Paper Citation Record · LEDGER

Boosting LLM via Learning from Data Iteratively and Selectively

As of 16 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 1 inbound Pith citation observation for arXiv:2412.17365.

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

pith.paper-citation-record.v1
2412.17365 v1

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T05:38:36.968415Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T14:13:48.299054Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T14:13:55.156116Z

Reference resolution

19 of 19 outbound references displayed

verified exact1
verified fuzzy2
unresolved15
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation dea4c427-acc5-4573-9b58-f89064dc166e · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Boosting LLM via Learning from Data Iteratively and Selectively Evaluating Large Language Models Trained on Code

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-11T05:38:36.707342Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c50f13a-de21-486a-b239-bc36dcf28681 · outbound

This paper cites Enhancing chat language models by scaling high-quality instructional conversations.

Boosting LLM via Learning from Data Iteratively and Selectively Enhancing chat language models by scaling high-quality instructional conversations

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:38:37.522908Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 66ee1c05-f02e-45c2-af2d-614de20ed494 · outbound

This paper cites The Llama 3 Herd of Models.

Boosting LLM via Learning from Data Iteratively and Selectively The Llama 3 Herd of Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T05:38:36.725214Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ab5d178-59ec-4a6c-bbc1-c530abc6f502 · outbound

This paper cites an unresolved cited work.

Boosting LLM via Learning from Data Iteratively and Selectively Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-11T05:38:37.349665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d2224adf-1eeb-4070-820c-5e68a8fccbdd · outbound

This paper cites Selective Reflection-Tuning: Student-Selected Data Recycling for LLM Instruction-Tuning.

Boosting LLM via Learning from Data Iteratively and Selectively Selective Reflection-Tuning: Student-Selected Data Recycling for LLM Instruction-Tuning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T05:38:36.733063Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0124e764-589f-4e25-8a83-1173690a618e · outbound

This paper cites SelectIT: Selective Instruction Tuning for LLMs via Uncertainty-Aware Self-Reflection.

Boosting LLM via Learning from Data Iteratively and Selectively SelectIT: Selective Instruction Tuning for LLMs via Uncertainty-Aware Self-Reflection

Reference 10

Resolution: metadata mismatch; local_arxiv, observed 2026-08-11T05:38:37.233646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation dbf93492-fea8-4a71-be11-c5e598b06202 · outbound

This paper cites MTEB: Massive Text Embedding Benchmark.

Boosting LLM via Learning from Data Iteratively and Selectively MTEB: Massive Text Embedding Benchmark

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T05:38:36.741434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e7bf1b1-b95f-47d3-9d13-6604753160c3 · outbound

This paper cites Instruction Tuning with GPT-4.

Boosting LLM via Learning from Data Iteratively and Selectively Instruction Tuning with GPT-4

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T05:38:36.813136Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb6d798a-daf5-481a-aac9-c5f7d9185824 · outbound

This paper cites W., Chowdhery, A., Le, Q., Chi, E., Zhou, D., et al.

Boosting LLM via Learning from Data Iteratively and Selectively W., Chowdhery, A., Le, Q., Chi, E., Zhou, D., et al

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:38:37.383544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4bbd1de7-89bb-4245-845b-a3ce80ee060e · outbound

This paper cites Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts.

Boosting LLM via Learning from Data Iteratively and Selectively Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T05:38:36.946910Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15ec9f82-7834-42a4-86cc-02f1d68b9585 · outbound

This paper cites Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks.

Boosting LLM via Learning from Data Iteratively and Selectively Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T05:38:36.959856Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54943a2a-9207-4879-9c2d-3b13c1df7575 · outbound

This paper cites The Best of Both Worlds: Bridging Quality and Diversity in Data Selection with Bipartite Graph.

Boosting LLM via Learning from Data Iteratively and Selectively The Best of Both Worlds: Bridging Quality and Diversity in Data Selection with Bipartite Graph

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-08-11T05:38:37.022829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6c8a38b3-a57a-4f0f-8789-2c22a981b988 · outbound

This paper cites Instruction Mining: Instruction Data Selection for Tuning Large Language Models.

Boosting LLM via Learning from Data Iteratively and Selectively Instruction Mining: Instruction Data Selection for Tuning Large Language Models

Reference 2006

Resolution: unresolved; no resolver link, observed 2026-08-11T05:38:36.697541Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a367526e-15a3-4901-8359-826e59fc0ac4 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Boosting LLM via Learning from Data Iteratively and Selectively Training Verifiers to Solve Math Word Problems

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-11T05:38:36.716507Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 380a5976-f76b-4e3e-aaea-3f1b5d27e457 · outbound

This paper cites Rethinking Data Selection for Supervised Fine-Tuning.

Boosting LLM via Learning from Data Iteratively and Selectively Rethinking Data Selection for Supervised Fine-Tuning

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-11T05:38:36.853572Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4742e01-864e-4fb6-bf66-49649684e893 · outbound

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

Boosting LLM via Learning from Data Iteratively and Selectively Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-11T05:38:36.711724Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eacee8db-6283-492b-acc1-77ff1dd99474 · outbound

This paper cites MTEB: Massive Text Embedding Benchmark.

Boosting LLM via Learning from Data Iteratively and Selectively MTEB: Massive Text Embedding Benchmark

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-11T05:38:36.777241Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf7ef1f6-2c6d-4bc7-845f-05a273af0b08 · outbound

This paper cites AlpaGasus: Training A Better Alpaca with Fewer Data.

Boosting LLM via Learning from Data Iteratively and Selectively AlpaGasus: Training A Better Alpaca with Fewer Data

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-11T05:38:36.702391Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c2a8bbae-4664-4735-98a8-2a84d0b01aed · outbound

This paper cites NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models.

Boosting LLM via Learning from Data Iteratively and Selectively NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-11T05:38:36.729400Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 661e6b99-0dc0-42b8-a46d-46840bce793d · inbound

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning cites this paper.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Boosting LLM via Learning from Data Iteratively and Selectively

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-05T14:13:55.210427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.