Efficient Benchmarking Is Just Feature Selection and Multiple Regression | Paper Citation Record

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This paper cites URL https:// doi.org/10.18653/v1/p19-1472.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression URL https:// doi.org/10.18653/v1/p19-1472

Reference 1

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Observation 4b06322c-a809-4661-9755-18ef6872d5a9 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression Measuring Massive Multitask Language Understanding

Reference 2

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This paper cites Evaluating Large Language Models Trained on Code.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression Evaluating Large Language Models Trained on Code

Reference 3

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This paper cites Manning, Christopher Re, Diana Acosta-Navas, Drew A.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression Manning, Christopher Re, Diana Acosta-Navas, Drew A

Reference 4

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This paper cites Open LLM Leaderboard v2, 2024.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression Open LLM Leaderboard v2, 2024

Reference 5

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This paper cites Anchor points: Benchmarking models with much fewer examples.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression Anchor points: Benchmarking models with much fewer examples

Reference 6

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This paper cites tinyBenchmarks : evaluating LLMs with fewer examples.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression tinyBenchmarks : evaluating LLMs with fewer examples

Reference 7

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This paper cites Schulze Buschoff, and Eric Schulz.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression Schulze Buschoff, and Eric Schulz

Reference 8

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This paper cites Confident Rankings with Fewer Items : Adaptive LLM Evaluation with Continuous Scores , 2026.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression Confident Rankings with Fewer Items : Adaptive LLM Evaluation with Continuous Scores , 2026

Reference 9

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This paper cites Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik R.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik R

Reference 10

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This paper cites TurnBench - MS : A Benchmark for Evaluating Multi - Turn , Multi - Step Reasoning in Large Language Models.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression TurnBench - MS : A Benchmark for Evaluating Multi - Turn , Multi - Step Reasoning in Large Language Models

Reference 11

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This paper cites Chain-of- Thought Prompting Elicits Reasoning in Large Language Models.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression Chain-of- Thought Prompting Elicits Reasoning in Large Language Models

Reference 12

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This paper cites The Curious Case of Neural Text Degeneration.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression The Curious Case of Neural Text Degeneration

Reference 13

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This paper cites The effect of sampling temperature on problem solving in large language models.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression The effect of sampling temperature on problem solving in large language models

Reference 14

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This paper cites Roush, Andreas Kirsch, and Ravid Shwartz-Ziv.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression Roush, Andreas Kirsch, and Ravid Shwartz-Ziv

Reference 15

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This paper cites Quantifying Language Models ' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression Quantifying Language Models ' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting

Reference 16

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This paper cites Brittlebench: Quantifying LLM robustness via prompt sensitivity.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression Brittlebench: Quantifying LLM robustness via prompt sensitivity

Reference 17

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This paper cites Language Models are Few - Shot Learners.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression Language Models are Few - Shot Learners

Reference 18

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This paper cites Analysis and comparison of feature selection methods towards performance and stability.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression Analysis and comparison of feature selection methods towards performance and stability

Reference 19

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This paper cites Knowledge and Information Systems 66(3), 1575–1637 (2024) https://doi.org/10.1007/s10115-023-02010-5 16 Under-review.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression Knowledge and Information Systems 66(3), 1575–1637 (2024) https://doi.org/10.1007/s10115-023-02010-5 16 Under-review

Reference 20

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This paper cites IEEE Trans.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression IEEE Trans

Reference 21

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This paper cites Minimum Redundancy Feature Selection From Microarray Gene Expression Data.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression Minimum Redundancy Feature Selection From Microarray Gene Expression Data

Reference 22

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This paper cites Exploiting centrality information with graph convolutions for network representation learning.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression Exploiting centrality information with graph convolutions for network representation learning

Reference 23

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Efficient Benchmarking Is Just Feature Selection and Multiple Regression Hoerl and Robert W

Reference 24

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This paper cites Ridge Regression Learning Algorithm in Dual Variables.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression Ridge Regression Learning Algorithm in Dual Variables

Reference 25

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This paper cites The Elements of Statistical Learning.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression The Elements of Statistical Learning

Reference 26

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This paper cites Automatic.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression Automatic

Reference 27

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This paper cites A coreset selection of coreset selection literature: Introduction and recent advances.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression A coreset selection of coreset selection literature: Introduction and recent advances

Reference 28

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This paper cites NP -completeness of searches for smallest possible feature sets.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression NP -completeness of searches for smallest possible feature sets

Reference 29

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This paper cites Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, and Yuxiong He.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, and Yuxiong He

Reference 30

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This paper cites Estimating Mutual Information.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression Estimating Mutual Information

Reference 31

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Efficient Benchmarking Is Just Feature Selection and Multiple Regression Efficient Estimation of Mutual Information for Strongly Dependent Variables

Reference 32

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Efficient Benchmarking Is Just Feature Selection and Multiple Regression Frank and Jerome H

Reference 33

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Efficient Benchmarking Is Just Feature Selection and Multiple Regression Gaussian Process Assisted Active Learning of Physical Laws

Reference 34

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Efficient Benchmarking Is Just Feature Selection and Multiple Regression and Guyon, Isabelle M

Reference 35

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This paper cites Inferences from Multinomal Data: Learning about a bag of marbles.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression Inferences from Multinomal Data: Learning about a bag of marbles

Reference 36

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Efficient Benchmarking Is Just Feature Selection and Multiple Regression Lord, M.R

Reference 37

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Efficient Benchmarking Is Just Feature Selection and Multiple Regression Item response theory: Parameter estimation techniques

Reference 38

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Efficient Benchmarking Is Just Feature Selection and Multiple Regression Van Der Linden

Reference 39

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Efficient Benchmarking Is Just Feature Selection and Multiple Regression and Wu, Hao and Yu, Hong

Reference 40

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Efficient Benchmarking Is Just Feature Selection and Multiple Regression Clustering Examples in Multi - Dataset Benchmarks with Item Response Theory

Reference 41

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This paper cites Position: AI Evaluations Should be Grounded on a Theory of Capability.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression Position: AI Evaluations Should be Grounded on a Theory of Capability

Reference 42

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Efficient Benchmarking Is Just Feature Selection and Multiple Regression Singh, Rylan Schaeffer, Andrew Poulton, Sanmi Koyejo, Pontus Stenetorp, Sharan Narang, and Dieuwke Hupkes

Reference 43

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Efficient Benchmarking Is Just Feature Selection and Multiple Regression A Beta Item Response Model for Continuous Bounded Responses

Reference 44

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Efficient Benchmarking Is Just Feature Selection and Multiple Regression Dorner, and Moritz Hardt

Reference 45

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Efficient Benchmarking Is Just Feature Selection and Multiple Regression Efficient Attentions for Long Document Summarization

Reference 46

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This paper cites Overview of the BioLaySumm 2025 Shared Task on Lay Summarization of Biomedical Research Articles and Radiology Reports.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression Overview of the BioLaySumm 2025 Shared Task on Lay Summarization of Biomedical Research Articles and Radiology Reports

Reference 47

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Efficient Benchmarking Is Just Feature Selection and Multiple Regression Weinberger, and Yoav Artzi

Reference 48

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Efficient Benchmarking Is Just Feature Selection and Multiple Regression Unresolved cited work

Reference 49

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Efficient Benchmarking Is Just Feature Selection and Multiple Regression Program Synthesis with Large Language Models

Reference 50

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This paper cites Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation

Reference 51

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Observation d1d91cc5-dbad-42b4-aa1a-0eccee9c93ff · outbound

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Efficient Benchmarking Is Just Feature Selection and Multiple Regression On Leakage of Code Generation Evaluation Datasets

Reference 52

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This paper cites Spearman.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression Spearman

Reference 53

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This paper cites A New Measure of Rank Correlation.Biometrika1938; 30(1–2): 81–93.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression A New Measure of Rank Correlation.Biometrika1938; 30(1–2): 81–93

Reference 54

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This paper cites On the stability of feature selection algorithms.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression On the stability of feature selection algorithms

Reference 55

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This paper cites PPI++: Efficient Prediction-Powered Inference.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression PPI++: Efficient Prediction-Powered Inference

Reference 56

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Observation a2b6b653-335b-488c-9741-30fb78724d9b · outbound

This paper cites Active Evaluation Acquisition for Efficient LLM Benchmarking.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression Active Evaluation Acquisition for Efficient LLM Benchmarking

Reference 57

Resolution: unresolved; no resolver link, observed 2026-06-29T20:20:15.314971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b1dceb5-43d3-4076-b705-a3facb155e43 · outbound

This paper cites an unresolved cited work.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression Unresolved cited work

Reference 58

Resolution: unresolved; no resolver link, observed 2026-06-29T20:20:15.314971Z

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Observation 2e2d9e09-3a96-4c26-be92-ad43072eacb1 · outbound

This paper cites Beyond One - Size - Fits - All : Tailored Benchmarks for Efficient Evaluation.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression Beyond One - Size - Fits - All : Tailored Benchmarks for Efficient Evaluation

Reference 59

Resolution: unresolved; no resolver link, observed 2026-06-29T20:20:15.314971Z

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Observation 1748603e-32aa-4c12-9a2a-95864d90ae46 · outbound

This paper cites Rubinstein, B.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression Rubinstein, B

Reference 60

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T00:24:05.150302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2e82c324-8eeb-4941-b0d0-f7fc9f966142 · outbound

This paper cites SubLIME : Subset Selection via Rank Correlation Prediction for Data - Efficient LLM Evaluation.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression SubLIME : Subset Selection via Rank Correlation Prediction for Data - Efficient LLM Evaluation

Reference 61

Resolution: verified exact; doi, observed 2026-06-29T20:23:57.529655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 095ad887-e5a9-4435-b8a9-e5d913988171 · outbound

This paper cites EffiEval: Efficient and Generalizable Model Evaluation via Capability Coverage Maximization.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression EffiEval: Efficient and Generalizable Model Evaluation via Capability Coverage Maximization

Reference 62

Resolution: verified exact; arxiv_id, observed 2026-06-30T00:24:05.157736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 76a94103-1a96-4e0b-8c1f-c8fb47ed5430 · outbound

This paper cites BenTo : Benchmark Task Reduction with In - Context Transferability.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression BenTo : Benchmark Task Reduction with In - Context Transferability

Reference 63

Resolution: unresolved; no resolver link, observed 2026-06-29T20:20:15.314971Z

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Observation 8f7be017-9460-4452-9ccb-06f495614956 · outbound

This paper cites You Don 't Need to Run Every Eval , 2026.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression You Don 't Need to Run Every Eval , 2026

Reference 64

Resolution: unresolved; no resolver link, observed 2026-06-29T20:20:15.314971Z

Source-reported events for the cited work

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Observation 6bcb9df7-67c6-4446-925a-c48b8ee25d64 · outbound

This paper cites 100 instances is all you need: predicting the success of a new LLM on unseen data by testing on a few instances.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression 100 instances is all you need: predicting the success of a new LLM on unseen data by testing on a few instances

Reference 65

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T00:24:05.140715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d4bb1e6d-f27f-4825-b990-f948cc73aeaf · outbound

This paper cites How to Select Datapoints for Efficient Human Evaluation of NLG Models ? Transactions of the Association for Computational Linguistics, 13: 0 1789--1811, 2025.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression How to Select Datapoints for Efficient Human Evaluation of NLG Models ? Transactions of the Association for Computational Linguistics, 13: 0 1789--1811, 2025

Reference 66

Resolution: verified exact; doi, observed 2026-06-29T20:23:57.543379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f7266e44-4980-428e-a64a-fa10f3a5be85 · outbound

This paper cites Note on Regression and Inheritance in the Case of Two Parents.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression Note on Regression and Inheritance in the Case of Two Parents

Reference 67

Resolution: unresolved; no resolver link, observed 2026-06-29T20:20:15.314971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e0a75714-3457-4c9a-9277-0d5b48827d6d · outbound

This paper cites Solutions to instability problems with sequential wrapper-based approaches to feature selection.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression Solutions to instability problems with sequential wrapper-based approaches to feature selection

Reference 68

Resolution: unresolved; no resolver link, observed 2026-06-29T20:20:15.314971Z

Source-reported events for the cited work

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Observation 82d5902b-b220-4aa7-98be-b882025174ce · outbound

This paper cites Instruction-Following Evaluation for Large Language Models.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression Instruction-Following Evaluation for Large Language Models

Reference 69

Resolution: verified exact; local_arxiv, observed 2026-06-30T00:24:05.142943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1b2eae54-23af-4b80-a327-afb5248aaced · outbound

This paper cites Measuring Mathematical Problem Solving With the MATH Dataset.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression Measuring Mathematical Problem Solving With the MATH Dataset

Reference 70

Resolution: unresolved; no resolver link, observed 2026-06-29T20:20:15.314971Z

Source-reported events for the cited work

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Observation 3549b3fe-e34c-4482-95f4-7196cfc8c96d · outbound

This paper cites Advances in.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression Advances in

Reference 71

Resolution: metadata mismatch; doi, observed 2026-06-29T20:23:57.505564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 111d0f79-e802-4d69-8bd9-9c419aeffc58 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 72

Resolution: verified exact; local_arxiv, observed 2026-06-30T00:24:05.175434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation be5f8723-73d2-4c02-9702-8e92b3e2b881 · outbound

This paper cites Challenging.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression Challenging

Reference 73

Resolution: metadata mismatch; doi, observed 2026-06-29T20:23:57.509589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 585485f1-c360-4aff-95a4-c830b5a4e25c · outbound

This paper cites an unresolved cited work.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression Unresolved cited work

Reference 74

Resolution: unresolved; no resolver link, observed 2026-06-29T20:20:15.314971Z

Source-reported events for the cited work

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Observation 9d1caec3-0ab7-4bcf-857a-555ccc3d2041 · outbound

This paper cites MuSR : Testing the Limits of Chain -of-thought with Multistep Soft Reasoning.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression MuSR : Testing the Limits of Chain -of-thought with Multistep Soft Reasoning

Reference 75

Resolution: unresolved; no resolver link, observed 2026-06-29T20:20:15.314971Z

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Observation 6f9d1d2b-379e-4194-84bc-f7a27f98285b · outbound

This paper cites doi: 10.18653/v1/n19-1421.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression doi: 10.18653/v1/n19-1421

Reference 76

Resolution: metadata mismatch; doi, observed 2026-06-29T20:23:57.500392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 26bfe734-3a07-48a8-8428-ef4b6afee8fa · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression Training Verifiers to Solve Math Word Problems

Reference 77

Resolution: verified exact; local_arxiv, observed 2026-06-30T00:24:05.133052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fdfd7c3a-daf7-4fa4-9703-0fff60593f4a · outbound

This paper cites LegalBench : A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression LegalBench : A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models

Reference 78

Resolution: unresolved; no resolver link, observed 2026-06-29T20:20:15.314971Z

Source-reported events for the cited work

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Observation ffec0047-098b-4d7d-b6e9-5458de48f5f6 · outbound

This paper cites What disease does this patient have? A large-scale open domain question answering dataset from medical exams.Applied Sciences, 11(14):6421.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression What disease does this patient have? A large-scale open domain question answering dataset from medical exams.Applied Sciences, 11(14):6421

Reference 79

Resolution: verified exact; doi, observed 2026-06-29T20:23:57.531701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e7c1abf9-1f0e-41f1-ab8f-cfa7ce811a34 · outbound

This paper cites arXiv preprint arXiv:2510.04051 , year=.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression arXiv preprint arXiv:2510.04051 , year=

Reference 80

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T00:24:05.135456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 94b38345-ad96-40ea-be00-3eaf54c464c6 · outbound

This paper cites Prudencio, Tom Diethe, and Peter Flach.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression Prudencio, Tom Diethe, and Peter Flach

Reference 81

Resolution: unresolved; no resolver link, observed 2026-06-29T20:20:15.314971Z

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