Paper Citation Record · LEDGER

Adaptive Few-shot Prompting for Machine Translation with Pre-trained Language Models

As of 19 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 1 inbound Pith citation observation for arXiv:2501.01679.

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

pith.paper-citation-record.v1
2501.01679 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:27:31.343071Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-22T21:38:29.497183Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T21:42:11.231074Z

Reference resolution

24 of 24 outbound references displayed

verified exact0
verified fuzzy3
unresolved20
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 758fac55-33fd-4f65-9f15-26dc62675e6e · outbound

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

Adaptive Few-shot Prompting for Machine Translation with Pre-trained Language Models M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-10T22:27:31.245112Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d2e26c2a-d63f-4a9e-a5a5-42b043fe96e2 · outbound

This paper cites The Llama 3 Herd of Models.

Adaptive Few-shot Prompting for Machine Translation with Pre-trained Language Models The Llama 3 Herd of Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T22:27:31.260466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9eea593-5a12-4022-af32-dff9c00e7349 · outbound

This paper cites The unreasonable effectiveness of few-shot learning for machine translation.

Adaptive Few-shot Prompting for Machine Translation with Pre-trained Language Models The unreasonable effectiveness of few-shot learning for machine translation

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T22:27:31.270675Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5aa6871-1179-471a-a02d-3b6b61bebdf7 · outbound

This paper cites SiLLM: Large Language Models for Simultaneous Machine Translation.

Adaptive Few-shot Prompting for Machine Translation with Pre-trained Language Models SiLLM: Large Language Models for Simultaneous Machine Translation

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T22:27:31.275554Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac71161b-ff1a-4a4d-b453-893eb985060d · outbound

This paper cites How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation.

Adaptive Few-shot Prompting for Machine Translation with Pre-trained Language Models How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T22:27:31.280184Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05032687-f079-4139-af2e-9c9c8f9ebc83 · outbound

This paper cites Is ChatGPT A Good Translator? Yes With GPT-4 As The Engine.

Adaptive Few-shot Prompting for Machine Translation with Pre-trained Language Models Is ChatGPT A Good Translator? Yes With GPT-4 As The Engine

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T22:27:31.285214Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e67b475e-b965-4957-84be-5614ee3ffccf · outbound

This paper cites Understanding the Effects of RLHF on LLM Generalisation and Diversity.

Adaptive Few-shot Prompting for Machine Translation with Pre-trained Language Models Understanding the Effects of RLHF on LLM Generalisation and Diversity

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T22:27:31.290165Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c25b5236-ec58-4ab9-86db-65647424fb17 · outbound

This paper cites Low-Resource Machine Translation through Retrieval-Augmented LLM Prompting: A Study on the Mambai Language.

Adaptive Few-shot Prompting for Machine Translation with Pre-trained Language Models Low-Resource Machine Translation through Retrieval-Augmented LLM Prompting: A Study on the Mambai Language

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T22:27:31.294870Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ebb43deb-17ed-447b-a334-0fe008ace55f · outbound

This paper cites Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine.

Adaptive Few-shot Prompting for Machine Translation with Pre-trained Language Models Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T22:27:31.299391Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 759ded0c-da11-4bb8-83f5-71f25dca4817 · outbound

This paper cites In Koehn, P.; Barrault, L.; Bojar, O.; Bougares, F.; Chatterjee, R.; Costa-juss `a, M.

Adaptive Few-shot Prompting for Machine Translation with Pre-trained Language Models In Koehn, P.; Barrault, L.; Bojar, O.; Bougares, F.; Chatterjee, R.; Costa-juss `a, M

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:27:31.748033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 13fa4754-554b-4a26-8738-718bb169bbb1 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Adaptive Few-shot Prompting for Machine Translation with Pre-trained Language Models Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T22:27:31.313996Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d6ff392-31ee-4bf7-b42d-736054d7cff0 · outbound

This paper cites Prompting PaLM for Translation: Assessing Strategies and Performance.

Adaptive Few-shot Prompting for Machine Translation with Pre-trained Language Models Prompting PaLM for Translation: Assessing Strategies and Performance

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T22:27:31.318820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5ffada5-7754-4339-8f98-06315834dbfc · outbound

This paper cites (Perhaps) Beyond Human Translation: Harnessing Multi-Agent Collaboration for Translating Ultra-Long Literary Texts.

Adaptive Few-shot Prompting for Machine Translation with Pre-trained Language Models (Perhaps) Beyond Human Translation: Harnessing Multi-Agent Collaboration for Translating Ultra-Long Literary Texts

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T22:27:31.323899Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63270dac-4303-44ca-8780-dbebfb962585 · outbound

This paper cites More Samples or More Prompts? Exploring Effective In-Context Sampling for LLM Few-Shot Prompt Engineering.

Adaptive Few-shot Prompting for Machine Translation with Pre-trained Language Models More Samples or More Prompts? Exploring Effective In-Context Sampling for LLM Few-Shot Prompt Engineering

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T22:27:31.328653Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b503432-6c8f-421d-999a-a553352452a9 · outbound

This paper cites In Rogers, A.; Boyd-Graber, J.; and Okazaki, N., eds., Findings of the Association for Computational Linguistics: ACL 2023 , 11518–11533.

Adaptive Few-shot Prompting for Machine Translation with Pre-trained Language Models In Rogers, A.; Boyd-Graber, J.; and Okazaki, N., eds., Findings of the Association for Computational Linguistics: ACL 2023 , 11518–11533

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:27:31.730405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 87d992ea-c301-45db-98c1-81c00919484d · outbound

This paper cites GLM-130B: An Open Bilingual Pre-trained Model.

Adaptive Few-shot Prompting for Machine Translation with Pre-trained Language Models GLM-130B: An Open Bilingual Pre-trained Model

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-10T22:27:31.338128Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04219eaa-e958-432b-99b7-3351b72c3eb8 · outbound

This paper cites In Findings of the Association for Computational Linguistics: NAACL 2024, 2765–2781.

Adaptive Few-shot Prompting for Machine Translation with Pre-trained Language Models In Findings of the Association for Computational Linguistics: NAACL 2024, 2765–2781

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:27:31.712581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e5576480-8675-40de-8d9b-491dc335d4ed · outbound

This paper cites Neural Machine Translation by Jointly Learning to Align and Translate.

Adaptive Few-shot Prompting for Machine Translation with Pre-trained Language Models Neural Machine Translation by Jointly Learning to Align and Translate

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-10T22:27:31.235351Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18a37fcd-90b5-41b6-9772-9a7f27579946 · outbound

This paper cites Neural Machine Translation of Rare Words with Subword Units.

Adaptive Few-shot Prompting for Machine Translation with Pre-trained Language Models Neural Machine Translation of Rare Words with Subword Units

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-10T22:27:31.309105Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6225ab14-8201-4d38-b52e-13d8e4f2ef64 · outbound

This paper cites an unresolved cited work.

Adaptive Few-shot Prompting for Machine Translation with Pre-trained Language Models Unresolved cited work

Reference 2019

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:27:31.763457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3a373f23-7eb2-4554-91e5-7fc9708a6972 · outbound

This paper cites Ladder: A Model-Agnostic Framework Boosting LLM-based Machine Translation to the Next Level.

Adaptive Few-shot Prompting for Machine Translation with Pre-trained Language Models Ladder: A Model-Agnostic Framework Boosting LLM-based Machine Translation to the Next Level

Reference 2021

Resolution: metadata mismatch; local_arxiv, observed 2026-08-10T22:27:31.596276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a7a0344d-4609-49d8-8e4d-5b3d38bd4ea8 · outbound

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

Adaptive Few-shot Prompting for Machine Translation with Pre-trained Language Models No Language Left Behind: Scaling Human-Centered Machine Translation

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-10T22:27:31.250486Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 250d77bf-f180-42e1-9b5a-47c60786f78e · outbound

This paper cites Unleashing the potential of prompt engineering for large language models.

Adaptive Few-shot Prompting for Machine Translation with Pre-trained Language Models Unleashing the potential of prompt engineering for large language models

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-10T22:27:31.240394Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5405e6b9-a21e-4a3a-be7b-82c8027fc410 · outbound

This paper cites Many-Shot In-Context Learning.

Adaptive Few-shot Prompting for Machine Translation with Pre-trained Language Models Many-Shot In-Context Learning

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-10T22:27:31.229759Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 30a913d6-81da-4eb4-b5c0-f16b9d5cbfa4 · inbound

Bridging the Linguistic Divide: A Survey on Leveraging Large Language Models for Machine Translation cites this paper.

Bridging the Linguistic Divide: A Survey on Leveraging Large Language Models for Machine Translation Adaptive Few-shot Prompting for Machine Translation with Pre-trained Language Models

Reference 71

Resolution: verified exact; arxiv_id, observed 2026-05-22T21:42:11.233631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.