Pith. sign in

Paper Citation Record · LEDGER

Hallucination Detection with Small Language Models

As of 7 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2506.22486.

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

pith.paper-citation-record.v1
2506.22486 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:11:44.524772Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

38 of 38 outbound references displayed

  • verified exact0
  • verified fuzzy6
  • unresolved32
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c98f6ece-c6ab-46b0-9458-35a715e9a10b · outbound

This paper cites A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions.

Hallucination Detection with Small Language Models A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-06T23:11:40.951534Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:11:40.951534Z digest=sha256:a96ab784c154f30ab4c58686a8d420cded84fb1756f3dbacb2ef60493f232c51

Observation 8863f3b9-e5a5-4ca2-a8b0-1f1e70662a32 · outbound

This paper cites Rouge: A package for automatic evaluation of summaries,.

Hallucination Detection with Small Language Models Rouge: A package for automatic evaluation of summaries,

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-06T23:11:41.039068Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:11:41.039068Z digest=sha256:ee51257fc66d205c0b84aba3a61063ae366c4d04984b44649882f1092b7b8c6d

Observation 20a7edbf-4277-4dac-baad-70a35fd03559 · outbound

This paper cites Language Models (Mostly) Know What They Know.

Hallucination Detection with Small Language Models Language Models (Mostly) Know What They Know

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-06T23:11:41.104384Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:11:41.104384Z digest=sha256:ca476714639109a3805005bc08a45423980a44e30102d881907004c6ad8f3ce4

Observation 010a0c4a-5ec3-4dbe-a921-b3808518c47c · outbound

This paper cites Retrieval- augmented generation for knowledge-intensive nlp tasks,.

Hallucination Detection with Small Language Models Retrieval- augmented generation for knowledge-intensive nlp tasks,

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-06T23:11:41.170579Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:11:41.170579Z digest=sha256:0c7ac4467f1f5cd2533dc97c504a868c7e5dec072bfac47128ea6b99030ad856

Observation 21d105b4-8e57-4a89-b795-80e729746cca · outbound

This paper cites When Large Language Models Meet Vector Databases: A Survey.

Hallucination Detection with Small Language Models When Large Language Models Meet Vector Databases: A Survey

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-06T23:11:41.231064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:11:41.231064Z digest=sha256:2627295c0385ff3b52bf99bbc5075e0c95f0061c5fead410ba2084e654b1b927

Observation 3e197c3c-4115-49ba-8867-0bb0abd59eab · outbound

This paper cites Small Language Models: Survey, Measurements, and Insights.

Hallucination Detection with Small Language Models Small Language Models: Survey, Measurements, and Insights

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-06T23:11:41.309503Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:11:41.309503Z digest=sha256:97378dc9f5ca80493d318cdbf3341eead417dbdc7aeb07c9064a1a180c0bfd9c

Observation 52499670-29e7-4871-9615-39869198ada2 · outbound

This paper cites Prompt programming for large language models: Beyond the few-shot paradigm,.

Hallucination Detection with Small Language Models Prompt programming for large language models: Beyond the few-shot paradigm,

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-06T23:11:41.358255Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:11:41.358255Z digest=sha256:1be9752228563b3055a9e6f57d0d8a422b858184f74457d593ce51818420b6a2

Observation 78228841-48af-4eed-9109-bd4175f41806 · outbound

This paper cites Challenges and Applications of Large Language Models.

Hallucination Detection with Small Language Models Challenges and Applications of Large Language Models

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-06T23:11:41.445965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:11:41.445965Z digest=sha256:a9479d6507c6abaea9d6e5c5dd1165d164e1dd49a44b695b8eee207417bec149

Observation 5951fa15-31fd-4c02-90f8-162ba0d19f3e · outbound

This paper cites Translating Natural Language to Planning Goals with Large-Language Models.

Hallucination Detection with Small Language Models Translating Natural Language to Planning Goals with Large-Language Models

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-06T23:11:41.552461Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:11:41.552461Z digest=sha256:1495a409955af64366592248ddff3a6d594d76883e1fc9415b5103a7b42f8c45

Observation 1245063c-0655-4595-946b-bf376e9f73d8 · outbound

This paper cites A Reality check of the benefits of LLM in business.

Hallucination Detection with Small Language Models A Reality check of the benefits of LLM in business

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-06T23:11:41.642056Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:11:41.642056Z digest=sha256:7caae13fc6ba3badf6518a4b538ee5a069ebb4d7da5d6faae0e27bf90e7e0322

Observation aa7bff0d-e90e-4833-8dcd-77f2529611a4 · outbound

This paper cites Recent Advances in Recurrent Neural Networks.

Hallucination Detection with Small Language Models Recent Advances in Recurrent Neural Networks

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-06T23:11:41.735110Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:11:41.735110Z digest=sha256:0d74fb2e1df6eb94d241919e92b7f2f66c3391c91b5b64b100bccbbc15d7a367

Observation b216261c-cf7c-4fc6-a696-969675f874dd · outbound

This paper cites Long short-term memory,.

Hallucination Detection with Small Language Models Long short-term memory,

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-06T23:11:41.911578Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:11:41.911578Z digest=sha256:8c7c7a14f5ff41b83b4d55b4fb49e78564792bbe8e440b044af5df0d51f16523

Observation 5d85f681-3ff3-4804-b3af-a73d3ae0c71f · outbound

This paper cites Overview of the transformer-based models for nlp tasks,.

Hallucination Detection with Small Language Models Overview of the transformer-based models for nlp tasks,

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:11:45.881873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:11:42.003103Z digest=sha256:9ee157c23ee7bd0399400d5feb8667ef7bbfcccb03f7f9f0571a4285f00949c5

Observation d1e886f6-9e0e-4447-ad53-4db00edbaaed · outbound

This paper cites Attention is all you need,.

Hallucination Detection with Small Language Models Attention is all you need,

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-06T23:11:42.088853Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:11:42.088853Z digest=sha256:05463d01dbbdaf90664220b4889e214f47b10ed652546b427fa61720478a02d5

Observation 0c3fbef1-6d6e-4f2d-8e18-40a1cf687ec4 · outbound

This paper cites Gpt-3: What’s it good for?.

Hallucination Detection with Small Language Models Gpt-3: What’s it good for?

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:11:45.713916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:11:42.243654Z digest=sha256:b5daae72bcedfd06c9aa87324a01e4fab44548122d328d2d80aa780d64d8ea6d

Observation 5fb0386e-f420-43ce-ab55-0e36351181c7 · outbound

This paper cites An overview of bard: an early experiment with generative ai,.

Hallucination Detection with Small Language Models An overview of bard: an early experiment with generative ai,

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:11:45.587332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:11:42.338201Z digest=sha256:465f21ddc9e03b742a27e4d9a4b80cad634fc4cd6f98c6bfb057819ed95e4935

Observation 55561904-3e5d-4a38-8746-ecac94d5052c · outbound

This paper cites Evaluating Verifiability in Generative Search Engines.

Hallucination Detection with Small Language Models Evaluating Verifiability in Generative Search Engines

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-06T23:11:42.435270Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:11:42.435270Z digest=sha256:bc824d2fd939fc6ccac0cf1f325d2234c1ef09806ab18d2295d4776a1e4bb37b

Observation e8a62c16-23ae-4114-bb54-ce478dd073d9 · outbound

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

Hallucination Detection with Small Language Models Training language models to follow instructions with human feedback,

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-06T23:11:42.535589Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:11:42.535589Z digest=sha256:8d0ee122f679163cae55ddcb0150431e45a1cda9bdfc7278a73334e656c93e9f

Observation 0d0804d4-fc7c-42b8-8ad2-05e0b68822df · outbound

This paper cites Language mod- els are few-shot learners,.

Hallucination Detection with Small Language Models Language mod- els are few-shot learners,

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-06T23:11:42.648129Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:11:42.648129Z digest=sha256:8541c665e4002fce33823f738eac2f7a7d3ceaf0ebd5a222fc648476d3157a3a

Observation 9e3cfe96-3543-4476-8dbe-550ee8a2db9d · outbound

This paper cites An Audit on the Perspectives and Challenges of Hallucinations in NLP.

Hallucination Detection with Small Language Models An Audit on the Perspectives and Challenges of Hallucinations in NLP

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-06T23:11:42.752082Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:11:42.752082Z digest=sha256:3ab59076dea0b06adb2e3f9a85a57fae43e80de43a986183507886de1f3db440

Observation fd2afcde-3686-49e3-bc94-4d985f0873be · outbound

This paper cites On calibration of modern neural networks,.

Hallucination Detection with Small Language Models On calibration of modern neural networks,

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-06T23:11:42.876120Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:11:42.876120Z digest=sha256:df8396dbd96b7f16f3713fef14130bfc8c884ad139cb20f1f56e6e91aae86141

Observation fc7b28cc-a04c-46f8-9852-b12be02f4b30 · outbound

This paper cites Bleu: a method for automatic evaluation of machine translation,.

Hallucination Detection with Small Language Models Bleu: a method for automatic evaluation of machine translation,

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-06T23:11:42.988896Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:11:42.988896Z digest=sha256:c4aba1c548e7965b19a85e1c4cb5fd55bb6fcaa589711a262f987182780339bf

Observation 8ff19275-d4d5-4b94-8fa4-0150e9b7f72e · outbound

This paper cites Hallucination detection: Robustly discerning reliable answers in large language models,.

Hallucination Detection with Small Language Models Hallucination detection: Robustly discerning reliable answers in large language models,

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:11:45.441797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:11:43.095569Z digest=sha256:2c315157bddd24f89776c643b844eb0f738e996782aca57d12ebb818afa785d1

Observation 49bf99fb-d3aa-4c22-b1bf-96bcecba2ae6 · outbound

This paper cites Controlled Hallucinations: Learning to Generate Faithfully from Noisy Data.

Hallucination Detection with Small Language Models Controlled Hallucinations: Learning to Generate Faithfully from Noisy Data

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-06T23:11:43.157795Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:11:43.157795Z digest=sha256:9a7f884a7f87e44c77b058c124c999a31b1af4a041b3b4d828a4a6e4dc5445e4

Observation 448560d3-f2d6-4a2e-a81d-a324e5ec4724 · outbound

This paper cites LLMs Know More Than They Show: On the Intrinsic Representation of LLM Hallucinations.

Hallucination Detection with Small Language Models LLMs Know More Than They Show: On the Intrinsic Representation of LLM Hallucinations

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-06T23:11:43.229239Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:11:43.229239Z digest=sha256:4fc0d6369569876465c466c2798825dc0f6a051f3ddc39019db75059a2958b50

Observation 91bf25c5-f553-482d-bf85-9d2a3fe134af · outbound

This paper cites GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinements.

Hallucination Detection with Small Language Models GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinements

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-06T23:11:43.320414Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:11:43.320414Z digest=sha256:e83d858d7ffcb0b87447c66701d8109a08d7de4f42cd42a7e45dca1823a38bdb

Observation 9440cc8e-c0e8-44ac-9c32-05538e2e1dc5 · outbound

This paper cites Generating Sequences by Learning to Self-Correct.

Hallucination Detection with Small Language Models Generating Sequences by Learning to Self-Correct

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-06T23:11:43.449396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:11:43.449396Z digest=sha256:704ce248f6d55703ffd16aec29155cda362d8b5dd786113626f201769a1ceef4

Observation 0f36a37a-a27e-4c54-aee5-70fac33595bf · outbound

This paper cites Detecting hallucinations in large language models using semantic entropy,.

Hallucination Detection with Small Language Models Detecting hallucinations in large language models using semantic entropy,

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:11:45.248931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:11:43.514563Z digest=sha256:b5e2c881fffd95bca9170b703c1762ae8e23c266eabd729aeb6b9ad913f1926d

Observation edf8ea62-d53a-4f2f-b2a6-4c2983aeac05 · outbound

This paper cites To Believe or Not to Believe Your LLM.

Hallucination Detection with Small Language Models To Believe or Not to Believe Your LLM

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-06T23:11:43.619466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:11:43.619466Z digest=sha256:52113d801f1f2c6feae5c15008d486369c8ea01ae85038af3f5a18663387d757

Observation 08e1fc67-a40b-415c-a128-dd2d9bde2c70 · outbound

This paper cites To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning.

Hallucination Detection with Small Language Models To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-06T23:11:43.718989Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:11:43.718989Z digest=sha256:e34188bf58bb0ad31ef38e7e02e36be88256d45a319ca78c6bc2bf20a1b0bcf4

Observation 21c7b13c-b495-4f0c-9350-0254e6ba777d · outbound

This paper cites Improving language understanding by generative pre-training,.

Hallucination Detection with Small Language Models Improving language understanding by generative pre-training,

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-06T23:11:43.809001Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:11:43.809001Z digest=sha256:a75079422cdf01286faaa809dfed8452d1689f465fdca27d34d102af15b35cc0

Observation 0850a13a-beac-49c5-a96a-a8059a719da4 · outbound

This paper cites (accessed: 12.11.2023).

Hallucination Detection with Small Language Models (accessed: 12.11.2023)

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:11:45.026179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:11:43.884185Z digest=sha256:4f8f9468348fa8a90919bd94f5db0ea56e14efaaac5000859a52fad308cf6e1f

Observation 37355f90-e2e7-4203-99dc-7cf2108fe1c8 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Hallucination Detection with Small Language Models LoRA: Low-Rank Adaptation of Large Language Models

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-06T23:11:43.987446Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:11:43.987446Z digest=sha256:6eda39463944044d152a9474a732b3c9ccdc074f7ee1c64ebc63777cb3590e68

Observation 065637cf-f68f-43b8-bba1-d81040644629 · outbound

This paper cites Reducing hallucination in structured outputs via Retrieval-Augmented Generation.

Hallucination Detection with Small Language Models Reducing hallucination in structured outputs via Retrieval-Augmented Generation

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-06T23:11:44.108057Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:11:44.108057Z digest=sha256:8be17beb0751984bcc7ce724490168732f2635c6d69e1fe8f6a74e2343205304

Observation e2c4ba21-897a-45cd-b518-ea27df255621 · outbound

This paper cites Qwen Technical Report.

Hallucination Detection with Small Language Models Qwen Technical Report

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-06T23:11:44.231633Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:11:44.231633Z digest=sha256:af3ecfa4e96021712cbd97eae8f1e545f614ae66e83ef65e86d0acbc4a17185a

Observation d606619a-503b-49e1-a5ae-1b774301f208 · outbound

This paper cites MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies.

Hallucination Detection with Small Language Models MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-06T23:11:44.322465Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:11:44.322465Z digest=sha256:57239c0d08dcb86be316fa1694483c4ec38087ab71e81a3b38da59eb09ec71d4

Observation 12064596-d6e3-4ecf-aa0c-4ace886b1729 · outbound

This paper cites Mixture-of-experts with expert choice routing,.

Hallucination Detection with Small Language Models Mixture-of-experts with expert choice routing,

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-06T23:11:44.442397Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:11:44.442397Z digest=sha256:1f4d2d5b9cfd91514ab72f411edb3652be31fadfa062742c495490798d9c344f

Observation e70cdb1e-0516-4a23-b808-15feffbe6d3a · outbound

This paper cites Complex Claim Verification with Evidence Retrieved in the Wild.

Hallucination Detection with Small Language Models Complex Claim Verification with Evidence Retrieved in the Wild

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-06T23:11:44.524772Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:11:44.524772Z digest=sha256:11c5e8c69b23d84d77f523e08e9be87a15f412f18ece4cf05f21627f16c2b1a1

Pith citing papers

No inbound Pith citation observations are available.