Paper Citation Record · LEDGER

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering

As of 12 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2412.09807.

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

pith.paper-citation-record.v1
2412.09807 v2

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T16:47:09.505974Z

measured 47 of 47 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 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

47 of 47 outbound references displayed

verified exact1
verified fuzzy11
unresolved35
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e2b9d5fe-18bd-4d53-b5d8-5a89bd645d41 · outbound

This paper cites GPT-4 Technical Report.

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering GPT-4 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-11T16:47:09.278895Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc0ae0e7-98a4-482a-b694-5c3485dc1c8e · outbound

This paper cites On-policy distillation of language models: Learning from self-generated mistakes.

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering On-policy distillation of language models: Learning from self-generated mistakes

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T16:47:09.283845Z

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Observation c759c4f3-9e2c-48e7-9b06-a43c36cb4f0c · outbound

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

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering Gemini: A Family of Highly Capable Multimodal Models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-11T16:47:09.287792Z

Source-reported events for the cited work

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Observation 183fa3a8-af90-4af6-a7ce-b1cf7270ae13 · outbound

This paper cites PaLM 2 Technical Report.

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering PaLM 2 Technical Report

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T16:47:09.291810Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b53c09a4-4729-4fbd-98e3-50c562b9b8ac · outbound

This paper cites Generating questions and multiple-choice answers using semantic analysis of texts.

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering Generating questions and multiple-choice answers using semantic analysis of texts

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:47:10.253673Z

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 5baaeb5b-cc9e-49a9-a084-c6387d0153b7 · outbound

This paper cites Language Models are Few-Shot Learners.

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering Language Models are Few-Shot Learners

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T16:47:09.300987Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a89b87ed-5bde-4dee-a300-5283a9a9df73 · outbound

This paper cites DISCO: Distilling Counterfactuals with Large Language Models.

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering DISCO: Distilling Counterfactuals with Large Language Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T16:47:09.307247Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23917871-ffd9-40d6-9b76-0745c1e73a6c · outbound

This paper cites Chatgpt versus human in generating medical graduate exam multiple choice questions—a multinational prospective study (hong kong sar, singapore, ireland, and the united kingdom).

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering Chatgpt versus human in generating medical graduate exam multiple choice questions—a multinational prospective study (hong kong sar, singapore, ireland, and the united kingdom)

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:47:10.241257Z

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 255c29e6-af0b-4f77-a1a0-49bee9a3c222 · outbound

This paper cites Scaling instruction-finetuned language models.

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering Scaling instruction-finetuned language models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T16:47:09.316408Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20a83bd2-02d8-425a-9032-e9b6841dfe90 · outbound

This paper cites Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions.

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T16:47:09.320208Z

Source-reported events for the cited work

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Observation 069a4207-1985-4e61-9b14-d205e6af74db · outbound

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

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T16:47:09.324073Z

Source-reported events for the cited work

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Observation 042d43ca-2c19-4a7b-85ab-0b017ba458d2 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T16:47:09.327945Z

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Observation 3fedc652-29bb-4caa-9b13-f8b74056ba74 · outbound

This paper cites The Llama 3 Herd of Models.

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering The Llama 3 Herd of Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T16:47:09.331816Z

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Observation 711c5711-608c-41fa-9025-5d428fdf9338 · outbound

This paper cites A Survey of Data Augmentation Approaches for NLP.

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering A Survey of Data Augmentation Approaches for NLP

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T16:47:09.335484Z

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Observation 35ad2235-d23c-422c-8e3e-4b5a591613d3 · outbound

This paper cites Minillm: Knowledge distillation of large language models.

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering Minillm: Knowledge distillation of large language models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T16:47:09.339588Z

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Observation bd1afc28-d43e-4b20-b4bd-6b775f708f95 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering Measuring Massive Multitask Language Understanding

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T16:47:09.343324Z

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Observation 0ac6dea2-49eb-4db4-bc4e-2c9d8043ff70 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering Distilling the Knowledge in a Neural Network

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T16:47:09.347658Z

Source-reported events for the cited work

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Observation cb115b0f-7834-4015-96f5-40bde63d1f48 · outbound

This paper cites What disease does this patient have? a large-scale open domain question answering dataset from medical exams.

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering What disease does this patient have? a large-scale open domain question answering dataset from medical exams

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T16:47:09.351876Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7954287-0765-4e27-b3ac-ae7df1a5ccf6 · outbound

This paper cites Sequence-Level Knowledge Distillation.

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering Sequence-Level Knowledge Distillation

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T16:47:09.357303Z

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Observation 20e37bd9-5a10-4b16-9ea8-c44e1db6b1fa · outbound

This paper cites Adam: A Method for Stochastic Optimization.

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering Adam: A Method for Stochastic Optimization

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T16:47:09.361418Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96d04787-cba4-41bf-a6d2-456c22d2baa2 · outbound

This paper cites Chatgpt prompts for generating multiple-choice questions in medical education and evidence on their validity: a literature review.

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering Chatgpt prompts for generating multiple-choice questions in medical education and evidence on their validity: a literature review

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:47:10.200796Z

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 c97c5f75-4c9c-4e0f-9390-2358802b8892 · outbound

This paper cites Datasets: A Community Library for Natural Language Processing.

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering Datasets: A Community Library for Natural Language Processing

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T16:47:09.370025Z

Source-reported events for the cited work

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Observation 675e0e51-0d13-461a-97a4-6ce7c5a4552a · outbound

This paper cites Self-Alignment with Instruction Backtranslation.

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering Self-Alignment with Instruction Backtranslation

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-11T16:47:09.374343Z

Source-reported events for the cited work

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Observation 48a173d6-d763-4571-af30-f38ff7e027a1 · outbound

This paper cites Distractor generation for multiple choice questions using learning to rank.

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering Distractor generation for multiple choice questions using learning to rank

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:47:10.188002Z

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 5b8221de-dc73-433b-a634-6f275e515bae · outbound

This paper cites D2LLM: Decomposed and Distilled Large Language Models for Semantic Search.

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering D2LLM: Decomposed and Distilled Large Language Models for Semantic Search

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-11T16:47:09.719488Z

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 93296338-a532-4b2e-9ae1-39475a5dc96c · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T16:47:09.386066Z

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Observation 53480295-9a46-4f9a-97f6-ab4dc07af994 · outbound

This paper cites On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey.

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T16:47:09.391170Z

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Observation 58328574-2585-48a3-872a-5abfa79f0f98 · outbound

This paper cites Does label smoothing mitigate label noise? In International Conference on Machine Learning, pp.\ 6448--6458.

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering Does label smoothing mitigate label noise? In International Conference on Machine Learning, pp.\ 6448--6458

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:47:10.177032Z

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 c2229463-0f48-49b1-ad5c-8b512f1d4bc0 · outbound

This paper cites Training language models to follow instructions with human feedback.

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering Training language models to follow instructions with human feedback

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-11T16:47:09.401487Z

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Observation e79b4742-aa78-4e2b-a17e-3f92e5dd4b48 · outbound

This paper cites Leveraging large language models for multiple choice question answering.

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering Leveraging large language models for multiple choice question answering

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:47:10.156091Z

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 fb97d68e-fed6-4dad-8cf0-9f552f9c574f · outbound

This paper cites End-to-end generation of multiple-choice questions using text-to-text transfer transformer models.

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering End-to-end generation of multiple-choice questions using text-to-text transfer transformer models

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:47:10.144074Z

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 8bcb5c5d-818b-4693-8438-80fe65d81bfa · outbound

This paper cites tasksource: A large collection of NLP tasks with a structured dataset preprocessing framework.

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering tasksource: A large collection of NLP tasks with a structured dataset preprocessing framework

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:47:10.130955Z

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 ff41671d-2410-4563-9daa-f87faad81a1c · outbound

This paper cites Automatic generation of multiple choice questions using wikipedia.

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering Automatic generation of multiple choice questions using wikipedia

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:47:10.117993Z

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 c31949c6-b093-41fe-a35e-4372191d91be · outbound

This paper cites Rethinking the inception architecture for computer vision.

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering Rethinking the inception architecture for computer vision

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-11T16:47:09.425586Z

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Observation 430c5bec-e74c-4a41-bd41-834f31761677 · outbound

This paper cites Gemma: Open Models Based on Gemini Research and Technology.

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering Gemma: Open Models Based on Gemini Research and Technology

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-11T16:47:09.430426Z

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Observation 6032928c-ba49-4caa-9c1e-77bbe023ce1c · outbound

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

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering LLaMA: Open and Efficient Foundation Language Models

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-11T16:47:09.434340Z

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Observation bd08b2bd-e5bf-4fb5-8945-5ec64550e085 · outbound

This paper cites Attention is all you need.

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering Attention is all you need

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-11T16:47:09.439085Z

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Unavailable: canonical work link unavailable.

Observation 4de23236-ef01-4069-b262-9fad47e5d917 · outbound

This paper cites Crowdsourcing Multiple Choice Science Questions.

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering Crowdsourcing Multiple Choice Science Questions

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-11T16:47:09.443481Z

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Observation 137179cf-49ea-4542-a9e8-a162873a7bbc · outbound

This paper cites Transformers: State-of-the-art natural language processing.

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering Transformers: State-of-the-art natural language processing

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:47:10.087291Z

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 e2e272d1-1eef-4c0d-9590-cb4f15b81101 · outbound

This paper cites A Survey on Knowledge Distillation of Large Language Models.

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering A Survey on Knowledge Distillation of Large Language Models

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-11T16:47:09.458652Z

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Unavailable: canonical work link unavailable.

Observation 03cbcc9c-9b6e-423c-811e-0614db246965 · outbound

This paper cites Genie: Achieving Human Parity in Content-Grounded Datasets Generation.

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering Genie: Achieving Human Parity in Content-Grounded Datasets Generation

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-11T16:47:09.464733Z

Source-reported events for the cited work

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Observation fad49b37-1cc7-49d7-803f-c76531e4457c · outbound

This paper cites Enhancing Distractor Generation for Multiple-Choice Questions with Retrieval Augmented Pretraining and Knowledge Graph Integration.

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering Enhancing Distractor Generation for Multiple-Choice Questions with Retrieval Augmented Pretraining and Knowledge Graph Integration

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-11T16:47:09.469941Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41643ad8-0295-4acb-9896-41cc63b3401c · outbound

This paper cites When does pretraining help? assessing self-supervised learning for law and the casehold dataset of 53,000+ legal holdings.

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering When does pretraining help? assessing self-supervised learning for law and the casehold dataset of 53,000+ legal holdings

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:47:10.073506Z

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 0ae691e2-e300-4723-b1f1-7767684adc48 · outbound

This paper cites write newline.

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering write newline

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-11T16:47:09.483402Z

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Observation 81b0edfd-c307-4650-9f2b-c03d188cb10d · outbound

This paper cites @esa (Ref.

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering @esa (Ref

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-11T16:47:09.490008Z

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Unavailable: canonical work link unavailable.

Observation 1fb76584-20c2-4e38-9c24-b933f1cc9e04 · outbound

This paper cites an unresolved cited work.

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering Unresolved cited work

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-11T16:47:09.496931Z

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Unavailable: canonical work link unavailable.

Observation bd6c359d-231c-4e6e-9377-6f465ece269a · outbound

This paper cites an unresolved cited work.

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering Unresolved cited work

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-11T16:47:09.505974Z

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Pith citing papers

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