Paper Citation Record · LEDGER

Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai

As of 23 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2411.15484.

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

pith.paper-citation-record.v1
2411.15484 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T14:19:24.878747Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+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

25 of 25 outbound references displayed

verified exact0
verified fuzzy7
unresolved17
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e00442f0-de60-4aa1-8571-364717310630 · outbound

This paper cites SambaLingo: Teaching Large Language Models New Languages.

Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai SambaLingo: Teaching Large Language Models New Languages

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-12T14:19:24.764296Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6789c6c-f8f5-47cd-bb35-4a75d40d1959 · outbound

This paper cites In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 3029–3051, Singapore.

Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 3029–3051, Singapore

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:19:25.272550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 53879705-d94e-4c79-96f3-a4728a9e4047 · outbound

This paper cites MoDS: Model-oriented Data Selection for Instruction Tuning.

Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai MoDS: Model-oriented Data Selection for Instruction Tuning

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T14:19:24.774982Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5b27a6f9-8628-43ff-a85f-0a971db5a9d3 · outbound

This paper cites From LLM to NMT: Advancing Low-Resource Machine Translation with Claude.

Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai From LLM to NMT: Advancing Low-Resource Machine Translation with Claude

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T14:19:24.780642Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a90beab7-f891-45e1-a036-7971cf5035ea · outbound

This paper cites In Findings of the As- sociation for Computational Linguistics: EMNLP 2023, pages 693–703, Singapore.

Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai In Findings of the As- sociation for Computational Linguistics: EMNLP 2023, pages 693–703, Singapore

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:19:25.255098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5a573eef-1220-4d41-90b2-8c7b55ed3004 · outbound

This paper cites In Findings of the Association for Com- putational Linguistics: EMNLP 2023, pages 12365– 12394, Singapore.

Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai In Findings of the Association for Com- putational Linguistics: EMNLP 2023, pages 12365– 12394, Singapore

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:19:25.237668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e41a716f-3739-4aaf-baa2-346e0ca0ce7e · outbound

This paper cites Watch Your Language: Investigating Content Moderation with Large Language Models.

Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai Watch Your Language: Investigating Content Moderation with Large Language Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T14:19:24.796639Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 826c3200-6c6b-496d-8e04-4463208f9ed1 · outbound

This paper cites A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity.

Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T14:19:24.802647Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 52635de5-0b81-4be7-8a4b-6f4a6e7f814c · outbound

This paper cites Eureka: Human-Level Reward Design via Coding Large Language Models.

Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai Eureka: Human-Level Reward Design via Coding Large Language Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T14:19:24.808542Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c51e138-e74b-41a5-9afc-bbe9b391e0c4 · outbound

This paper cites SeaLLMs -- Large Language Models for Southeast Asia.

Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai SeaLLMs -- Large Language Models for Southeast Asia

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T14:19:24.813951Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6139071-8860-4c50-9e0e-fbbb55b1321b · outbound

This paper cites GPT-4 Technical Report.

Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai GPT-4 Technical Report

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-12T14:19:24.820133Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1595b964-ab38-4dcc-ad50-cf2456b136f9 · outbound

This paper cites WangchanLion and WangchanX MRC Eval.

Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai WangchanLion and WangchanX MRC Eval

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T14:19:24.825966Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e912cc9f-a8fc-46a3-8001-97eb5bcf5239 · outbound

This paper cites Typhoon: Thai Large Language Models.

Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai Typhoon: Thai Large Language Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T14:19:24.831120Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d91c6ed3-6955-468c-ac68-ff86ec8144b2 · outbound

This paper cites In Proceedings of the First Workshop on Patient-Oriented Language Pro- cessing (CL4Health) @ LREC-COLING 2024, pages 124–130, Torino, Italia.

Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai In Proceedings of the First Workshop on Patient-Oriented Language Pro- cessing (CL4Health) @ LREC-COLING 2024, pages 124–130, Torino, Italia

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:19:25.220721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a1b79abc-757d-422a-893e-04268e6cd960 · outbound

This paper cites In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 1941–1961, Singapore.

Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 1941–1961, Singapore

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:19:25.203944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f0ed2940-5a0c-417d-b877-f6234b84018c · outbound

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

Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai Gemini: A Family of Highly Capable Multimodal Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-12T14:19:24.846881Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa48736a-fcb9-4693-87da-68b5b7c5c5da · outbound

This paper cites Self-Instruct: Aligning Language Models with Self-Generated Instructions.

Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai Self-Instruct: Aligning Language Models with Self-Generated Instructions

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T14:19:24.857946Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49219387-37eb-4bb6-a27f-ba7b386abdb9 · outbound

This paper cites WizardLM: Empowering large pre-trained language models to follow complex instructions.

Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai WizardLM: Empowering large pre-trained language models to follow complex instructions

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-12T14:19:24.863719Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24dcdebb-d9c8-495b-b2d7-51b996158bce · outbound

This paper cites MlingConf: A Comprehensive Study of Multilingual Confidence Estimation on Large Language Models.

Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai MlingConf: A Comprehensive Study of Multilingual Confidence Estimation on Large Language Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-12T14:19:24.869107Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ddcad77-e98d-4f4b-930a-8210cff89cfa · outbound

This paper cites In Proceedings of the 2023 Conference on Empirical Methods in Natu- ral Language Processing, pages 7915–7927, Singa- pore.

Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai In Proceedings of the 2023 Conference on Empirical Methods in Natu- ral Language Processing, pages 7915–7927, Singa- pore

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:19:25.186730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 167b625b-0865-4a04-a8fb-e19620b65076 · outbound

This paper cites LIMA: Less Is More for Alignment.

Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai LIMA: Less Is More for Alignment

Reference 25

Resolution: malformed identifier; no resolver link, observed 2026-08-12T14:19:24.878747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 979baa38-4f44-4c52-a8bd-ee6bc09e70ad · outbound

This paper cites an unresolved cited work.

Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai Unresolved cited work

Reference 2019

Resolution: unresolved; raw_fallback, observed 2026-08-12T14:19:25.305434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6d9232fb-e918-4b98-9a45-a5783fe511a8 · outbound

This paper cites No Language Left Behind: Scaling Human-Centered Machine Translation.

Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai No Language Left Behind: Scaling Human-Centered Machine Translation

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-12T14:19:24.852629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5fbd0f1-23a5-42c1-9331-b8fa8d606f64 · outbound

This paper cites In Proceedings of the 2023 Conference on Empir- ical Methods in Natural Language Processing, pages 4232–4267, Singapore.

Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai In Proceedings of the 2023 Conference on Empir- ical Methods in Natural Language Processing, pages 4232–4267, Singapore

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:19:25.290224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c3892b82-6df8-4a13-938e-10c929315e32 · outbound

This paper cites M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation.

Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-12T14:19:24.758805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.