Paper Citation Record · LEDGER

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities

As of 24 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 2 inbound Pith citation observations for arXiv:2508.01290.

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

pith.paper-citation-record.v1
2508.01290 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T05:49:49.946124Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T14:56:36.123251Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-15T14:56:36.181773Z

Reference resolution

48 of 48 outbound references displayed

verified exact0
verified fuzzy29
unresolved18
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 73dfce31-bf6c-4a08-9b12-7e350f9fa712 · outbound

This paper cites Unifying large language models and knowledge graphs: A roadmap.IEEE Transactions on Knowledge and Data Engineering, 36(7):3580–3599, 2024.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities Unifying large language models and knowledge graphs: A roadmap.IEEE Transactions on Knowledge and Data Engineering, 36(7):3580–3599, 2024

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T05:49:48.328324Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9006a1cd-d029-4d25-89fa-9d1e1a4034e3 · outbound

This paper cites Language models are few-shot learners.Advances in neural infor- mation processing systems, 33:1877–1901, 2020.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities Language models are few-shot learners.Advances in neural infor- mation processing systems, 33:1877–1901, 2020

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T05:49:48.390576Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51e36de1-9492-493e-84f1-cf1f2828acc4 · outbound

This paper cites Large language models-guided dynamic adaptation for temporal knowledge graph reasoning.Advances in Neural Information Processing Systems, 37:8384–8410, 2024.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities Large language models-guided dynamic adaptation for temporal knowledge graph reasoning.Advances in Neural Information Processing Systems, 37:8384–8410, 2024

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T05:49:48.452436Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c3d4ed5a-7336-4af5-bf49-3f3251359283 · outbound

This paper cites Retrieval-augmented generation for knowledge-intensive nlp tasks.Advances in neural information processing systems, 33:9459–9474, 2020.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities Retrieval-augmented generation for knowledge-intensive nlp tasks.Advances in neural information processing systems, 33:9459–9474, 2020

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T05:49:48.500829Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36151fb8-f26e-4884-bc7e-cec0a7f29ac6 · outbound

This paper cites Retrieval-augmented generation for large language models: A survey, 2023.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities Retrieval-augmented generation for large language models: A survey, 2023

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:49:50.752787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 32235f2c-5409-43a7-b43d-568b1c4d582a · outbound

This paper cites A survey on rag meeting llms: Towards retrieval- augmented large language models.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities A survey on rag meeting llms: Towards retrieval- augmented large language models

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:49:50.737242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 55654cdb-f61f-46ce-807d-107f5f202891 · outbound

This paper cites Enabling large language models to generate text with citations.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities Enabling large language models to generate text with citations

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T05:49:48.680664Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1beee7fd-f4ff-4d22-880d-aa1f694df430 · outbound

This paper cites Self- rag: Learning to retrieve, generate, and critique through self-reflection.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities Self- rag: Learning to retrieve, generate, and critique through self-reflection

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:49:50.708175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b6c8ae64-53a9-49d5-bd0b-712a6a33a9e0 · outbound

This paper cites Fine tuning vs.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities Fine tuning vs

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:49:50.691416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation af2db2dd-40da-4e31-b401-70d01a773b12 · outbound

This paper cites Sufficient context: A new lens on retrieval augmented genera- tion systems.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities Sufficient context: A new lens on retrieval augmented genera- tion systems

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:49:50.675576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 219a6f40-f6e8-4d63-829c-4243e5a31a9b · outbound

This paper cites Making retrieval- augmented language models robust to irrelevant context.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities Making retrieval- augmented language models robust to irrelevant context

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:49:50.656162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation fa88323e-f728-42a3-860b-3e11d6879e09 · outbound

This paper cites The power of noise: Redefining retrieval for rag systems.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities The power of noise: Redefining retrieval for rag systems

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T05:49:48.923046Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7727cd06-47ee-4159-821a-295a51c4bebe · outbound

This paper cites The distracting effect: Understanding irrelevant passages in RAG.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities The distracting effect: Understanding irrelevant passages in RAG

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:49:50.622044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 70a8658d-af2c-4728-8b17-d9d7ee1f600a · outbound

This paper cites A spreading activation theory of memory.Journal of verbal learning and verbal behavior, 22(3):261–295, 1983.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities A spreading activation theory of memory.Journal of verbal learning and verbal behavior, 22(3):261–295, 1983

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:49:50.606061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0d3ddee3-7c0b-44ae-91f5-76110853d3f8 · outbound

This paper cites Knowl- edge neurons in pretrained transformers.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities Knowl- edge neurons in pretrained transformers

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:49:50.591152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 94171ecd-5995-4135-a6e3-2ff5368584bc · outbound

This paper cites Hipporag: Neurobiologically inspired long-term memory for large language models.Advances in Neural Information Processing Systems, 37:59532–59569, 2024.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities Hipporag: Neurobiologically inspired long-term memory for large language models.Advances in Neural Information Processing Systems, 37:59532–59569, 2024

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:49:50.573658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 79a20998-bf4a-4e19-a178-5987e8ac8893 · outbound

This paper cites Psychology Press, 2013.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities Psychology Press, 2013

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:49:50.555228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4b4d8833-896f-403e-9668-bc66619e1502 · outbound

This paper cites Enhancing noise robustness of retrieval-augmented language models with adaptive adversarial training.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities Enhancing noise robustness of retrieval-augmented language models with adaptive adversarial training

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:49:50.537777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c5dd82c0-9d54-4cdf-8446-6a53a12d189f · outbound

This paper cites How easily do irrelevant inputs skew the responses of large language models? InFirst Conference on Language Modeling, 2024.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities How easily do irrelevant inputs skew the responses of large language models? InFirst Conference on Language Modeling, 2024

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:49:50.520821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7e7733a7-4599-4ade-8bca-945c2c77eeab · outbound

This paper cites Robust information retrieval.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities Robust information retrieval

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:49:50.504071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation fde9c830-9ab5-4f12-92e4-8a38b3c0a963 · outbound

This paper cites Query2doc: Query expansion with large language models.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities Query2doc: Query expansion with large language models

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:49:50.486195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7a9cbf9f-95b0-4197-817a-76dc192c8847 · outbound

This paper cites Sketching without worrying: Noise-tolerant sketch-based image retrieval.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities Sketching without worrying: Noise-tolerant sketch-based image retrieval

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:49:50.466086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1e0fc94b-d26e-4bf8-a983-4e5ccd35aafc · outbound

This paper cites Retrieval, re- ranking and multi-task learning for knowledge-base question answering.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities Retrieval, re- ranking and multi-task learning for knowledge-base question answering

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:49:50.447337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7074281b-8577-40a4-92e2-c202ef0765e1 · outbound

This paper cites G-retriever: Retrieval-augmented generation for textual graph understanding and question answering.Advances in Neural Information Processing Systems, 37:132876–132907, 2024.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities G-retriever: Retrieval-augmented generation for textual graph understanding and question answering.Advances in Neural Information Processing Systems, 37:132876–132907, 2024

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:49:50.430463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7870ca61-fd3f-40bd-a7f5-b7b5854064f7 · outbound

This paper cites Think-on-graph: Deep and responsible reasoning of large language model on knowledge graph.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities Think-on-graph: Deep and responsible reasoning of large language model on knowledge graph

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:49:50.411260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation bf1698c8-25e5-4fc2-bda8-8b88f70a117d · outbound

This paper cites Reasoning on graphs: Faithful and interpretable large language model reasoning.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities Reasoning on graphs: Faithful and interpretable large language model reasoning

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:49:50.391923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation dbcacf87-a900-48ea-b6c8-420a46913a50 · outbound

This paper cites From Local to Global: A Graph RAG Approach to Query-Focused Summarization.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities From Local to Global: A Graph RAG Approach to Query-Focused Summarization

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T05:49:49.855530Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7afb4604-efc6-4cb6-b98c-3605222cea16 · outbound

This paper cites LightRAG: Simple and Fast Retrieval-Augmented Generation.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities LightRAG: Simple and Fast Retrieval-Augmented Generation

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T05:49:49.860548Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20c48e1a-1dba-45ae-b0e9-a65aeea15681 · outbound

This paper cites Search-o1: Agentic Search-Enhanced Large Reasoning Models.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities Search-o1: Agentic Search-Enhanced Large Reasoning Models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T05:49:49.865299Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da3a0101-9dc7-4161-a81f-dab338fd9a5a · outbound

This paper cites Rethinking Reflection in Pre-Training.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities Rethinking Reflection in Pre-Training

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T05:49:49.869704Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa2f1d8c-076b-4d39-a1b8-153b43ef1ecd · outbound

This paper cites Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRs.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRs

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T05:49:49.874260Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9948832c-7eb3-47c0-aedd-09ab49a50cb8 · outbound

This paper cites R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T05:49:49.878419Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07fcce3b-36e3-408b-bb6e-0ec6f92b11b1 · outbound

This paper cites Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T05:49:49.882867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5eeeb3c2-97d0-4dfe-9a1e-374c5db56754 · outbound

This paper cites ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T05:49:49.887339Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6f1726b-4953-44fa-b44b-1964dff54e4b · outbound

This paper cites Awakening augmented generation: Learning to awaken internal knowledge of large language models for question answering.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities Awakening augmented generation: Learning to awaken internal knowledge of large language models for question answering

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:49:50.374780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 26230010-364b-4885-9d3b-b419989705b1 · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.Advances in neural information processing systems, 35:24824–24837, 2022.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities Chain-of-thought prompting elicits reasoning in large language models.Advances in neural information processing systems, 35:24824–24837, 2022

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T05:49:49.895458Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99a1e77d-caff-4a03-bcde-9b112be14a8f · outbound

This paper cites Tree of thoughts: Deliberate problem solving with large language models.Advances in neural information processing systems, 36:11809– 11822, 2023.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities Tree of thoughts: Deliberate problem solving with large language models.Advances in neural information processing systems, 36:11809– 11822, 2023

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:49:50.344540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 178a4caa-9bfd-4cdb-9074-1768420847fa · outbound

This paper cites Chain-of-note: Enhancing robustness in retrieval- augmented language models.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities Chain-of-note: Enhancing robustness in retrieval- augmented language models

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:49:50.326800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9c6eb0b7-8d6a-486c-8726-75aca7c61064 · outbound

This paper cites Structured Prompting: Scaling In-Context Learning to 1,000 Examples.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities Structured Prompting: Scaling In-Context Learning to 1,000 Examples

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T05:49:49.908118Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dcd80439-6d04-4941-b358-0b60e843254e · outbound

This paper cites Not all demonstration examples are equally beneficial: Reweighting demonstration examples for in- context learning.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities Not all demonstration examples are equally beneficial: Reweighting demonstration examples for in- context learning

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:49:50.302704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b442fea2-c51c-421d-9f0a-6eb8bd2ff22d · outbound

This paper cites Can we edit factual knowledge by in-context learning? In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 4862–4876, 2023.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities Can we edit factual knowledge by in-context learning? In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 4862–4876, 2023

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:49:50.286230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 67eb32bf-78cd-4158-9f97-ac3b3c0923d8 · outbound

This paper cites Prompting as probing: Using language models for knowledge base construction.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities Prompting as probing: Using language models for knowledge base construction

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:49:50.267862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1f3c14d8-9be5-453f-a6eb-216296ae18d4 · outbound

This paper cites From self-attention to markov models: Unveiling the dynamics of generative transformers.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities From self-attention to markov models: Unveiling the dynamics of generative transformers

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:49:50.248666Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation cd1c044f-d741-4c40-8c7b-424bb228ac77 · outbound

This paper cites Bge m3-embedding: Multi-lingual, multi-functionality, multi-granularity text embeddings through self-knowledge distillation, 2024.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities Bge m3-embedding: Multi-lingual, multi-functionality, multi-granularity text embeddings through self-knowledge distillation, 2024

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-06T05:49:49.929058Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f197df09-5063-4421-8d97-56a3e1d44465 · outbound

This paper cites Mintaka: A complex, natural, and multilingual dataset for end-to-end question answering.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities Mintaka: A complex, natural, and multilingual dataset for end-to-end question answering

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:49:50.217974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6587c048-5103-4aa8-a17c-cf542011278c · outbound

This paper cites ReARTeR: Retrieval-Augmented Reasoning with Trustworthy Process Rewarding.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities ReARTeR: Retrieval-Augmented Reasoning with Trustworthy Process Rewarding

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-06T05:49:49.937321Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23a46a80-e711-4927-9bf2-b920944c3760 · outbound

This paper cites Direct fact retrieval from knowledge graphs without entity linking.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities Direct fact retrieval from knowledge graphs without entity linking

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:49:50.201281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b4fbd205-bbf5-435a-917a-5089399e758c · outbound

This paper cites T” represents the knowledge types (e.g., No RAG acts the knowledge �−�−� ), “F.

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities T” represents the knowledge types (e.g., No RAG acts the knowledge �−�−� ), “F

Reference 48

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T05:49:50.183841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

Observation 9f059c79-4a65-4798-9ea7-b6d85392dc49 · inbound

CAGE: Cognitive Attribution Graphs for Faithful Inline Citation Generation in Long-Form Question Answering cites this paper.

CAGE: Cognitive Attribution Graphs for Faithful Inline Citation Generation in Long-Form Question Answering Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities

Reference 54

Resolution: unresolved; no resolver link, observed 2026-07-31T20:22:26.094144Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cdf85a7b-ad02-4973-9364-9053bf350506 · inbound

Trajectory-Guided Forget-Recover Network for Continual LLM Unlearning cites this paper.

Trajectory-Guided Forget-Recover Network for Continual LLM Unlearning Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities

Reference 38

Resolution: metadata mismatch; local_arxiv, observed 2026-08-15T14:56:36.194665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.