Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T12:40:26.743983Z
Paper Citation Record · LEDGER
As of 7 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 1 inbound Pith citation observation for arXiv:2505.24147.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T12:40:26.743983Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-04T19:34:29.377709Z
A source-named dated measurement, never combined with another source.
Source: cited_works
63 of 63 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 9a44730b-7cb5-4230-966a-77be09148183 · outbound
Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability e-SNLI: Natural Language Inference with Natural Language Explanations
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Observation 28d0f066-cc60-472f-9c0d-2ef3ed4c4ee9 · outbound
Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability What to Learn, and How: Toward Effective Learning from Rationales
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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Scaling Instruction-Finetuned Language Models
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Observation 161dc58f-b8ea-435b-a4cf-80d4c290baf4 · outbound
Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
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Observation 561e22a8-88ba-42ab-b917-6760969e44fa · outbound
Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Training Verifiers to Solve Math Word Problems
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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work
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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Calibration of Pre-trained Transformers
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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work
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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work
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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work
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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Understanding Dataset Difficulty with $\mathcal{V}$-Usable Information
Reference 11
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Observation 55d64ad1-a4c2-4cb8-8a2a-f7342e66b8b9 · outbound
Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Specializing Smaller Language Models towards Multi-Step Reasoning
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Observation 5b86da8c-5c8e-4bef-aadb-2c20b1c4aaef · outbound
Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work
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Observation e4d1d33d-c773-4b3c-ba0d-be347a81914f · outbound
Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability On Calibration of Modern Neural Networks
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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work
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Observation 6a428f17-441c-4495-b920-10589e7e418f · outbound
Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Preserving Pre-trained Features Helps Calibrate Fine-tuned Language Models
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Observation ff5be32c-c1b9-40da-9360-ab046edb059c · outbound
Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
Reference 17
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Observation 5c778750-e0b3-429c-859e-0ff7ba174de0 · outbound
Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Program-Aided Reasoners (better) Know What They Know
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Observation 87bba128-4d09-42ba-9549-726fc78a78b1 · outbound
Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Language Models (Mostly) Know What They Know
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Observation 40c41778-a9c3-439d-a34e-ef9f44e289e7 · outbound
Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work
Reference 20
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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Large Language Models are Zero-Shot Reasoners
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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work
Reference 22
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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Symbolic Chain-of-Thought Distillation: Small Models Can Also "Think" Step-by-Step
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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Explanations from Large Language Models Make Small Reasoners Better
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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work
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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Teaching Small Language Models to Reason
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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work
Reference 27
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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work
Reference 28
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Observation ac63deaf-e776-4267-8881-5356e894b10f · outbound
Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Orca 2: Teaching Small Language Models How to Reason
Reference 29
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Observation d5280896-2f0b-4ca6-afa9-c0a13888ff92 · outbound
Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Orca: Progressive Learning from Complex Explanation Traces of GPT-4
Reference 30
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Observation 60471de3-a638-4f6a-b7ac-bade45924015 · outbound
Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work
Reference 31
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Observation 4429db8d-9897-4509-ac80-c4e8c2f14e0e · outbound
Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work
Reference 32
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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Posterior calibration and exploratory analysis for natural language processing models
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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Adversarial NLI: A New Benchmark for Natural Language Understanding
Reference 34
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Observation 79fd8426-6614-4db8-bae0-9f1c6b8b9c99 · outbound
Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work
Reference 35
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Observation 1daaaa3f-9a28-40db-8c6b-1d23294a8688 · outbound
Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Show Your Work: Scratchpads for Intermediate Computation with Language Models
Reference 36
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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability CREAK: A Dataset for Commonsense Reasoning over Entity Knowledge
Reference 37
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Observation 9c942df4-bfc7-49a0-960f-2d0900d8cf93 · outbound
Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability GPT-4 Technical Report
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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work
Reference 39
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Observation 78315454-865e-47d5-85c2-8a506a3bdceb · outbound
Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work
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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Are NLP Models really able to Solve Simple Math Word Problems?
Reference 41
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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations
Reference 42
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Observation 65f43137-fc52-4c53-bd1e-8a217e64d890 · outbound
Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work
Reference 43
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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability WinoGrande: An Adversarial Winograd Schema Challenge at Scale
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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Distilling Reasoning Capabilities into Smaller Language Models
Reference 45
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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
Reference 46
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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Manning, Andrew Ng, and Christopher Potts
Reference 47
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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability ConceptNet 5.5: An Open Multilingual Graph of General Knowledge
Reference 48
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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning
Reference 49
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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge
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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Llama 2: Open Foundation and Fine-Tuned Chat Models
Reference 51
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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems
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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability PINTO: Faithful Language Reasoning Using Prompt-Generated Rationales
Reference 53
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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Self-Consistency Improves Chain of Thought Reasoning in Language Models
Reference 54
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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Are Human Explanations Always Helpful? Towards Objective Evaluation of Human Natural Language Explanations
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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work
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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work
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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work
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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability PAWS: Paraphrase Adversaries from Word Scrambling
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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work
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Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability online" 'onlinestring :=
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