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Paper Citation Record · LEDGER

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability

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.

pith.paper-citation-record.v1
2505.24147 v1

Coverage vector

measured 63 of 63 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:40:26.743983Z

measured 64 of 64 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T19:34:29.377709Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

63 of 63 outbound references displayed

  • verified exact1
  • verified fuzzy0
  • unresolved62
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9a44730b-7cb5-4230-966a-77be09148183 · outbound

This paper cites e-SNLI: Natural Language Inference with Natural Language Explanations.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability e-SNLI: Natural Language Inference with Natural Language Explanations

Reference 1

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source=arxiv_source observed=2026-08-07T12:40:19.931343Z digest=sha256:8e51dcf437ea2f9d236780ae4541d393c8f40bc5777f8dc9da81f72e2aa6e963

Observation 28d0f066-cc60-472f-9c0d-2ef3ed4c4ee9 · outbound

This paper cites What to Learn, and How: Toward Effective Learning from Rationales.

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

Reference 2

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source=arxiv_source observed=2026-08-07T12:40:20.075543Z digest=sha256:b57c36af39bde37f1979494d8886d3690530c1f41db7888d96e6d4a622356a1d

Observation b611289b-cd53-4d9f-a96c-9463267bb6f7 · outbound

This paper cites Scaling Instruction-Finetuned Language Models.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Scaling Instruction-Finetuned Language Models

Reference 3

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source=arxiv_source observed=2026-08-07T12:40:20.235803Z digest=sha256:9f9d7197373358851ba9547f77bcadaa49361e0d1a6e31acdd3500ece920f7af

Observation 161dc58f-b8ea-435b-a4cf-80d4c290baf4 · outbound

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

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

Reference 4

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source=arxiv_source observed=2026-08-07T12:40:20.376449Z digest=sha256:398b850c636dfe37348e69fa878ae89b293b0e911759cc18346cefff57f42213

Observation 561e22a8-88ba-42ab-b917-6760969e44fa · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Training Verifiers to Solve Math Word Problems

Reference 5

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source=arxiv_source observed=2026-08-07T12:40:20.478762Z digest=sha256:934ee43fcb79ab5863ccd56466f27fb3a3f7379bd876d8ee00da13a57fbbdd4b

Observation 9b199d19-f52f-4282-bdf3-034b538f339a · outbound

This paper cites an unresolved cited work.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work

Reference 6

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Observation 80f18ef8-7ff6-4e65-a87e-96e0cadb69c5 · outbound

This paper cites Calibration of Pre-trained Transformers.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Calibration of Pre-trained Transformers

Reference 7

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source=arxiv_source observed=2026-08-07T12:40:20.670222Z digest=sha256:66a2632ad5d94f9ee0c9be8764aeea2db651c54efbd6d3ca9ac4c675d1bd3256

Observation c2a9bc4f-3040-4ad6-8cef-9b049892a534 · outbound

This paper cites an unresolved cited work.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work

Reference 8

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Observation 38c17928-dfe3-4ca3-a413-5c551fbef8a9 · outbound

This paper cites an unresolved cited work.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work

Reference 9

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Observation 32f8bb84-2b67-49b2-8150-9fde9ec8e34c · outbound

This paper cites an unresolved cited work.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work

Reference 10

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Observation a69755ad-827a-4e21-8dd7-3121394fd948 · outbound

This paper cites Understanding Dataset Difficulty with $\mathcal{V}$-Usable Information.

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

This paper cites Specializing Smaller Language Models towards Multi-Step Reasoning.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Specializing Smaller Language Models towards Multi-Step Reasoning

Reference 12

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Observation 5b86da8c-5c8e-4bef-aadb-2c20b1c4aaef · outbound

This paper cites an unresolved cited work.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work

Reference 13

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Observation e4d1d33d-c773-4b3c-ba0d-be347a81914f · outbound

This paper cites On Calibration of Modern Neural Networks.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability On Calibration of Modern Neural Networks

Reference 14

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Observation f0f9e274-45f8-477e-8f7d-b3b34bb95c2b · outbound

This paper cites an unresolved cited work.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work

Reference 15

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Observation 6a428f17-441c-4495-b920-10589e7e418f · outbound

This paper cites Preserving Pre-trained Features Helps Calibrate Fine-tuned Language Models.

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

Reference 16

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Observation ff5be32c-c1b9-40da-9360-ab046edb059c · outbound

This paper cites A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks.

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

This paper cites Program-Aided Reasoners (better) Know What They Know.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Program-Aided Reasoners (better) Know What They Know

Reference 18

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Observation 87bba128-4d09-42ba-9549-726fc78a78b1 · outbound

This paper cites Language Models (Mostly) Know What They Know.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Language Models (Mostly) Know What They Know

Reference 19

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Observation 40c41778-a9c3-439d-a34e-ef9f44e289e7 · outbound

This paper cites an unresolved cited work.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work

Reference 20

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Observation e9521933-7c8e-4969-844a-3c985b928b30 · outbound

This paper cites Large Language Models are Zero-Shot Reasoners.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Large Language Models are Zero-Shot Reasoners

Reference 21

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Observation b1324cd2-e53e-4e28-a639-d7ba67a5c294 · outbound

This paper cites an unresolved cited work.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work

Reference 22

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Observation f999883d-599e-44fc-b4ce-a812673cec89 · outbound

This paper cites Symbolic Chain-of-Thought Distillation: Small Models Can Also "Think" Step-by-Step.

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

Reference 23

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Observation 24ab85c0-e912-44e2-93c3-5e39193b1ccd · outbound

This paper cites Explanations from Large Language Models Make Small Reasoners Better.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Explanations from Large Language Models Make Small Reasoners Better

Reference 24

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Observation 0396449b-6fd5-4a27-b5a3-2a55086dec67 · outbound

This paper cites an unresolved cited work.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work

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Observation 6d6a7199-fcda-42f4-99fb-77b23b6d8f91 · outbound

This paper cites Teaching Small Language Models to Reason.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Teaching Small Language Models to Reason

Reference 26

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Observation e86b44f5-654f-4c75-96ba-7f0641dd71f3 · outbound

This paper cites an unresolved cited work.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work

Reference 27

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Observation c3a1828e-e532-4e12-b819-1f19d0b3e3fc · outbound

This paper cites an unresolved cited work.

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

This paper cites Orca 2: Teaching Small Language Models How to Reason.

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

This paper cites Orca: Progressive Learning from Complex Explanation Traces of GPT-4.

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

This paper cites an unresolved cited work.

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

This paper cites an unresolved cited work.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work

Reference 32

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Observation fca23586-df35-4a81-a7b0-d268fe3008de · outbound

This paper cites Posterior calibration and exploratory analysis for natural language processing models.

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

Reference 33

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Observation cdcb8956-cdec-48cc-9ecf-5808c8df91cc · outbound

This paper cites Adversarial NLI: A New Benchmark for Natural Language Understanding.

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

This paper cites an unresolved cited work.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work

Reference 35

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 1daaaa3f-9a28-40db-8c6b-1d23294a8688 · outbound

This paper cites Show Your Work: Scratchpads for Intermediate Computation with Language Models.

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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Observation aee1e717-c13b-424d-9560-f2696fb8047d · outbound

This paper cites CREAK: A Dataset for Commonsense Reasoning over Entity Knowledge.

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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source=arxiv_source observed=2026-08-07T12:40:23.494889Z digest=sha256:44dfabe1d58f6a4cc69cef7413051d66659097992ac3dd8040eabce0a0ac2c5a

Observation 9c942df4-bfc7-49a0-960f-2d0900d8cf93 · outbound

This paper cites GPT-4 Technical Report.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability GPT-4 Technical Report

Reference 38

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source=arxiv_source observed=2026-08-07T12:40:23.653922Z digest=sha256:e326b580ed23593c5d6c43b7c204220ac2070f0acf7b5a8210e2ca97832214f5

Observation 38047f34-8511-4af7-a79c-3aa3e0647524 · outbound

This paper cites an unresolved cited work.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work

Reference 39

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raw_fallback, observed 2026-08-07T12:40:27.665972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T12:40:23.774847Z digest=sha256:a3c7248ebe0ed51323e092f5b9949fc21c0d6d180a746588b4261d02e14143d8

Observation 78315454-865e-47d5-85c2-8a506a3bdceb · outbound

This paper cites an unresolved cited work.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work

Reference 40

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source=arxiv_source observed=2026-08-07T12:40:23.909309Z digest=sha256:3159d137d315da4f3d8ab2487948aad93d20e30bfc5f2f0fe2a06c272a3a021a

Observation b02de5f7-6bdf-4022-bd79-3fc8e6f1fc5d · outbound

This paper cites Are NLP Models really able to Solve Simple Math Word Problems?.

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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source=arxiv_source observed=2026-08-07T12:40:24.048605Z digest=sha256:1f492e8ffca06353f0fe395ed4cf187085d020f03706a4c8c01c5b003daf45c7

Observation 4bafe963-8524-44d5-8505-30bc59749d11 · outbound

This paper cites WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations.

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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source=arxiv_source observed=2026-08-07T12:40:24.207704Z digest=sha256:30c6a7862a1a87d997150d143584613b274e2289367036d3299efd32b92da8a3

Observation 65f43137-fc52-4c53-bd1e-8a217e64d890 · outbound

This paper cites an unresolved cited work.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work

Reference 43

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source=arxiv_source observed=2026-08-07T12:40:24.416142Z digest=sha256:e41052a2471d8cac0210f7440abdd2551054942848509b1fa719f2feb6f7adb4

Observation 54cbe89a-4503-4799-96db-12f820cdfc4e · outbound

This paper cites WinoGrande: An Adversarial Winograd Schema Challenge at Scale.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability WinoGrande: An Adversarial Winograd Schema Challenge at Scale

Reference 44

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source=arxiv_source observed=2026-08-07T12:40:24.573184Z digest=sha256:c50df96fb9310a97984b5f189403b2cc39ed34457720a86eeb46425607493b99

Observation 257dc47b-fe8a-44c9-a2f9-2b9ff886e6f3 · outbound

This paper cites Distilling Reasoning Capabilities into Smaller Language Models.

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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source=arxiv_source observed=2026-08-07T12:40:24.689688Z digest=sha256:94a16e8ee071de6d194b8b02b16a845b17b7a65958eedec5416477ba62ad512b

Observation a5c47a0b-e2c9-4d6b-8fec-911624b38379 · outbound

This paper cites Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters.

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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source=arxiv_source observed=2026-08-07T12:40:24.830152Z digest=sha256:3808303f1bffe1fea3f663bda29ff90762e938fbcc95bfaba5ee22da634fb86d

Observation 00570187-611f-4a24-8219-5899ed7363f1 · outbound

This paper cites Manning, Andrew Ng, and Christopher Potts.

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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source=arxiv_source observed=2026-08-07T12:40:24.930772Z digest=sha256:59f815fcae171d9408aa68b04215facf3bf2d5a4fae2ff3793540085b729f433

Observation 095d3917-8746-411d-af70-811b98c2edc7 · outbound

This paper cites ConceptNet 5.5: An Open Multilingual Graph of General Knowledge.

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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no resolver link, observed 2026-08-07T12:40:25.039599Z

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source=arxiv_source observed=2026-08-07T12:40:25.039599Z digest=sha256:0354d88ad02484172a316102d038d5bb7c55b024d14d1595de2882b3775deaaa

Observation 12763add-803e-4d01-b0af-2d0cc0ac8fa8 · outbound

This paper cites To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning.

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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source=arxiv_source observed=2026-08-07T12:40:25.116324Z digest=sha256:2e7539b708027b4eca93dcdb8ea57219c44b5f59640016879b8bed44bf0af97f

Observation b27329ce-cb66-49f2-9255-abf8fc2f0679 · outbound

This paper cites CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge

Reference 50

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source=arxiv_source observed=2026-08-07T12:40:25.216295Z digest=sha256:e79ffe2cc37c8179bfcde5df78b88f0e1f34f754f4cd94ef0d40df34b02ca2c7

Observation 2d2074c2-1477-4ada-9528-de233adf634f · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

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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source=arxiv_source observed=2026-08-07T12:40:25.323189Z digest=sha256:f9094887cb16d61eb03ee11044128e07b68fa4c20d88f8f05e9214ba07ee6a9a

Observation 4dcbe3fe-33e6-4846-9bad-65b86dd06fa6 · outbound

This paper cites SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems.

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

Reference 52

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source=arxiv_source observed=2026-08-07T12:40:25.533618Z digest=sha256:b581adebda16a2ae3070eb4c3e9990514ea9c0cebe13c1df0099efe5d01ecb9a

Observation 462cfb3a-0f95-4934-9d1a-ff179df4481a · outbound

This paper cites PINTO: Faithful Language Reasoning Using Prompt-Generated Rationales.

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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source=arxiv_source observed=2026-08-07T12:40:25.631368Z digest=sha256:bfcefdf981e6d5dd4737d84fde0a447c78161f38d92ffd32c57224662180bb00

Observation dcc9c0e4-4c24-4f64-a5d2-4dd864de52b9 · outbound

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

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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no resolver link, observed 2026-08-07T12:40:25.760065Z

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source=arxiv_source observed=2026-08-07T12:40:25.760065Z digest=sha256:e783f4faab14f9728c9433f3e02a3cc78be3c79561b324baf6482cac404d1b5d

Observation 7ecdca65-0108-400a-b15a-f9b1e5d7e4c8 · outbound

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

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

Reference 55

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source=arxiv_source observed=2026-08-07T12:40:25.907355Z digest=sha256:fa41484c0ef664b8c43b8f32e1e051936f249d02b8a8194629519f5b17a3cb16

Observation 40dd2405-2a69-490a-b677-efd1ac8a51fc · outbound

This paper cites Are Human Explanations Always Helpful? Towards Objective Evaluation of Human Natural Language Explanations.

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

Reference 56

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verified exact
local_arxiv, observed 2026-08-07T12:40:27.015667Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T12:40:25.997416Z digest=sha256:c86b95daf0b9e7dd9b1f088c806113e979809dc701cf4506c261dac261309aeb

Observation b536e245-d0fa-497e-adb3-06c316f44fdd · outbound

This paper cites an unresolved cited work.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work

Reference 57

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no resolver link, observed 2026-08-07T12:40:26.067413Z

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source=arxiv_source observed=2026-08-07T12:40:26.067413Z digest=sha256:c7e80cc4eb13600218464cc8f9eeea35e5c9268d2546ed239c6584cb1659ca91

Observation 6690da1f-68b7-4a73-892a-147b45a4e27e · outbound

This paper cites an unresolved cited work.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work

Reference 58

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:40:26.195150Z digest=sha256:d243d49af83746c019cc5b30b1e3336da769e0b967806e43feb2ff616662b438

Observation d0dbb333-3f45-44e5-b7b9-b5790291ec42 · outbound

This paper cites an unresolved cited work.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work

Reference 59

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no resolver link, observed 2026-08-07T12:40:26.350229Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:40:26.350229Z digest=sha256:2896d9652ef3e3911b27918b7053f7f13fb743b05b0379ac1596cefbc9164194

Observation d0fb600b-51b0-4f95-a85a-c1f343441d17 · outbound

This paper cites PAWS: Paraphrase Adversaries from Word Scrambling.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability PAWS: Paraphrase Adversaries from Word Scrambling

Reference 60

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no resolver link, observed 2026-08-07T12:40:26.482188Z

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source=arxiv_source observed=2026-08-07T12:40:26.482188Z digest=sha256:67dbf3d188025ac2c6ca0ad300190a814fec841a4e1e76b7dc42e0b1e8449837

Observation fab1b627-0bfa-4555-9478-4047dc0cb1a6 · outbound

This paper cites an unresolved cited work.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Unresolved cited work

Reference 61

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no resolver link, observed 2026-08-07T12:40:26.595287Z

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source=arxiv_source observed=2026-08-07T12:40:26.595287Z digest=sha256:e760358090abc407603c5b01b143edf02866f894ec82bb6fa4fc8e3c6ffa71f2

Observation 0be44cce-6a99-4ae4-8fb1-142526a1010d · outbound

This paper cites online" 'onlinestring :=.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability online" 'onlinestring :=

Reference 62

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source=arxiv_source observed=2026-08-07T12:40:26.667293Z digest=sha256:24949c5edc8abd8fca46add6b5cd9467bedc79ac04eebd907b71555a802303df

Observation d084c7b1-cc74-4273-b5d3-a193c4244004 · outbound

This paper cites write newline.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability write newline

Reference 63

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no resolver link, observed 2026-08-07T12:40:26.743983Z

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source=arxiv_source observed=2026-08-07T12:40:26.743983Z digest=sha256:51991613e1717b384778ad344a93cf8d254daf17d8e124798e052b1fb5c56384

Pith citing papers

Observation b57b68c9-4876-41b3-a345-711e53062310 · inbound

AI Reasoning for Wireless Communications and Networking: A Survey and Perspectives cites this paper.

AI Reasoning for Wireless Communications and Networking: A Survey and Perspectives Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability

Reference 77

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no resolver link, observed 2026-08-04T19:34:29.377709Z

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source=pdf_text observed=2026-08-04T19:34:29.377709Z digest=sha256:3b11a47a4c9e5ef282ebe7dfca7e376808f28d4d3176702bc5d8cfa72b0dec13