Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations | Paper Citation Record

Observation 6bb77809-1a62-4e62-ba54-7d5a9a071925 · outbound

This paper cites GPT-4 Technical Report.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations GPT-4 Technical Report

Reference 1

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This paper cites The Llama 3 Herd of Models.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations The Llama 3 Herd of Models

Reference 2

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

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 3

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This paper cites A primer in BERTology: What we know about how BERT works,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations A primer in BERTology: What we know about how BERT works,

Reference 4

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This paper cites Language models as knowledge bases?.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Language models as knowledge bases?

Reference 5

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This paper cites Do PLMs know and understand ontological knowledge?.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Do PLMs know and understand ontological knowledge?

Reference 6

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This paper cites Attention can reflect syntactic structure (if you let it),.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Attention can reflect syntactic structure (if you let it),

Reference 7

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This paper cites Do transformer models show similar attention patterns to task-specific human gaze?.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Do transformer models show similar attention patterns to task-specific human gaze?

Reference 8

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Observation 8b40d404-43f8-4dfb-bcd4-20a96065dc22 · outbound

This paper cites Mlps compass: What is learned when mlps are combined with plms?.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Mlps compass: What is learned when mlps are combined with plms?

Reference 9

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Observation a0f0a437-da95-48c1-82b2-08c95a487fbc · outbound

This paper cites What does BERT learn about the structure of language?.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations What does BERT learn about the structure of language?

Reference 10

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This paper cites Acceptability judgements via examining the topology of attention maps,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Acceptability judgements via examining the topology of attention maps,

Reference 11

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Observation 6d3ba3b2-9480-4e33-905a-84bb24ba1768 · outbound

This paper cites How well do text embedding models understand syntax?.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations How well do text embedding models understand syntax?

Reference 12

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This paper cites Do LLMs learn a true syntactic uni- versal?.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Do LLMs learn a true syntactic uni- versal?

Reference 13

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This paper cites Investigating entity knowledge in BERT with simple neural end-to-end entity linking,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Investigating entity knowledge in BERT with simple neural end-to-end entity linking,

Reference 14

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This paper cites What’s in a name? are BERT named entity representations just as good for any other name?.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations What’s in a name? are BERT named entity representations just as good for any other name?

Reference 15

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This paper cites Reassessing semantic knowledge encoded in large language models through the word-in-context task,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Reassessing semantic knowledge encoded in large language models through the word-in-context task,

Reference 16

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This paper cites Evaluating llms’ capability to identify lexical semantic equiv- alence: Probing with the word-in-context task,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Evaluating llms’ capability to identify lexical semantic equiv- alence: Probing with the word-in-context task,

Reference 17

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This paper cites How much knowledge can you pack into the parameters of a language model?.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations How much knowledge can you pack into the parameters of a language model?

Reference 18

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This paper cites MuLan: A study of fact mutability in language models,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations MuLan: A study of fact mutability in language models,

Reference 19

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This paper cites Does Mapo Tofu Contain Coffee? Probing LLMs for Food-related Cultural Knowledge.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Does Mapo Tofu Contain Coffee? Probing LLMs for Food-related Cultural Knowledge

Reference 20

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This paper cites BERT rediscovers the classical NLP pipeline,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations BERT rediscovers the classical NLP pipeline,

Reference 21

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This paper cites Graph convolutional encoders for syntax-aware neural machine translation,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Graph convolutional encoders for syntax-aware neural machine translation,

Reference 22

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This paper cites Dsisa: A new neural machine translation combining dependency weight and neighbors,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Dsisa: A new neural machine translation combining dependency weight and neighbors,

Reference 23

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This paper cites Structured neural summarization,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Structured neural summarization,

Reference 24

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This paper cites Cross-document distillation via graph-based summarization of extracted essential knowl- edge,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Cross-document distillation via graph-based summarization of extracted essential knowl- edge,

Reference 25

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This paper cites A weighted gcn with logical adjacency matrix for relation extraction,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations A weighted gcn with logical adjacency matrix for relation extraction,

Reference 26

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This paper cites Document-level relation extraction with structure enhanced transformer encoder,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Document-level relation extraction with structure enhanced transformer encoder,

Reference 27

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This paper cites Diffusyn: A diffusion- driven framework with syntactic dependency for aspect sentiment triplet extraction,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Diffusyn: A diffusion- driven framework with syntactic dependency for aspect sentiment triplet extraction,

Reference 28

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This paper cites Dualgcn: Exploring syntactic and semantic information for aspect-based sentiment analysis,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Dualgcn: Exploring syntactic and semantic information for aspect-based sentiment analysis,

Reference 29

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This paper cites Integrated syntactic and semantic tree for targeted sentiment classification using dual- channel graph convolutional network,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Integrated syntactic and semantic tree for targeted sentiment classification using dual- channel graph convolutional network,

Reference 30

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This paper cites Incorporating syntax and lexical knowledge to multilingual sentiment classification on large language models,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Incorporating syntax and lexical knowledge to multilingual sentiment classification on large language models,

Reference 31

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This paper cites Semi-Supervised Classification with Graph Convolutional Networks,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Semi-Supervised Classification with Graph Convolutional Networks,

Reference 32

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This paper cites Graph attention networks,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Graph attention networks,

Reference 33

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This paper cites Dpgnn: Dual-perception graph neural network for representation learning,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Dpgnn: Dual-perception graph neural network for representation learning,

Reference 34

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This paper cites Graph convolution over pruned dependency trees improves relation extraction,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Graph convolution over pruned dependency trees improves relation extraction,

Reference 35

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This paper cites A fair comparison of graph neural networks for graph classification,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations A fair comparison of graph neural networks for graph classification,

Reference 37

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This paper cites Gnn is a counter? revisiting gnn for question answering,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Gnn is a counter? revisiting gnn for question answering,

Reference 38

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Observation 96688a09-907f-46c8-9681-f5cda0ff6d36 · outbound

This paper cites Graph-less neural networks: Teaching old mlps new tricks via distillation,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Graph-less neural networks: Teaching old mlps new tricks via distillation,

Reference 39

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Observation afee381b-ce3c-4067-9764-a3e03d76b6df · outbound

This paper cites Learning mlps on graphs: A unified view of effectiveness, robustness, and efficiency,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Learning mlps on graphs: A unified view of effectiveness, robustness, and efficiency,

Reference 40

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Observation 79fce8c3-c8ea-4f97-b32f-21f087cdd029 · outbound

This paper cites SA-MLP: Distilling graph knowledge from GNNs into structure-aware MLP,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations SA-MLP: Distilling graph knowledge from GNNs into structure-aware MLP,

Reference 41

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Observation dc8286aa-a04f-447d-b1b0-3b080961ff44 · outbound

This paper cites Bag-of-words vs. graph vs. sequence in text classification: Questioning the necessity of text-graphs and the surprising strength of a wide MLP,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Bag-of-words vs. graph vs. sequence in text classification: Questioning the necessity of text-graphs and the surprising strength of a wide MLP,

Reference 42

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This paper cites Do we really need gcns in traffic forecasting? a graph-less pure- mlp architecture,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Do we really need gcns in traffic forecasting? a graph-less pure- mlp architecture,

Reference 43

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This paper cites How well apply simple MLP to incomplete utterance rewriting?.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations How well apply simple MLP to incomplete utterance rewriting?

Reference 44

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Observation 8a47db30-a569-468e-a0ff-06024b6cc8d8 · outbound

This paper cites Re-tacred: Addressing shortcomings of the tacred dataset,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Re-tacred: Addressing shortcomings of the tacred dataset,

Reference 45

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Observation 33bfdaec-eb3d-435b-82aa-94b077a8002f · outbound

This paper cites SemEval- 2010 task 8: Multi-way classification of semantic relations between pairs of nominals,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations SemEval- 2010 task 8: Multi-way classification of semantic relations between pairs of nominals,

Reference 46

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Observation f4c35633-d447-4801-90b2-b66b6ac20698 · outbound

This paper cites Filter-enhanced mlp is all you need for sequential recommendation,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Filter-enhanced mlp is all you need for sequential recommendation,

Reference 47

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Observation 50bfa785-ba1c-4d29-bb6f-c1371d93bd2f · outbound

This paper cites Smlp4rec: an efficient all-mlp architecture for sequential recommendations,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Smlp4rec: an efficient all-mlp architecture for sequential recommendations,

Reference 48

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Observation b88bb9ce-4c6d-452d-afd2-0c2184c2cfec · outbound

This paper cites Predicting temporal sets with simplified fully connected networks,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Predicting temporal sets with simplified fully connected networks,

Reference 49

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Observation e45c6758-cff4-47c6-a5ce-e4dfc67fb2da · outbound

This paper cites Mlpst: Mlp is all you need for spatio-temporal prediction,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Mlpst: Mlp is all you need for spatio-temporal prediction,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:25:31.519022Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f2cd791a-22f9-4aec-bbf5-03895c8c83b1 · outbound

This paper cites Mlp-mixer: An all-mlp architecture for vision,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Mlp-mixer: An all-mlp architecture for vision,

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-06T22:25:23.925722Z

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Observation c346e063-3caa-4892-8459-ce7665626e46 · outbound

This paper cites As-mlp: An axial shifted mlp architecture for vision,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations As-mlp: An axial shifted mlp architecture for vision,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:25:31.323787Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fd656e04-5e26-4fc4-a3d8-3b6a29894ba3 · outbound

This paper cites A weighted gcn with logical adjacency matrix for relation extraction,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations A weighted gcn with logical adjacency matrix for relation extraction,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:25:31.165475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e1b00f58-9be8-4341-bcdf-f1a4a182df2c · outbound

This paper cites Semi-supervised classification with graph convolutional networks,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Semi-supervised classification with graph convolutional networks,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:25:30.984058Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4da19b8d-c6d2-4c40-b4d7-314796aeb2ff · outbound

This paper cites Universal Dependencies,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Universal Dependencies,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:25:30.795630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5fd7ad91-037e-4fb4-be9d-07842805ae20 · outbound

This paper cites Heterogeneous graph transformer for graph-to-sequence learning,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Heterogeneous graph transformer for graph-to-sequence learning,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:25:30.608713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 16dc5bf3-88a7-4c63-ae03-1e3784361efc · outbound

This paper cites Improving neural machine translation with the Abstract Meaning Representation by combining graph and sequence transformers,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Improving neural machine translation with the Abstract Meaning Representation by combining graph and sequence transformers,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:25:30.449975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 96639a93-2c3b-4f0c-9754-2b11a111ba9a · outbound

This paper cites Abstract Meaning Representation for sembanking,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Abstract Meaning Representation for sembanking,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:25:30.235273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7c1c009f-57cc-4bb7-81c5-cd703bcb20b4 · outbound

This paper cites Do syntax trees help pre-trained transformers extract information?.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Do syntax trees help pre-trained transformers extract information?

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:25:30.047340Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4f8da3b4-f790-4775-a4d3-7ed347f9d453 · outbound

This paper cites Graph attention networks,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Graph attention networks,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:25:29.809670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b76355d3-2507-42bd-9878-78e266a5b88c · outbound

This paper cites Probing classifiers: Promises, shortcomings, and advances,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Probing classifiers: Promises, shortcomings, and advances,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:25:29.566258Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 705195b9-bf9e-4a0e-9410-ce955c17a588 · outbound

This paper cites Information-theoretic probing for linguistic structure,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Information-theoretic probing for linguistic structure,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:25:29.207151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f50512f7-581f-4b3f-8575-7ba8c09caec4 · outbound

This paper cites An information theoretic view on selecting linguistic probes,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations An information theoretic view on selecting linguistic probes,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:25:28.931459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 11c82b9b-7ca8-4d97-8f64-f7107e017e6b · outbound

This paper cites Probing pretrained language models for lexical semantics,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Probing pretrained language models for lexical semantics,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:25:28.742440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation da3e2e73-c631-43d8-831b-d29b04917aa7 · outbound

This paper cites Beyond Binary: Towards Fine-Grained LLM-Generated Text Detection via Role Recognition and Involvement Measurement.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Beyond Binary: Towards Fine-Grained LLM-Generated Text Detection via Role Recognition and Involvement Measurement

Reference 65

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:25:27.068381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 03cfd7df-ae9b-442f-9e72-d4985100dd0e · outbound

This paper cites Semi-supervised classification with graph convolutional networks,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Semi-supervised classification with graph convolutional networks,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:25:28.574540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9843ff9b-2a48-42c7-b6e4-fb076097c968 · outbound

This paper cites Towards deeper graph neural networks,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Towards deeper graph neural networks,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:25:28.409734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 21e4cf1b-8cee-4617-a9f3-4297360feedd · outbound

This paper cites Deep residual learning for image recognition,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Deep residual learning for image recognition,

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-06T22:25:25.594352Z

Source-reported events for the cited work

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Observation 6dcdd772-d037-4f79-b997-354bbc3629bf · outbound

This paper cites What do you learn from context? probing for sentence structure in contextualized word representations,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations What do you learn from context? probing for sentence structure in contextualized word representations,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:25:28.241083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ccc8e521-0d15-46e4-afa7-108f3945ba57 · outbound

This paper cites A gold standard dependency corpus for English,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations A gold standard dependency corpus for English,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:25:28.078859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8ca21c58-46ec-4dc1-8fd0-d54a73242fcf · outbound

This paper cites Neural-Davidsonian semantic proto-role labeling,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Neural-Davidsonian semantic proto-role labeling,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:25:27.920737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a1a53e9e-c764-4b7c-8493-0937199c0c7c · outbound

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

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations BERT: Pre- training of deep bidirectional transformers for language understanding,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:25:27.789552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bc921f6f-595b-4ba9-a67e-cdf943b61534 · outbound

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

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Exploring the limits of transfer learning with a unified text-to-text transformer,

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-06T22:25:26.057327Z

Source-reported events for the cited work

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Observation b0d29250-1408-4bc4-aefe-5da2967c2630 · outbound

This paper cites Llama 3 model card,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Llama 3 model card,

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-06T22:25:26.298597Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 550b2a3d-3bd8-44e7-afa1-cf9b54760832 · outbound

This paper cites LLM2Vec: Large language models are secretly powerful text encoders,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations LLM2Vec: Large language models are secretly powerful text encoders,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:25:27.642179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f0f1dd61-8af3-41b8-ba26-9916ef9a16c7 · outbound

This paper cites Stanza: A python natural language processing toolkit for many human languages,.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations Stanza: A python natural language processing toolkit for many human languages,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:25:27.513486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2f0e6fe8-d067-4ab7-a1a6-f84e4930bbd4 · outbound

This paper cites KAN: Kolmogorov-Arnold Networks.

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations KAN: Kolmogorov-Arnold Networks

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-06T22:25:26.925357Z

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