SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models | Paper Citation Record

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This paper cites GPT-4 Technical Report.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models GPT-4 Technical Report

Reference 1

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This paper cites Sparks of Artificial General Intelligence: Early experiments with GPT-4.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Sparks of Artificial General Intelligence: Early experiments with GPT-4

Reference 2

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This paper cites LLaMA: Open and Efficient Foundation Language Models.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models LLaMA: Open and Efficient Foundation Language Models

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This paper cites Gemini: A Family of Highly Capable Multimodal Models.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Gemini: A Family of Highly Capable Multimodal Models

Reference 4

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This paper cites The economic potential of generative ai: The next productivity frontier, 2023.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models The economic potential of generative ai: The next productivity frontier, 2023

Reference 5

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Observation a77f83e9-b076-47b2-bab1-54b48e044733 · outbound

This paper cites Pwc is accelerating adoption of ai with chatgpt enterprise in us and uk and with clients, 2024.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Pwc is accelerating adoption of ai with chatgpt enterprise in us and uk and with clients, 2024

Reference 6

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Observation 8d2d8d73-ee8e-4180-82a9-32fcbcc1dac6 · outbound

This paper cites Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models -- A Survey.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models -- A Survey

Reference 7

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This paper cites Shortcut learning of large language models in natural language understanding.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Shortcut learning of large language models in natural language understanding

Reference 8

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Observation f349f8bc-052c-40fb-9d71-1c872ffe3904 · outbound

This paper cites Anchored Answers: Unravelling Positional Bias in GPT-2's Multiple-Choice Questions.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Anchored Answers: Unravelling Positional Bias in GPT-2's Multiple-Choice Questions

Reference 10

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This paper cites CalibraEval: Calibrating Prediction Distribution to Mitigate Selection Bias in LLMs-as-Judges.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models CalibraEval: Calibrating Prediction Distribution to Mitigate Selection Bias in LLMs-as-Judges

Reference 11

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This paper cites Look at the Text: Instruction-Tuned Language Models are More Robust Multiple Choice Selectors than You Think.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Look at the Text: Instruction-Tuned Language Models are More Robust Multiple Choice Selectors than You Think

Reference 12

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Observation e2ddc13a-a790-4983-8e6b-0df7fcbf2a43 · outbound

This paper cites Language models are few-shot learners.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Language models are few-shot learners

Reference 13

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Observation 0cc113ea-39ac-4f19-a6ef-57a2f6c3590c · outbound

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

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 14

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Observation d5ea368c-46eb-4423-8f61-3f20b0bb8b88 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Measuring Massive Multitask Language Understanding

Reference 15

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Observation 792dc029-15b8-4a49-bf2f-6bb7d7223cfd · outbound

This paper cites Commonsenseqa: A question answering challenge targeting commonsense knowledge.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Commonsenseqa: A question answering challenge targeting commonsense knowledge

Reference 16

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Observation ca26ebfc-5895-458f-80aa-cdd3cebe1982 · outbound

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

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 17

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This paper cites Can a suit of armor conduct electricity? a new dataset for open book question answering.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Can a suit of armor conduct electricity? a new dataset for open book question answering

Reference 18

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Observation bd3fae3e-6895-46f2-8f8a-9d697081e08e · outbound

This paper cites Beyond the imitation game: Quantifying and extrapolating the capabilities of language models.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Beyond the imitation game: Quantifying and extrapolating the capabilities of language models

Reference 19

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Observation b91a51a6-a339-4d1e-b70a-3fb91eafeb9b · outbound

This paper cites Holistic Evaluation of Language Models.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Holistic Evaluation of Language Models

Reference 20

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Observation a999ef10-10e4-4015-be27-90b254a96c08 · outbound

This paper cites M3exam: A multilingual, multimodal, multilevel benchmark for examining large language models.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models M3exam: A multilingual, multimodal, multilevel benchmark for examining large language models

Reference 21

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Observation 8123bda6-f4b8-46fc-8f07-d6a3cbaaecc7 · outbound

This paper cites Agieval: A human-centric benchmark for evaluating foundation models.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Agieval: A human-centric benchmark for evaluating foundation models

Reference 22

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Observation d3e3c2aa-2ceb-4a65-8b2d-724c83c9f536 · outbound

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SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Medmcqa: A large-scale multi-subject multi-choice dataset for medical domain question answering

Reference 23

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This paper cites Crowdsourcing multiple choice science questions.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Crowdsourcing multiple choice science questions

Reference 24

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This paper cites From live data to high-quality benchmarks: The arena-hard pipeline.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models From live data to high-quality benchmarks: The arena-hard pipeline

Reference 25

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Observation e3db5bdc-e636-4f69-a407-95adab2f9f80 · outbound

This paper cites Chatbot arena: An open platform for evaluating llms by human preference.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Chatbot arena: An open platform for evaluating llms by human preference

Reference 26

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Observation 9f7a62ba-9fde-4987-ae00-0777e92a8a65 · outbound

This paper cites Large language models are not fair evaluators.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Large language models are not fair evaluators

Reference 27

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Observation ab2c8cd0-06b0-4f5d-9b3f-6be3e265e722 · outbound

This paper cites Apbench and benchmarking large language model performance in fundamental astrodynamics problems for space engineering.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Apbench and benchmarking large language model performance in fundamental astrodynamics problems for space engineering

Reference 28

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Observation ecf966ea-e5af-4d75-a462-0c8c733f6360 · outbound

This paper cites Where is the answer? an empirical study of positional bias for parametric knowledge extraction in language model.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Where is the answer? an empirical study of positional bias for parametric knowledge extraction in language model

Reference 29

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Observation 64cad35f-936a-4df2-9b8a-c5f5aff12226 · outbound

This paper cites Option symbol matters: Investigating and mitigating multiple-choice option symbol bias of large language models.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Option symbol matters: Investigating and mitigating multiple-choice option symbol bias of large language models

Reference 30

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SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Large language models sensitivity to the order of options in multiple- choice questions

Reference 31

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Observation 23bf2d76-da00-464e-8b44-1cf3feee1a49 · outbound

This paper cites Large Language Models Are Not Robust Multiple Choice Selectors.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Large Language Models Are Not Robust Multiple Choice Selectors

Reference 32

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SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Fool your (vision and) language model with embarrassingly simple permutations

Reference 33

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Observation 4fd8a892-a18c-4df6-acf8-52a6a94ba69c · outbound

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SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Teacher-student training for debiasing: General permutation debiasing for large language models

Reference 34

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Observation 49d0fb8b-749a-4c49-a354-e988cd73e886 · outbound

This paper cites Mitigating Selection Bias with Node Pruning and Auxiliary Options.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Mitigating Selection Bias with Node Pruning and Auxiliary Options

Reference 35

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This paper cites Unveiling selection biases: Exploring order and token sensitivity in large language models.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Unveiling selection biases: Exploring order and token sensitivity in large language models

Reference 36

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Observation c5073e1d-af59-4fab-a0aa-60dbc4c9be1b · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.Advances in neural information processing systems, 35:24824–24837, 2022.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Chain-of-thought prompting elicits reasoning in large language models.Advances in neural information processing systems, 35:24824–24837, 2022

Reference 37

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Observation c5c97aeb-d96c-41b5-b53c-15482b0c79ff · outbound

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SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Large language models are zero-shot reasoners

Reference 38

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Observation 7ed7787b-ed82-49ae-9e66-846a8d6348ed · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 39

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Observation 5bbae5f3-6a6e-4388-ae67-6029fe8ad69a · outbound

This paper cites Star: Self-taught reasoner bootstrapping reasoning with reasoning.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Star: Self-taught reasoner bootstrapping reasoning with reasoning

Reference 40

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Observation e06a5064-bf7a-4a76-b3ef-2bab70a4aafa · outbound

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SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Least-to-Most Prompting Enables Complex Reasoning in Large Language Models

Reference 41

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Observation 7d3b7e95-806d-40bb-8b0a-78b2baf192ec · outbound

This paper cites React: Synergizing reasoning and acting in language models.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models React: Synergizing reasoning and acting in language models

Reference 42

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Observation 9d7f7b60-f742-4ed4-8262-538aa0aa21a9 · outbound

This paper cites Debiasing in-context learning by instructing llms how to follow demonstrations.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Debiasing in-context learning by instructing llms how to follow demonstrations

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:43.412777Z

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Observation 568a757c-58f2-439f-9301-512f20c93783 · outbound

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SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks

Reference 44

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Observation 46c97963-d8cb-490f-b595-6a688a93b16c · outbound

This paper cites Prompt sketching for large language models.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Prompt sketching for large language models

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:43.403188Z

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Observation 25ae7ea6-21e6-4729-a55d-f3fa6c05b86a · outbound

This paper cites Self-refine: Iterative refinement with self-feedback.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Self-refine: Iterative refinement with self-feedback

Reference 46

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Observation 88a31bde-6223-435b-90a9-6c6349be6b86 · outbound

This paper cites Constitutional AI: Harmlessness from AI Feedback.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Constitutional AI: Harmlessness from AI Feedback

Reference 47

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Observation 74233023-f874-45d8-8741-9eebd5e2d606 · outbound

This paper cites Neurologic decoding:(un) supervised neural text generation with predicate logic constraints.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Neurologic decoding:(un) supervised neural text generation with predicate logic constraints

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:43.389274Z

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Observation fe1876f5-78d7-4d4c-b641-4d40858ffdf3 · outbound

This paper cites Calibration of pre-trained transformers.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Calibration of pre-trained transformers

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:43.379919Z

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Observation 5ceeb87a-9260-4404-b871-4ed2589f7ac2 · outbound

This paper cites Calibrate before use: Improving few-shot performance of language models.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Calibrate before use: Improving few-shot performance of language models

Reference 50

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Observation 5f4750a8-0d4e-46bd-9a14-32a3ef571d81 · outbound

This paper cites Calibrating language models with adaptive temperature scaling.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Calibrating language models with adaptive temperature scaling

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:43.365364Z

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Observation 291e6d06-dc20-49f7-91ee-40bff5409dc0 · outbound

This paper cites Calibrating large language models with sample consistency.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Calibrating large language models with sample consistency

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:43.356435Z

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Observation f28128f1-d3a7-4447-b8ad-b0a4c509c41b · outbound

This paper cites Benchmarking uncertainty quantification methods for large language models with lm-polygraph.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Benchmarking uncertainty quantification methods for large language models with lm-polygraph

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:43.347645Z

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Observation c7ad5102-cabb-474c-b834-4c0c79213041 · outbound

This paper cites Thermometer: towards universal calibration for large language models.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Thermometer: towards universal calibration for large language models

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:43.338319Z

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Observation 981f2f23-405a-4692-a00e-7f33bd4543e3 · outbound

This paper cites Monte carlo temperature: a robust sampling strategy for llm’s uncertainty quantification methods.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Monte carlo temperature: a robust sampling strategy for llm’s uncertainty quantification methods

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:43.329636Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 30a565e2-2bc1-44e8-947c-a349b0dbe6b3 · outbound

This paper cites Charm: Calibrating reward models with chatbot arena scores.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Charm: Calibrating reward models with chatbot arena scores

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-06T14:45:42.716462Z

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Observation 8941c8ba-16f9-438b-9dcb-e1edbbcd7d68 · outbound

This paper cites Restoring calibration for aligned large language models: A calibration-aware fine-tuning approach.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Restoring calibration for aligned large language models: A calibration-aware fine-tuning approach

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T14:45:42.719088Z

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Observation 54a0abea-7d07-4838-bc15-dd125385f050 · outbound

This paper cites Uncertainty estimation in large language models to support biodiversity conservation.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Uncertainty estimation in large language models to support biodiversity conservation

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:43.321254Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c4da330a-b34e-4de7-b732-7b850c1a77d2 · outbound

This paper cites Evaluating Large Language Models in Theory of Mind Tasks.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Evaluating Large Language Models in Theory of Mind Tasks

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-06T14:45:42.724363Z

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Observation 45e80a02-291f-45f8-af0f-f452ba01f3b9 · outbound

This paper cites Neural theory-of-mind? on the limits of social intelligence in large lms.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Neural theory-of-mind? on the limits of social intelligence in large lms

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:43.311892Z

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Observation 3732c923-91aa-4618-9630-d810928a1e8b · outbound

This paper cites Social iqa: Commonsense reasoning about social interactions.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Social iqa: Commonsense reasoning about social interactions

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:43.303710Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f6ae2707-9ad2-4811-ade7-46f523ba93a3 · outbound

This paper cites Large language models are not strong abstract reasoners.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Large language models are not strong abstract reasoners

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:43.295120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0c5f58cd-35f4-43ac-a61b-3680e204b522 · outbound

This paper cites Coglm: Tracking cognitive development of large language models.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Coglm: Tracking cognitive development of large language models

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:43.285900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 51baff84-0779-4c6e-9fb3-16938bc28486 · outbound

This paper cites V-alphasocial: Benchmark and self-reflective chain-of- thought generation for visual social commonsense reasoning.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models V-alphasocial: Benchmark and self-reflective chain-of- thought generation for visual social commonsense reasoning

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:43.277771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 627a74e2-98d7-42b2-929e-0df52d2e2100 · outbound

This paper cites Mind2web: Towards a generalist agent for the web.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Mind2web: Towards a generalist agent for the web

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-06T14:45:42.739479Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7592be3c-8ba4-4e30-8d6d-1e4ef8e92ddf · outbound

This paper cites Mind2Web 2: Evaluating Agentic Search with Agent-as-a-Judge.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Mind2Web 2: Evaluating Agentic Search with Agent-as-a-Judge

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-06T14:45:42.742049Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7bed5f91-00c3-4549-b38e-a7350617a961 · outbound

This paper cites Long Range Arena: A Benchmark for Efficient Transformers.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Long Range Arena: A Benchmark for Efficient Transformers

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-06T14:45:42.745219Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91e16ef8-01f0-4903-8d7c-ca63f47db4b5 · outbound

This paper cites Minerva: A Programmable Memory Test Benchmark for Language Models.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Minerva: A Programmable Memory Test Benchmark for Language Models

Reference 68

Resolution: verified exact; local_arxiv, observed 2026-08-06T14:45:42.847863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0b7abe6e-7add-4c05-9ba7-0a785830c544 · outbound

This paper cites L-eval: Instituting standardized evaluation for long context language models.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models L-eval: Instituting standardized evaluation for long context language models

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:43.264284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation af100c43-3815-4d90-a3df-597d7498c86d · outbound

This paper cites Needle in the Haystack for Memory Based Large Language Models.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Needle in the Haystack for Memory Based Large Language Models

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-06T14:45:42.754635Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3c961f2-fb53-4177-96d8-a6b899f08052 · outbound

This paper cites Conversational AI Powered by Large Language Models Amplifies False Memories in Witness Interviews.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Conversational AI Powered by Large Language Models Amplifies False Memories in Witness Interviews

Reference 71

Resolution: verified exact; local_arxiv, observed 2026-08-06T14:45:42.825972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 15d3363f-d81e-4c91-ab3b-69513f717179 · outbound

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

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Sentence-bert: Sentence embeddings using siamese bert-networks

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:43.255832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e5677550-9e97-4b0e-9fda-11a30c4b5b32 · outbound

This paper cites Introduction to information retrieval , volume 39.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Introduction to information retrieval , volume 39

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:43.246950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c7c6cf1c-fa05-4c7a-9208-b05183f4260b · outbound

This paper cites The claude 3 model family: Opus, sonnet, haiku.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models The claude 3 model family: Opus, sonnet, haiku

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:43.237288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e6138f8e-1207-4806-a43c-7e73eb63aefa · outbound

This paper cites Model card addendum: Claude 3.5 haiku and sonnet.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Model card addendum: Claude 3.5 haiku and sonnet

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:43.229332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8918cd5d-be6f-4bd8-b8f9-282fccf6cdc2 · outbound

This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-06T14:45:42.772324Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4eee62f-382c-4feb-ad6a-30862ead2565 · outbound

This paper cites Introducing meta llama 3.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models Introducing meta llama 3

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:43.221259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation de7165fe-97ad-4746-84c9-0e7cb86218a3 · outbound

This paper cites The serial position effect of free recall.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models The serial position effect of free recall

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:43.213126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1e0237f2-0b8c-4141-b590-1b0b2faa909c · outbound

This paper cites luck-free.

SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models luck-free

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:43.203784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers