Paper Citation Record · LEDGER

Affordably Fine-tuned LLMs Provide Better Answers to Course-specific MCQs

As of 21 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2501.05891.

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

pith.paper-citation-record.v1
2501.05891 v2

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:10:10.996713Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

29 of 29 outbound references displayed

verified exact2
verified fuzzy1
unresolved26
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5f40ed84-4090-420a-8094-bf70d962ad4e · outbound

This paper cites an unresolved cited work.

Affordably Fine-tuned LLMs Provide Better Answers to Course-specific MCQs Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-10T21:10:11.333680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 563f671c-e81c-4388-8c1c-1abfaf7093da · outbound

This paper cites 8-bit Optimizers via Block-wise Quantization.

Affordably Fine-tuned LLMs Provide Better Answers to Course-specific MCQs 8-bit Optimizers via Block-wise Quantization

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:10.889603Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ffb8074a-aa6d-4268-8c62-5a528377ba0e · outbound

This paper cites QLoRA: Efficient Finetuning of Quantized LLMs.

Affordably Fine-tuned LLMs Provide Better Answers to Course-specific MCQs QLoRA: Efficient Finetuning of Quantized LLMs

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:10.893596Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8cc6b30c-8cd4-4732-9f19-1181912ed797 · outbound

This paper cites an unresolved cited work.

Affordably Fine-tuned LLMs Provide Better Answers to Course-specific MCQs Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-10T21:10:11.322844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e2c07974-bd1e-4daf-b3c9-00cf18778b06 · outbound

This paper cites an unresolved cited work.

Affordably Fine-tuned LLMs Provide Better Answers to Course-specific MCQs Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-10T21:10:11.310798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4d111968-17cb-40ef-96eb-ab77df0fd761 · outbound

This paper cites an unresolved cited work.

Affordably Fine-tuned LLMs Provide Better Answers to Course-specific MCQs Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-10T21:10:11.299639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 087e6d4a-17f0-4fc0-9482-d5e9d4712cf5 · outbound

This paper cites Deep Learning Scaling is Predictable, Empirically.

Affordably Fine-tuned LLMs Provide Better Answers to Course-specific MCQs Deep Learning Scaling is Predictable, Empirically

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:10.910115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d9d006b-aa98-4505-aae0-7521a069919f · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Affordably Fine-tuned LLMs Provide Better Answers to Course-specific MCQs LoRA: Low-Rank Adaptation of Large Language Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:10.914038Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ab70a4a-8aba-4711-92cd-c005b02180aa · outbound

This paper cites Mixtral of Experts.

Affordably Fine-tuned LLMs Provide Better Answers to Course-specific MCQs Mixtral of Experts

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:10.918438Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation acaaac17-3b36-4020-a0cd-52e959f46956 · outbound

This paper cites an unresolved cited work.

Affordably Fine-tuned LLMs Provide Better Answers to Course-specific MCQs Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-08-10T21:10:11.288895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d863e1c4-f048-4b0f-af39-8a83cc9bc25e · outbound

This paper cites A Study of BFLOAT16 for Deep Learning Training.

Affordably Fine-tuned LLMs Provide Better Answers to Course-specific MCQs A Study of BFLOAT16 for Deep Learning Training

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:10.927898Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf87f588-c450-4f58-98c6-8437881274d4 · outbound

This paper cites an unresolved cited work.

Affordably Fine-tuned LLMs Provide Better Answers to Course-specific MCQs Unresolved cited work

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:10.932719Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d544d831-97ba-4a74-9616-44b0d3cd6bad · outbound

This paper cites an unresolved cited work.

Affordably Fine-tuned LLMs Provide Better Answers to Course-specific MCQs Unresolved cited work

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:10.936479Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a47ffae-0d8a-42af-a68a-66ba7a6cceaf · outbound

This paper cites an unresolved cited work.

Affordably Fine-tuned LLMs Provide Better Answers to Course-specific MCQs Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-10T21:10:11.264398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 94894763-2b35-4c58-a4fe-61f5ef27a047 · outbound

This paper cites Capabilities of GPT-4 on Medical Challenge Problems.

Affordably Fine-tuned LLMs Provide Better Answers to Course-specific MCQs Capabilities of GPT-4 on Medical Challenge Problems

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:10.943440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6fbf4880-09e8-4e1f-9845-26011a5771be · outbound

This paper cites The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities.

Affordably Fine-tuned LLMs Provide Better Answers to Course-specific MCQs The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:10.947084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 70ff4e3b-46bd-4267-8323-1c296180bc04 · outbound

This paper cites Large Language Models Sensitivity to The Order of Options in Multiple-Choice Questions.

Affordably Fine-tuned LLMs Provide Better Answers to Course-specific MCQs Large Language Models Sensitivity to The Order of Options in Multiple-Choice Questions

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:10.951080Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91013b73-ae43-44a7-a04d-334f8180bd51 · outbound

This paper cites an unresolved cited work.

Affordably Fine-tuned LLMs Provide Better Answers to Course-specific MCQs Unresolved cited work

Reference 18

Resolution: unresolved; raw_fallback, observed 2026-08-10T21:10:11.252227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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

This paper cites Leveraging Large Language Models for Multiple Choice Question Answering.

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 a86fa1c8-04a0-4dd9-9cd0-fa355abbbaf9 · outbound

This paper cites Large Language Models (GPT) Struggle to Answer Multiple-Choice Questions about Code.

Affordably Fine-tuned LLMs Provide Better Answers to Course-specific MCQs Large Language Models (GPT) Struggle to Answer Multiple-Choice Questions about Code

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-10T21:10:11.089771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d483b34a-13d5-4e7f-99ec-9d5968fc05ec · outbound

This paper cites an unresolved cited work.

Affordably Fine-tuned LLMs Provide Better Answers to Course-specific MCQs Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-08-10T21:10:11.239495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 26a86632-297e-4327-ade0-f9077857a16a · outbound

This paper cites an unresolved cited work.

Affordably Fine-tuned LLMs Provide Better Answers to Course-specific MCQs Unresolved cited work

Reference 22

Resolution: unresolved; raw_fallback, observed 2026-08-10T21:10:11.228400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6a355adc-9664-4b15-b841-d8f912bb08a3 · outbound

This paper cites Williams.

Affordably Fine-tuned LLMs Provide Better Answers to Course-specific MCQs Williams

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:10:11.216967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 65eca5b7-97c1-49b1-9e69-07ae1522b46d · outbound

This paper cites Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code.

Affordably Fine-tuned LLMs Provide Better Answers to Course-specific MCQs Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:10.981800Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1034b4a1-25ee-4054-85b2-b5f793d8ee17 · outbound

This paper cites Exploring the Cognitive Knowledge Structure of Large Language Models: An Educational Diagnostic Assessment Approach.

Affordably Fine-tuned LLMs Provide Better Answers to Course-specific MCQs Exploring the Cognitive Knowledge Structure of Large Language Models: An Educational Diagnostic Assessment Approach

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-10T21:10:11.054539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d4b2ca3c-dea0-44fb-8ed5-03dbbe93182f · outbound

This paper cites A Survey of Large Language Models.

Affordably Fine-tuned LLMs Provide Better Answers to Course-specific MCQs A Survey of Large Language Models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:10.988985Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca383208-8d25-4ae1-853d-20ffeac67d12 · outbound

This paper cites an unresolved cited work.

Affordably Fine-tuned LLMs Provide Better Answers to Course-specific MCQs Unresolved cited work

Reference 27

Resolution: unresolved; raw_fallback, observed 2026-08-10T21:10:11.205957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7bf7c2f1-3676-49bb-af3d-7ce1b886c388 · outbound

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

Affordably Fine-tuned LLMs Provide Better Answers to Course-specific MCQs Large Language Models Are Not Robust Multiple Choice Selectors

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:10.996713Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0826804a-ea32-435b-a716-dc24de4c7f4d · outbound

This paper cites GPT-4 as an Agronomist Assistant? Answering Agriculture Exams Using Large Language Models.

Affordably Fine-tuned LLMs Provide Better Answers to Course-specific MCQs GPT-4 as an Agronomist Assistant? Answering Agriculture Exams Using Large Language Models

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:10.970346Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.