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Paper Citation Record · LEDGER

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning

As of 18 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2509.05635.

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

pith.paper-citation-record.v1
2509.05635 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T05:21:30.874144Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

46 of 46 outbound references displayed

  • verified exact1
  • verified fuzzy29
  • unresolved16
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 376d6c5c-c377-419e-a798-53f7601b4df3 · outbound

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

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning Bert: Pre-training of deep bidirectional transformers for language understanding,

Reference 1

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Source-reported events for the cited work

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Observation 3108767f-0e74-45f2-b824-d958d8494913 · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer,.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning Exploring the limits of transfer learning with a unified text-to-text transformer,

Reference 2

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 8e1faad9-cc1d-4e85-9cb5-59df1ca41f95 · outbound

This paper cites Language Models are Few-Shot Learners.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning Language Models are Few-Shot Learners

Reference 3

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Unavailable: canonical work link unavailable.

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Observation 1545d2f7-2923-4b3e-b23b-57c42c9a1da9 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning LLaMA: Open and Efficient Foundation Language Models

Reference 4

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Unavailable: canonical work link unavailable.

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Observation d0bdbdfb-f6f5-4aef-9ece-0650e455d83d · outbound

This paper cites GPT-4 Technical Report.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning GPT-4 Technical Report

Reference 5

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Observation 511a0996-a6e3-440f-90b6-f22ad4f343f1 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning Gemini: A Family of Highly Capable Multimodal Models

Reference 6

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Unavailable: canonical work link unavailable.

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Observation f4be014b-3659-49d6-bd04-4899e894c0a7 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 7

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Observation f9eab0c8-9c59-4acf-adcb-ad9d3001b822 · outbound

This paper cites A New Dialogue Response Generation Agent for Large Language Models by Asking Questions to Detect User's Intentions.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning A New Dialogue Response Generation Agent for Large Language Models by Asking Questions to Detect User's Intentions

Reference 8

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Observation 2e91f4eb-0649-489c-90ea-612601b20981 · outbound

This paper cites Tell Me More! Towards Implicit User Intention Understanding of Language Model Driven Agents.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning Tell Me More! Towards Implicit User Intention Understanding of Language Model Driven Agents

Reference 9

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Observation db7198bf-5fc7-4981-bee5-c4dc28eab259 · outbound

This paper cites Effectiveness of pre-training for few-shot intent classi- fication,.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning Effectiveness of pre-training for few-shot intent classi- fication,

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 664e54d1-0ff7-44a1-a61c-5595569b4868 · outbound

This paper cites Cluster & tune: Boost cold start performance in text classification,.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning Cluster & tune: Boost cold start performance in text classification,

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 7d1f689e-6ede-429d-a997-a26cca65aa74 · outbound

This paper cites Fine-tuning pre-trained language models for few-shot intent detection: Supervised pre-training and isotropization,.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning Fine-tuning pre-trained language models for few-shot intent detection: Supervised pre-training and isotropization,

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 2fd4b76c-8790-45a0-8f76-597f8d13c5cd · outbound

This paper cites Few-shot intent detection via con- trastive pre-training and fine-tuning,.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning Few-shot intent detection via con- trastive pre-training and fine-tuning,

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 9de25778-d06e-43ab-8bb9-0f6b3777e71f · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 14

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source=pdf_text observed=2026-08-05T05:21:30.772020Z digest=sha256:1a4a5ef58b107c8d69101aa8e423c0887b75b4e5f5d40d85c1c41ebd95bcade2

Observation 27fa73d8-23d6-4dda-9554-6c078801a4cd · outbound

This paper cites Exploring zero and few-shot techniques for intent classification,.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning Exploring zero and few-shot techniques for intent classification,

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 617b8648-e964-4618-a1a0-2f03f543fd64 · outbound

This paper cites Simcse: Simple contrastive learn- ing of sentence embeddings,.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning Simcse: Simple contrastive learn- ing of sentence embeddings,

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 01ac8e01-ced1-4ba7-a648-f98430f027d7 · outbound

This paper cites Efficient intent detection with dual sentence encoders,.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning Efficient intent detection with dual sentence encoders,

Reference 17

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation c4f217fe-dce6-4683-b842-8195a4180af4 · outbound

This paper cites DialoGLUE: A Natural Language Understanding Benchmark for Task-Oriented Dialogue.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning DialoGLUE: A Natural Language Understanding Benchmark for Task-Oriented Dialogue

Reference 18

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 1edcddeb-0703-4192-ad10-45c28a7e67f1 · outbound

This paper cites Revisit few-shot intent classification with plms: Direct fine-tuning vs. con- tinual pre-training,.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning Revisit few-shot intent classification with plms: Direct fine-tuning vs. con- tinual pre-training,

Reference 19

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 0828e531-9564-434f-acb5-07d356aa80d7 · outbound

This paper cites Region embedding with intra and inter-view contrastive learning,.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning Region embedding with intra and inter-view contrastive learning,

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation c7552dbc-5f87-430d-8b63-edf551902f2b · outbound

This paper cites A simple framework for contrastive learning of visual representations,.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning A simple framework for contrastive learning of visual representations,

Reference 21

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Unavailable: canonical work link unavailable.

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Observation a87b94a0-45a9-42e8-9033-bfd45d80dc91 · outbound

This paper cites Discriminative nearest neighbor few- shot intent detection by transferring natural language inference,.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning Discriminative nearest neighbor few- shot intent detection by transferring natural language inference,

Reference 22

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 9e8fe313-26a4-4995-8821-a16ec70c4aaa · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer,.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning Exploring the limits of transfer learning with a unified text-to-text transformer,

Reference 23

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation f4dc5e7c-5b1e-4636-86c7-886552f8222c · outbound

This paper cites Language models are unsupervised multitask learners,.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning Language models are unsupervised multitask learners,

Reference 24

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Observation 658cc680-5028-440f-a4a6-4da2a369b60b · outbound

This paper cites Commonsense knowl- edge mining from pretrained models,.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning Commonsense knowl- edge mining from pretrained models,

Reference 25

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation fc22915c-6166-405b-93a5-d9dc640c7324 · outbound

This paper cites Ppt: Pre-trained prompt tuning for few-shot learning,.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning Ppt: Pre-trained prompt tuning for few-shot learning,

Reference 26

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 385923f0-6d05-4aa6-a0a9-75e7b6eec8eb · outbound

This paper cites Learning to compose soft prompts for compositional zero-shot learning,.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning Learning to compose soft prompts for compositional zero-shot learning,

Reference 27

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 480045ef-4191-4d3a-b88f-72228ddcb600 · outbound

This paper cites Exploiting cloze-questions for few-shot text classification and natural language inference,.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning Exploiting cloze-questions for few-shot text classification and natural language inference,

Reference 28

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 84e35ff8-24d7-4c00-955c-0779b7142b96 · outbound

This paper cites The power of scale for parameter-efficient prompt tuning,.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning The power of scale for parameter-efficient prompt tuning,

Reference 29

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 00cc17d8-0507-4fe7-903b-f210dfb6ef89 · outbound

This paper cites P-tuning: Prompt tuning can be comparable to fine-tuning across scales and tasks,.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning P-tuning: Prompt tuning can be comparable to fine-tuning across scales and tasks,

Reference 30

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 1cf6f25b-336e-4aed-be0b-4f25d2bc6365 · outbound

This paper cites Pre- train, prompt, and predict: A systematic survey of prompting methods in natural language processing,.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning Pre- train, prompt, and predict: A systematic survey of prompting methods in natural language processing,

Reference 31

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation a762b94b-84dc-4f9e-9dbd-63e97f01e7ca · outbound

This paper cites A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications

Reference 32

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Unavailable: canonical work link unavailable.

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Observation c79543d6-17e6-43c7-9407-fed1e11f6866 · outbound

This paper cites Visual prompt tuning,.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning Visual prompt tuning,

Reference 33

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Observation 1128d73f-a5d9-4deb-8e6a-35935c20d013 · outbound

This paper cites Exploring Visual Prompts for Adapting Large-Scale Models.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning Exploring Visual Prompts for Adapting Large-Scale Models

Reference 34

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T05:21:30.834999Z digest=sha256:c05f81a0455b7024decedc7dcbc66dc295955d3020e396891a27e18f9815e8be

Observation 4f872833-15a0-4699-a9a4-bcf81c02afca · outbound

This paper cites Graphprompt: Unifying pre-training and downstream tasks for graph neural networks,.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning Graphprompt: Unifying pre-training and downstream tasks for graph neural networks,

Reference 35

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raw_fallback, observed 2026-08-05T05:21:31.102742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T05:21:30.838121Z digest=sha256:37461b47be0d21d7fa50ef9ea06b33be668438cb9d407c0d65bb929d6c7f5a82

Observation 6926a400-ee8f-42df-9623-0ec5069462d4 · outbound

This paper cites Gppt: Graph pre- training and prompt tuning to generalize graph neural networks,.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning Gppt: Graph pre- training and prompt tuning to generalize graph neural networks,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T05:21:31.092794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T05:21:30.841149Z digest=sha256:b33810bacee74539a19f2dce0137f3f0b950d01df5efe34687143a587cedd870

Observation 9a1882fd-ccfd-470e-adfb-d4c5a53d97f6 · outbound

This paper cites Convert: Efficient and accurate conversational rep- resentations from transformers,.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning Convert: Efficient and accurate conversational rep- resentations from transformers,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T05:21:31.081986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T05:21:30.844686Z digest=sha256:70ed24c50ce6df588115de19bb33e1290e92db1c329911314f8762364c92e037

Observation dcc616ef-4d09-4a2f-a93a-73acbe110aa3 · outbound

This paper cites Dialogpt: Large-scale generative pre- training for conversational response generation,.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning Dialogpt: Large-scale generative pre- training for conversational response generation,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T05:21:31.071984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T05:21:30.847533Z digest=sha256:c954d1bd51952f10c7defcba0779fc782e3225f39af6741b531cd4aed056eefa

Observation a8e82fc1-c7cf-4bf8-b171-68b9805d23ee · outbound

This paper cites Tod-bert: Pre-trained natural language understanding for task-oriented dialogue,.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning Tod-bert: Pre-trained natural language understanding for task-oriented dialogue,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T05:21:31.062170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T05:21:30.851085Z digest=sha256:ba6875954fef4b607f69ee73623884aec1bb21a5cef4db8ae6ab751728e38de4

Observation f215ed98-43b4-47bf-80b5-1a9da9a9663b · outbound

This paper cites Wildchat: 1m chatgpt interaction logs in the wild,.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning Wildchat: 1m chatgpt interaction logs in the wild,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T05:21:31.052320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T05:21:30.854263Z digest=sha256:f201f520906814ffadf69703ae8c1efd71979f693447931913056c9422e0586e

Observation f4b4bf6d-3d3e-4119-afda-63f00db38a32 · outbound

This paper cites Learn to adapt for generalized zero-shot text classification,.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning Learn to adapt for generalized zero-shot text classification,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T05:21:31.042509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T05:21:30.857382Z digest=sha256:8a03d9f5b12f7e074c5c0c6a95df13167aaa98dd7e02ba22f7600155b1d1823b

Observation bbbe839c-a6cb-45ae-af72-a93f273671ff · outbound

This paper cites DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-05T05:21:30.860374Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T05:21:30.860374Z digest=sha256:2d189f586aacedd9ee9782495adc8b9b60428676c82595ce1253c05be4ba9727

Observation 5106fd86-f8ee-4604-b633-57335602653b · outbound

This paper cites ALBERT: A Lite BERT for Self-supervised Learning of Language Representations.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-05T05:21:30.864248Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T05:21:30.864248Z digest=sha256:a82a930139e2ca9d1c7da17c403a1ed34e6f8294aae6e45a5d791aa900b5aac8

Observation a0c62731-83bc-4780-8e52-00421f36e5f0 · outbound

This paper cites Efficient Few-Shot Learning Without Prompts.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning Efficient Few-Shot Learning Without Prompts

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-05T05:21:30.867771Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T05:21:30.867771Z digest=sha256:e1d36ff924f2b41db5f3481def85f8c2a6899ef5c5a097bd2361f180becb5a44

Observation 51b72bf8-f4fd-4426-a985-27ac8d1ed1e9 · outbound

This paper cites Beyond similarity: Relation-based collaborative filtering,.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning Beyond similarity: Relation-based collaborative filtering,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T05:21:31.033175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T05:21:30.871026Z digest=sha256:6a7ef1c5c95ed4f65a605c35b1143414615387ba86807f20d492d691089dfd21

Observation 609b8f48-01bf-4006-bb79-7ec558de38cd · outbound

This paper cites Visualizing data using t-sne,.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning Visualizing data using t-sne,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T05:21:31.022383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T05:21:30.874144Z digest=sha256:060408d79721933c8cb34d5d7dc95caaee9cdebd7e4000c1538aa8d7c139b481

Pith citing papers

No inbound Pith citation observations are available.