InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity | Paper Citation Record

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This paper cites Active Example Selection for In-Context Learning.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Active Example Selection for In-Context Learning

Reference 1

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Observation 1668909d-8d77-47fb-9d58-030150fec48d · outbound

This paper cites Self-adaptive in- context learning: An information compression perspec- tive for in-context example selection and ordering,.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Self-adaptive in- context learning: An information compression perspec- tive for in-context example selection and ordering,

Reference 2

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Observation 8ef6a47c-f885-4ca4-9faa-afdf0cf87e1f · outbound

This paper cites Finding support examples for in- context learning,.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Finding support examples for in- context learning,

Reference 3

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This paper cites An end-to-end submodular framework for data-efficient in-context learning,.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity An end-to-end submodular framework for data-efficient in-context learning,

Reference 4

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This paper cites IDEAL: Influence-Driven Selective Annotations Empower In-Context Learners in Large Language Models.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity IDEAL: Influence-Driven Selective Annotations Empower In-Context Learners in Large Language Models

Reference 5

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This paper cites Selective Annotation Makes Language Models Better Few-Shot Learners.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Selective Annotation Makes Language Models Better Few-Shot Learners

Reference 6

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Observation 92977ee6-7da1-4e81-ac03-5db86e21bba6 · outbound

This paper cites Which Examples to Annotate for In-Context Learning? Towards Effective and Efficient Selection.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Which Examples to Annotate for In-Context Learning? Towards Effective and Efficient Selection

Reference 7

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Observation 20d7cf12-2478-494d-b517-488e42cc32e6 · outbound

This paper cites Compositional exemplars for in-context learning,.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Compositional exemplars for in-context learning,

Reference 8

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This paper cites Learning To Retrieve Prompts for In-Context Learning.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Learning To Retrieve Prompts for In-Context Learning

Reference 9

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This paper cites Generalized submodular information measures: Theo- retical properties, examples, optimization algorithms, and applications,.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Generalized submodular information measures: Theo- retical properties, examples, optimization algorithms, and applications,

Reference 10

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This paper cites Prism: A rich class of parameterized submodular information measures for guided data subset selection,.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Prism: A rich class of parameterized submodular information measures for guided data subset selection,

Reference 11

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This paper cites A sequential algorithm for training text classifiers,.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity A sequential algorithm for training text classifiers,

Reference 12

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Observation 87dfeea7-c568-4bfa-a6d4-cde9d82c6932 · outbound

This paper cites An analysis of approximations for maximizing submodular set functions—i,.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity An analysis of approximations for maximizing submodular set functions—i,

Reference 13

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This paper cites Sentence-bert: Sentence embeddings using siamese bert-networks,.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Sentence-bert: Sentence embeddings using siamese bert-networks,

Reference 14

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This paper cites Submodularity beyond submodular energies: Coupling edges in graph cuts,.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Submodularity beyond submodular energies: Coupling edges in graph cuts,

Reference 15

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Observation 460682fc-20df-4c8a-afb4-a19f8d2d548b · outbound

This paper cites A class of submodular functions for document summarization,.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity A class of submodular functions for document summarization,

Reference 16

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Observation 269e6641-f8d5-4d1f-a20d-298f800ed804 · outbound

This paper cites Hotpotqa: A dataset for diverse, explain- able multi-hop question answering,.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Hotpotqa: A dataset for diverse, explain- able multi-hop question answering,

Reference 17

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This paper cites Active prompting with chain-of-thought for large language models,.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Active prompting with chain-of-thought for large language models,

Reference 18

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Observation eb0088c0-1124-40f3-8ffe-352cd27a0c1d · outbound

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

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?

Reference 19

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This paper cites Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrations.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrations

Reference 20

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This paper cites An Explanation of In-context Learning as Implicit Bayesian Inference.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity An Explanation of In-context Learning as Implicit Bayesian Inference

Reference 21

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This paper cites A Theory of Emergent In-Context Learning as Implicit Structure Induction.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity A Theory of Emergent In-Context Learning as Implicit Structure Induction

Reference 22

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This paper cites Unified Demonstration Retriever for In-Context Learning.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Unified Demonstration Retriever for In-Context Learning

Reference 23

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This paper cites Understanding in-context learning via supportive pretraining data,.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Understanding in-context learning via supportive pretraining data,

Reference 24

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This paper cites Rethinking the Role of Scale for In-Context Learning: An Interpretability-based Case Study at 66 Billion Scale.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Rethinking the Role of Scale for In-Context Learning: An Interpretability-based Case Study at 66 Billion Scale

Reference 25

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This paper cites Larger language models do in-context learning differently.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Larger language models do in-context learning differently

Reference 26

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This paper cites In-context Learning and Induction Heads.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity In-context Learning and Induction Heads

Reference 27

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This paper cites Active learning for bert: An empirical study,.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Active learning for bert: An empirical study,

Reference 28

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This paper cites Revisiting uncertainty-based query strategies for active learning with transformers,.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Revisiting uncertainty-based query strategies for active learning with transformers,

Reference 29

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InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Attention is all you need,

Reference 30

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This paper cites Transformers learn in-context by gradient descent,.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Transformers learn in-context by gradient descent,

Reference 31

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This paper cites Trained transformers learn linear models in-context,.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Trained transformers learn linear models in-context,

Reference 32

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This paper cites Don't Make Your LLM an Evaluation Benchmark Cheater.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Don't Make Your LLM an Evaluation Benchmark Cheater

Reference 33

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InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity On the Opportunities and Risks of Foundation Models

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InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity A Survey of Large Language Models

Reference 35

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This paper cites A survey on in-context learning,.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity A survey on in-context learning,

Reference 36

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InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Emergent Abilities of Large Language Models

Reference 37

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This paper cites Data Distributional Properties Drive Emergent In-Context Learning in Transformers.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Data Distributional Properties Drive Emergent In-Context Learning in Transformers

Reference 38

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InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Submodular functions and optimization,

Reference 39

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This paper cites AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning

Reference 40

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This paper cites Talisman: Targeted active learning for object detection with rare classes and slices using submodular mutual information,.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Talisman: Targeted active learning for object detection with rare classes and slices using submodular mutual information,

Reference 41

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This paper cites Efficient Data Subset Selection to Generalize Training Across Models: Transductive and Inductive Networks.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Efficient Data Subset Selection to Generalize Training Across Models: Transductive and Inductive Networks

Reference 42

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This paper cites Demystifying multi-faceted video summarization: Tradeoff between diversity, representa- tion, coverage and importance,.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Demystifying multi-faceted video summarization: Tradeoff between diversity, representa- tion, coverage and importance,

Reference 43

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Observation dbfd1520-ab32-44b6-a443-fc5d8b352252 · outbound

This paper cites How Good is a Video Summary? A New Benchmarking Dataset and Evaluation Framework Towards Realistic Video Summarization.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity How Good is a Video Summary? A New Benchmarking Dataset and Evaluation Framework Towards Realistic Video Summarization

Reference 44

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InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Lazier Than Lazy Greedy

Reference 45

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This paper cites Submodular point processes with applications to machine learning,.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Submodular point processes with applications to machine learning,

Reference 46

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This paper cites Score: Submodular combinatorial representation learn- ing,.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Score: Submodular combinatorial representation learn- ing,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:44:28.463686Z

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Observation 00471e45-39b3-4eb4-a9f1-d709d6307385 · outbound

This paper cites SMILe: Leveraging Submodular Mutual Information For Robust Few-Shot Object Detection.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity SMILe: Leveraging Submodular Mutual Information For Robust Few-Shot Object Detection

Reference 48

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Observation 9e707c8d-6034-4c8c-98b1-ef7dd332067a · outbound

This paper cites Submodular combinatorial information measures with applications in machine learning,.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Submodular combinatorial information measures with applications in machine learning,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:44:28.457534Z

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Observation 48d4babb-28a2-4708-b08e-e88f36cc4d16 · outbound

This paper cites Learning mixtures of submod- ular shells with application to document summarization,.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Learning mixtures of submod- ular shells with application to document summarization,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:44:28.450929Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d998ac54-5b07-4169-8a04-81cf5bbe5a8a · outbound

This paper cites Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity,.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:44:28.444552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1a79bc28-9dd3-4940-8780-37ac84942891 · outbound

This paper cites Squad: 100,000+ questions for machine comprehension of text,.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Squad: 100,000+ questions for machine comprehension of text,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:44:28.438063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1bfb6864-9ebc-4e94-9e72-b62926d04ec8 · outbound

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

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Training language models to follow instructions with human feedback,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:44:28.431593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0bd764ea-a659-4690-a4b8-1219688fe0a3 · outbound

This paper cites Questions are all you need to train a dense passage retriever,.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Questions are all you need to train a dense passage retriever,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:44:28.425525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2b38af5a-d35f-4370-bf47-4c236c21b27a · outbound

This paper cites Ask to Understand: Question Generation for Multi-hop Question Answering.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Ask to Understand: Question Generation for Multi-hop Question Answering

Reference 55

Resolution: verified exact; local_arxiv, observed 2026-08-05T14:44:28.185932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 296cc82c-a989-4b60-aa0e-786fbd344853 · outbound

This paper cites MuSiQue: Multihop questions via single-hop question composition,.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity MuSiQue: Multihop questions via single-hop question composition,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:44:28.419243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8c136086-56ce-4b22-88f2-d8a861ef4306 · outbound

This paper cites Reasoning over public and private data in retrieval-based systems,.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Reasoning over public and private data in retrieval-based systems,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:44:28.413127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6506c3a1-9899-4c45-b7c1-d1410cd309e4 · outbound

This paper cites Exploiting se- mantic annotations and q-learning for constructing an ef- ficient hierarchy/graph texts organization,.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Exploiting se- mantic annotations and q-learning for constructing an ef- ficient hierarchy/graph texts organization,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:44:28.406590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 11162e27-1234-4aed-bd37-b47a3dbe6422 · outbound

This paper cites The pas- cal recognising textual entailment challenge,.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity The pas- cal recognising textual entailment challenge,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:44:28.400283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation fc6af1ad-5983-483b-8775-436109a8f5ab · outbound

This paper cites A broad- coverage challenge corpus for sentence understanding through inference,.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity A broad- coverage challenge corpus for sentence understanding through inference,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:44:28.394195Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8fd5ec50-2db6-4a89-a028-92987e20eeb1 · outbound

This paper cites Recursive deep models for semantic compositionality over a sentiment treebank,.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Recursive deep models for semantic compositionality over a sentiment treebank,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:44:28.387722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a62d7d0b-06fa-44c8-b42e-3336b7eddc9a · outbound

This paper cites Dbpedia - a large-scale, mul- tilingual knowledge base extracted from wikipedia,.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Dbpedia - a large-scale, mul- tilingual knowledge base extracted from wikipedia,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:44:28.380387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e202a91d-98b2-4c38-871a-584efeaa8992 · outbound

This paper cites Hellaswag: Can a machine really finish your sentence?.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Hellaswag: Can a machine really finish your sentence?

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:44:28.373640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2e281076-237a-41fa-9bc3-a54bb09e8b0b · outbound

This paper cites Multiwoz - a large-scale multi- domain wizard-of-oz dataset for task-oriented dialogue modelling,.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Multiwoz - a large-scale multi- domain wizard-of-oz dataset for task-oriented dialogue modelling,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:44:28.366692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c784760a-bedb-4464-ab55-b2fd9543e1fb · outbound

This paper cites Learning to parse database queries using inductive logic programming,.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Learning to parse database queries using inductive logic programming,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:44:28.360265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8de39ddc-7b08-4b66-8d0e-569d7d813d11 · outbound

This paper cites Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-05T14:44:28.124349Z

Source-reported events for the cited work

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Observation 9cd58d69-3da0-4b19-8f2d-ad6174d0d6f7 · outbound

This paper cites MetaICL: Learning to Learn In Context.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity MetaICL: Learning to Learn In Context

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-05T14:44:28.126452Z

Source-reported events for the cited work

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Observation 24c85cbb-a51e-4395-b667-fbaa7183c1b4 · outbound

This paper cites Gemma: Open Models Based on Gemini Research and Technology.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Gemma: Open Models Based on Gemini Research and Technology

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-05T14:44:28.128451Z

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Observation 7225a406-f69d-4e2a-9bef-954290cd76f5 · outbound

This paper cites Language Models are Few-Shot Learners.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Language Models are Few-Shot Learners

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-05T14:44:28.130412Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13ccdc09-7dc7-477d-97d8-73b7f5567c1c · outbound

This paper cites Decoupled weight decay regularization,.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Decoupled weight decay regularization,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:44:28.353786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f72a705e-aa49-46da-b0e8-218b064568ed · outbound

This paper cites MPNet: Masked and Permuted Pre-training for Language Understanding.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity MPNet: Masked and Permuted Pre-training for Language Understanding

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-05T14:44:28.134281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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