ef3-8b01-cb749d587751 · outbound

This paper cites A joint learning framework with bert for spoken language understanding,.

Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning A joint learning framework with bert for spoken language understanding,

Reference 21

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Observation 0dfe7e5a-c73f-4978-a919-b38c7d3a2c96 · outbound

This paper cites A Survey of Joint Intent Detection and Slot Filling Models in Natural Lan- guage Understanding,.

Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning A Survey of Joint Intent Detection and Slot Filling Models in Natural Lan- guage Understanding,

Reference 22

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Observation 26339e1d-7052-45e1-8cd6-ec4b83b6e409 · outbound

This paper cites Can ChatGPT Detect Intent? Evaluating Large Language Models for Spoken Language Understanding.

Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning Can ChatGPT Detect Intent? Evaluating Large Language Models for Spoken Language Understanding

Reference 23

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Observation dfc8c79b-022d-4f82-b71b-49201c34ef79 · outbound

This paper cites Illuminer: Instruction-tuned large language models as few-shot intent clas- sifier and slot filler,.

Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning Illuminer: Instruction-tuned large language models as few-shot intent clas- sifier and slot filler,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:16.653352Z

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Observation 28539e3d-0ffd-449b-aa30-20e397fa33e2 · outbound

This paper cites Zero-shot spoken language understanding via large language models: A preliminary study,.

Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning Zero-shot spoken language understanding via large language models: A preliminary study,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:16.478420Z

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Observation 05318384-7feb-4c8a-b2c9-5ad6a5b92498 · outbound

This paper cites Croprompt: Cross-task interactive prompting for zero- shot spoken language understanding,.

Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning Croprompt: Cross-task interactive prompting for zero- shot spoken language understanding,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:16.350182Z

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Observation 6b2c57db-c48c-45ec-9790-f78d4a55bcb8 · outbound

This paper cites Retrieval-based prompt selection for code-related few-shot learning,.

Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning Retrieval-based prompt selection for code-related few-shot learning,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:16.217771Z

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Observation 2d281525-87c0-4ed2-9f38-0a7181060661 · outbound

This paper cites The probabilistic relevance framework: Bm25 and beyond,.

Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning The probabilistic relevance framework: Bm25 and beyond,

Reference 28

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Observation f04f381d-1837-4b96-9822-29e954c79ac7 · outbound

This paper cites Colbert: Efficient and effective pas- sage search via contextualized late interaction over BERT,.

Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning Colbert: Efficient and effective pas- sage search via contextualized late interaction over BERT,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:15.890435Z

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Observation 3c44ea08-1e89-4341-82c4-456d2f0800e5 · outbound

This paper cites Colbertv2: Effective and efficient retrieval via lightweight late interaction,.

Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning Colbertv2: Effective and efficient retrieval via lightweight late interaction,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:15.775976Z

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Observation b131a53d-afc1-4b9b-baa5-0bf163ad20f4 · outbound

This paper cites Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces.

Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces

Reference 31

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This paper cites New Semantic Task for the French Spoken Language Under- standing MEDIA Benchmark,.

Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning New Semantic Task for the French Spoken Language Under- standing MEDIA Benchmark,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:15.622687Z

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Observation 568d5da3-276f-48a5-9730-967ebc46d792 · outbound

This paper cites The spoken language un- derstanding MEDIA benchmark dataset in the era of deep learn- ing: data updates, training and evaluation tools,.

Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning The spoken language un- derstanding MEDIA benchmark dataset in the era of deep learn- ing: data updates, training and evaluation tools,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:15.504641Z

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Observation bda1d41c-9884-4177-b024-3a45497162dc · outbound

This paper cites The llama 3 herd of models,.

Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning The llama 3 herd of models,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:15.348820Z

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Observation c7178bfc-1355-4303-b3bf-fa636c2de7e2 · outbound

This paper cites Hermes 3 technical report,.

Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning Hermes 3 technical report,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:15.188940Z

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Observation 06f79cf3-c007-429a-8ae4-f0b2df730544 · outbound

This paper cites Towards joint intent detection and slot filling via higher-order attention,.

Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning Towards joint intent detection and slot filling via higher-order attention,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:15.023831Z

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Observation ac3645ac-4fcd-4a25-a45c-490a0f994228 · outbound

This paper cites Hierarchical multi-task natural language understanding for cross-domain conversational AI: HERMIT NLU,.

Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning Hierarchical multi-task natural language understanding for cross-domain conversational AI: HERMIT NLU,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:14.848265Z

Source-reported events for the cited work

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Pith citing papers

Observation 968c0b69-3cfb-4753-9aed-4bf8a3f8bd0e · inbound