Pith. sign in

Paper Citation Record · LEDGER

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

As of 7 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 2026-08-07T06:34:17.273281+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
  • metadata mismatch0

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T05:21:31.323420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T05:21:30.725300Z digest=sha256:9545fc545a4a24fa6340d66ab007e7ea83e647b993f9eb15a9aece988babd999

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T05:21:31.313370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T05:21:30.728539Z digest=sha256:7c5e4356bac2082f781b23b4d5d125a2371b665c5019f2c3950889974349222b

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

Resolution
unresolved
no resolver link, observed 2026-08-05T05:21:30.732046Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T05:21:30.732046Z digest=sha256:d2a26b788934f5fb7155c5bb09859206c2d18d8ec425be966a4676df4c9216d6

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

Resolution
unresolved
no resolver link, observed 2026-08-05T05:21:30.736252Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T05:21:30.736252Z digest=sha256:0ab2a6bb3046a361da6239e99cffe02b6b87e283e240550f165006ed84d7cbcd

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

Resolution
unresolved
no resolver link, observed 2026-08-05T05:21:30.740381Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T05:21:30.740381Z digest=sha256:f10979c007f9ab5290ababfac86dbdf63209df86b4bc10b1bca8d959c4431a67

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

Resolution
unresolved
no resolver link, observed 2026-08-05T05:21:30.743754Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T05:21:30.743754Z digest=sha256:6a01c0f2fad203ac426722079ead69f4fe3ce1e242712c88e8430fa84d1958c9

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

Resolution
unresolved
no resolver link, observed 2026-08-05T05:21:30.747915Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T05:21:30.747915Z digest=sha256:850a5852405de82adc653d3e4b5e65fb629d45f8897a2edba56aa0223804f986

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

Resolution
unresolved
no resolver link, observed 2026-08-05T05:21:30.751235Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T05:21:30.751235Z digest=sha256:c7e782584d53be7e7b91ec123917878a94b4a2eb5896ef8abdedacef2287a6aa

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

Resolution
unresolved
no resolver link, observed 2026-08-05T05:21:30.754777Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T05:21:30.754777Z digest=sha256:695c413c64576d5c109101bef8adb372d9fb1844607f4c3b10da6776d3167aa3

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T05:21:31.303675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T05:21:30.758975Z digest=sha256:542d91c28a76848b13d5e03443b98f5dfbeb89524fe8d3d858aaa72251bb422e

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T05:21:31.293813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T05:21:30.762016Z digest=sha256:5b28b0f06e3c6bdd64a78589ac76fe8cc26b6e28e85739268d57f9d11716dd15

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T05:21:31.283983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T05:21:30.765402Z digest=sha256:709504287a04a821b80dda75517ab421005f0bf4ba30c8d881b7408d253653f8

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T05:21:31.272850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T05:21:30.768994Z digest=sha256:08fbe967d551f75a0104cde7468ce01bc014daba1044be8ba96818f07fb6f512

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

Resolution
unresolved
no resolver link, observed 2026-08-05T05:21:30.772020Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T05:21:30.772020Z digest=sha256:41472ccec7813203838a460111f25f8fe98c3dc6dbf0722a8bc15c602cfc4079

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T05:21:31.261920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T05:21:30.775834Z digest=sha256:c97c65e3a7844ddb3ff89249d4175e2f54b31acf6b0e58e63a4d7e58973287eb

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T05:21:31.252003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T05:21:30.778759Z digest=sha256:e305a3d627d9453181ca26a961f07dae57ee4cb18e11367be534a5271dabc887

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T05:21:31.241020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T05:21:30.781721Z digest=sha256:c0042fcb14659cb6181e3cf0846ffd6d85b9335af6dafbbceb9a4fb42a656755

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

Resolution
verified exact
local_arxiv, observed 2026-08-05T05:21:30.946197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T05:21:30.784596Z digest=sha256:bbb2077965f77dd70a0ec0c3c1b7c050f0b3d5ba0dd8e5fb26c2f909197c9b5e

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T05:21:31.231158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T05:21:30.787880Z digest=sha256:9ce5de7dc1d94801e95d81bbc54588c353625a4602a3fde68c882cd1f39f36fe

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T05:21:31.221064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T05:21:30.791024Z digest=sha256:7ee626efac8056a16011cc8b98018a9c0d6eb326185153394128205189b581a6

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

Resolution
unresolved
no resolver link, observed 2026-08-05T05:21:30.794028Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T05:21:30.794028Z digest=sha256:ea54f21f8ef14276ed6a2ad87716d89407b197de971e8b1967958984005e5ef0

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T05:21:31.205997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T05:21:30.796992Z digest=sha256:2fe992e40836df9737504ecef79face2ee1279650d9aa5403cd83de41c26bea9

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T05:21:31.196047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T05:21:30.799904Z digest=sha256:b5b2e74363d5348971131144899cf16c15d4121e1ba4dc6f97fb2678e4f27f56

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

Resolution
unresolved
no resolver link, observed 2026-08-05T05:21:30.803259Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T05:21:30.803259Z digest=sha256:ed06de11bf591a3e348fccd4e6aba8559cf1f2a380b3c84abbbff5741559d671

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T05:21:31.180192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T05:21:30.806076Z digest=sha256:6fbb8e13b326f7cd2d9291bbaae92069d4aa64ac93f76390672f7c00be532a6f

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T05:21:31.169721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T05:21:30.809039Z digest=sha256:0936a60bcff65df5904b25ce95df8ad3ca021b09633cd5335835612925719766

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T05:21:31.159822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T05:21:30.812493Z digest=sha256:8d65e896c3efe128cbe3b71227451a003c9eae455dae7bc712cca0839ef901de

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T05:21:31.150190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T05:21:30.816676Z digest=sha256:654c86474e717209e06831496d433de47b6ee057e10cc593a697c6713929ad0c

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T05:21:31.140244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T05:21:30.819583Z digest=sha256:cb51e859892328ca67e1598f8ea4af88698bcf5c8f51830acfeb2e66560443aa

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T05:21:31.130141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T05:21:30.822609Z digest=sha256:410a0d1bcf1c98e89e38612ed3636acf37c7c20c6587a5366c794cb32584aee0

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T05:21:31.118879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T05:21:30.825572Z digest=sha256:eb84aefbcb51f097401e0f3a4c314b753835e43b3e62b5577fed8abecded9b40

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

Resolution
unresolved
no resolver link, observed 2026-08-05T05:21:30.828689Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T05:21:30.828689Z digest=sha256:8fd515a7239fc2aadaddcdd59923bfd7c336b8933dee465712129ff0f7aaa47e

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

Resolution
unresolved
no resolver link, observed 2026-08-05T05:21:30.832014Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T05:21:30.832014Z digest=sha256:866844edcad3c5fdb70e9794ac0ac4cb57ddda6ae28a670f4e71c029fe0bd3de

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

Resolution
unresolved
no resolver link, observed 2026-08-05T05:21:30.834999Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T05:21:30.834999Z digest=sha256:454ae54392ad42593854359cb50175832c56b04f06138b7656d5ab4f63cbf045

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

Resolution
verified fuzzy
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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T05:21:30.838121Z digest=sha256:47a97265b6ddb56afeb662e1c131e269f6bf28915dd4a0b2340cf382429dc26a

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T05:21:30.841149Z digest=sha256:979fd7cd07b5a1ca48c36375146c194894475139e53565735840de76f1d4e429

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T05:21:30.844686Z digest=sha256:dfd522b327b7ccdbc1b48f50fa1bed312a6ee0254689847166b0449268308eea

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T05:21:30.847533Z digest=sha256:8b6ea73a74efe5dd58863b22530153014210de573385693ec6029e4908af2516

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T05:21:30.851085Z digest=sha256:f953ce029d5b98b2be9b861026b96b430ed1924b60d36dec11ed4948fdb0b72e

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T05:21:30.854263Z digest=sha256:a11033b5cb1a8bade3ab5a4d42bf93bbd95b3a1967540f33575a7e4db65e79da

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T05:21:30.857382Z digest=sha256:d83ef9b43252cdee20ab32d803c13c80f9fc1480399a413250ebfc321f96ae9d

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=sha256:940bd17a40bb22fd704fb4f278947db95bb6879a473471a1e81569fa269eb66e

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=sha256:74b760ad8efb6c583c0d6aade63c1e36953c4e1f1e47a3ad9cb033a20e26bc5b

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:ebc9fcfceb06492a635d39aaba79c4332f8e187ebae5109ec35855231303ca1b

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T05:21:30.871026Z digest=sha256:861cbc016de5b0ba43df0f22773c8bbb17c987eac31a7e4968ecde6013afbced

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T05:21:30.874144Z digest=sha256:002c65df7fba0f240f2a6733dfea6bebbab4d80502c6721014eda7d2216baa03

Pith citing papers

No inbound Pith citation observations are available.