Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets | Paper Citation Record

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This paper cites Deriving machine attention from human rationales.

Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Deriving machine attention from human rationales

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Interpretable neural predictions with differentiable binary variables

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets LLaVA Steering: Visual Instruction Tuning with 500x Fewer Parameters through Modality Linear Representation-Steering

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This paper cites PRISM: Self-Pruning Intrinsic Selection Method for Training-Free Multimodal Data Selection.

Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets PRISM: Self-Pruning Intrinsic Selection Method for Training-Free Multimodal Data Selection

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets CoT-Kinetics: A Theoretical Modeling Assessing LRM Reasoning Process

Reference 5

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This paper cites UNIREX: A unified learning framework for language model rationale extraction.

Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets UNIREX: A unified learning framework for language model rationale extraction

Reference 6

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Unresolved cited work

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Unresolved cited work

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Unresolved cited work

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This paper cites Learning phrase representations using RNN encoder-decoder for statistical machine translation.

Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Learning phrase representations using RNN encoder-decoder for statistical machine translation

Reference 10

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets BERT: pre-training of deep bidirectional transformers for language understanding

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets F., Lehman, E., Xiong, C., Socher, R., and Wallace, B

Reference 12

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Learning musical representations for music performance question answering

Reference 13

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This paper cites Temporal working memory: Query-guided segment refinement for enhanced multimodal understanding.

Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Temporal working memory: Query-guided segment refinement for enhanced multimodal understanding

Reference 14

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Unresolved cited work

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets GOOD: A graph out-of-distribution benchmark

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This paper cites Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization.

Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization

Reference 17

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This paper cites Evaluating Large Language Models: A Comprehensive Survey.

Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Evaluating Large Language Models: A Comprehensive Survey

Reference 18

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Unresolved cited work

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This paper cites Distribution matching for rationalization.

Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Distribution matching for rationalization

Reference 20

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets and Goldberg, Y

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets and Goldberg, Y

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Unresolved cited work

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Categorical reparameterization with gumbel-softmax

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets HiddenDetect: Detecting Jailbreak Attacks against Large Vision-Language Models via Monitoring Hidden States

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Adam: A Method for Stochastic Optimization

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Unresolved cited work

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Unresolved cited work

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets FR : Folded rationalization with a unified encoder

Reference 29

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets MGR: multi-generator based rationalization

Reference 30

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets D-separation for causal self-explanation

Reference 31

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Decoupled rationalization with asymmetric learning rates: A flexible lipschitz restraint

Reference 32

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Is the mmi criterion necessary for interpretability? degenerating non-causal features to plain noise for self-rationalization

Reference 33

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Enhancing the rationale-input alignment for self-explaining rationalization

Reference 34

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Breaking free from MMI : A new frontier in rationalization by probing input utilization

Reference 35

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Parameterized explainer for graph neural network

Reference 36

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets J., Leskovec, J., and Jurafsky, D

Reference 37

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Unresolved cited work

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets An information bottleneck approach for controlling conciseness in rationale extraction

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Unresolved cited work

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Making a (counterfactual) difference one rationale at a time

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Interpretable data-based explanations for fairness debugging

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets H., and Tsvetkov, Y

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Where we have arrived in proving the emergence of sparse interaction primitives in DNN s

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Unresolved cited work

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Learning from the best: Rationalizing predictions by adversarial information calibration

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Unresolved cited work

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets TrustLLM: Trustworthiness in Large Language Models

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Dynamic knowledge adapter with probabilistic calibration for generalized few-shot semantic segmentation

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Lightweight frequency masker for cross-domain few-shot semantic segmentation

Reference 51

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Flowcut: Rethinking redundancy via information flow for efficient vision-language models

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Latent aspect rating analysis on review text data: a rating regression approach

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Discovering invariant rationales for graph neural networks

Reference 55

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets LESS: Selecting Influential Data for Targeted Instruction Tuning

Reference 56

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Observation 77f4fed0-f010-43d2-9b61-6b803f53eb7b · outbound

This paper cites Mitigating the Backdoor Effect for Multi-Task Model Merging via Safety-Aware Subspace.

Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Mitigating the Backdoor Effect for Multi-Task Model Merging via Safety-Aware Subspace

Reference 57

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Observation 3cb34739-38dd-482c-bcef-495e55882648 · outbound

This paper cites Leveraging invariant principle for heterophilic graph structure distribution shifts.

Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Leveraging invariant principle for heterophilic graph structure distribution shifts

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:09:04.526770Z

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Observation 99c708bb-3ea0-40fd-ae27-4bf3997d3e8b · outbound

This paper cites Gnnexplainer: Generating explanations for graph neural networks.

Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Gnnexplainer: Generating explanations for graph neural networks

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:09:04.517504Z

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Unresolved cited work

Reference 60

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Observation 8612fb3f-dc26-4a24-8d4d-f7005ce1d04e · outbound

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Unresolved cited work

Reference 61

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Observation 1738ced9-d69e-4390-b499-dc72c54b327e · outbound

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Unresolved cited work

Reference 62

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Observation b6469543-cfe1-405a-ac65-be35682395f9 · outbound

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Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Unresolved cited work

Reference 63

Resolution: verified exact; doi, observed 2026-08-16T01:09:03.448722Z

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Observation 24f56e1d-893f-405d-ae42-ab0d049464f6 · outbound

This paper cites Interpreting image classifiers by generating discrete masks.

Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Interpreting image classifiers by generating discrete masks

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-16T01:09:03.350558Z

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Observation 4f786bad-d906-443f-989b-0bca836e6187 · outbound

This paper cites Interventional rationalization.

Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Interventional rationalization

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:09:04.494873Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 44cd78d0-e6cd-4a35-b8a2-f99de58b3f3f · outbound

This paper cites Towards Trustworthy Explanation: On Causal Rationalization.

Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Towards Trustworthy Explanation: On Causal Rationalization

Reference 66

Resolution: verified exact; local_arxiv, observed 2026-08-16T01:09:03.908458Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2fb108da-b270-4749-a943-a4d69770638d · outbound

This paper cites A Survey of Large Language Models.

Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets A Survey of Large Language Models

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-16T01:09:03.364146Z

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Observation 957953b5-11e2-4bda-ace6-408fa4c6492b · outbound

This paper cites The irrationality of neural rationale models.

Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets The irrationality of neural rationale models

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-16T01:09:03.368825Z

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Observation c456fbe7-a949-4279-affb-f89d6488f788 · outbound

This paper cites Dual-arm robotic fabric manipulation with quasi-static and dynamic primitives for rapid garment flattening.

Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Dual-arm robotic fabric manipulation with quasi-static and dynamic primitives for rapid garment flattening

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-16T01:09:03.373572Z

Source-reported events for the cited work

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Observation 6fae4d67-1f6a-4b10-9097-d71bfa09f76d · outbound

This paper cites write newline.

Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets write newline

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-16T01:09:03.378435Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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