Paper Citation Record · LEDGER

Leveraging Large Language Models for Multiple Choice Question Answering

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2210.12353.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2210.12353 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:10:10.958606Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

40
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 88b85214-2d7e-4f28-8d67-d691adc3761f · inbound

Affordably Fine-tuned LLMs Provide Better Answers to Course-specific MCQs cites this paper.

Affordably Fine-tuned LLMs Provide Better Answers to Course-specific MCQs Leveraging Large Language Models for Multiple Choice Question Answering

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:10.958606Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c8d45bf4-cb03-4b7e-b8af-fae0dddd9161 · inbound

Simulating Training Data Leakage in Multiple-Choice Benchmarks for LLM Evaluation cites this paper.

Simulating Training Data Leakage in Multiple-Choice Benchmarks for LLM Evaluation Leveraging Large Language Models for Multiple Choice Question Answering

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T12:35:27.989088Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e3e6299b-8c9c-4ca4-9b08-7e3bcb022348 · inbound

Token Constraint Decoding Improves Robustness on Question Answering for Large Language Models cites this paper.

Token Constraint Decoding Improves Robustness on Question Answering for Large Language Models Leveraging Large Language Models for Multiple Choice Question Answering

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T04:54:44.226947Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49aad555-c701-4ef5-80e8-962f6355f413 · inbound

Enterprise Large Language Model Evaluation Benchmark cites this paper.

Enterprise Large Language Model Evaluation Benchmark Leveraging Large Language Models for Multiple Choice Question Answering

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:33.668975Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44c2033e-480a-40bd-a8a3-b40a7d002e56 · inbound

Adaptive Repetition for Mitigating Position Bias in LLM-Based Ranking cites this paper.

Adaptive Repetition for Mitigating Position Bias in LLM-Based Ranking Leveraging Large Language Models for Multiple Choice Question Answering

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T14:56:03.512819Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96043be3-8fd4-4071-8eeb-9dbad6fd4f1c · inbound

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization cites this paper.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization Leveraging Large Language Models for Multiple Choice Question Answering

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-05T12:43:44.723926Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b4ccc65-c845-4a43-b760-be09404450d1 · inbound

LLMs Struggle with Abstract Meaning Comprehension More Than Expected cites this paper.

LLMs Struggle with Abstract Meaning Comprehension More Than Expected Leveraging Large Language Models for Multiple Choice Question Answering

Reference 16

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T10:16:07.647268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f1d5d2e9-61df-4f2a-a996-14a28cefd91d · inbound

REALISTA: Realistic Latent Adversarial Attacks that Elicit LLM Hallucinations cites this paper.

REALISTA: Realistic Latent Adversarial Attacks that Elicit LLM Hallucinations Leveraging Large Language Models for Multiple Choice Question Answering

Reference 63

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:17:56.366231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.