Paper Citation Record · LEDGER

AnswerCarefully: A Dataset for Improving the Safety of Japanese LLM Output

As of 8 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 1 inbound Pith citation observation for arXiv:2506.02372.

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

pith.paper-citation-record.v1
2506.02372 v1

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:29:15.193096Z

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:17:26.850823Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

18 of 18 outbound references displayed

verified exact0
verified fuzzy9
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 258bae2d-697f-433d-a09f-efaae2cac962 · outbound

This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

AnswerCarefully: A Dataset for Improving the Safety of Japanese LLM Output Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T11:29:13.172924Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d43c3a6-5a43-45d0-b0c8-a0efb2f51d51 · outbound

This paper cites Bowman, Zac Hatfield-Dodds, Ben Mann, Dario Amodei, Nicholas Joseph, Sam McCandlish, Tom Brown, and Jared Kaplan.

AnswerCarefully: A Dataset for Improving the Safety of Japanese LLM Output Bowman, Zac Hatfield-Dodds, Ben Mann, Dario Amodei, Nicholas Joseph, Sam McCandlish, Tom Brown, and Jared Kaplan

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:29:17.996876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 312bed32-3c6c-4f5a-a6d9-bba5dc19d8e0 · outbound

This paper cites Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations.

AnswerCarefully: A Dataset for Improving the Safety of Japanese LLM Output Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T11:29:13.320214Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1bec56d0-8f72-4102-8c4a-a814d710fabb · outbound

This paper cites Investigating tuning methods for achieving usefulness and safety for Japanese large language models (in Japanese).

AnswerCarefully: A Dataset for Improving the Safety of Japanese LLM Output Investigating tuning methods for achieving usefulness and safety for Japanese large language models (in Japanese)

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:29:17.723376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e95eafd8-5227-4877-85f6-b62b11c09b86 · outbound

This paper cites Japanese safety boundary test for large language models (in Japanese).

AnswerCarefully: A Dataset for Improving the Safety of Japanese LLM Output Japanese safety boundary test for large language models (in Japanese)

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:29:17.503783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0b3b0dce-8bbd-4034-baf7-21f25fdb39a8 · outbound

This paper cites LLM-jp: A Cross-organizational Project for the Research and Development of Fully Open Japanese LLMs.

AnswerCarefully: A Dataset for Improving the Safety of Japanese LLM Output LLM-jp: A Cross-organizational Project for the Research and Development of Fully Open Japanese LLMs

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T11:29:13.679486Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cfe4d581-d78d-43c8-b876-8dfb84542e94 · outbound

This paper cites Construction of the Japanese TruthfulQA Dataset (in Japanese).

AnswerCarefully: A Dataset for Improving the Safety of Japanese LLM Output Construction of the Japanese TruthfulQA Dataset (in Japanese)

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:29:17.253806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7c5ba52e-7c82-4aa3-8f24-a59be2b46736 · outbound

This paper cites an unresolved cited work.

AnswerCarefully: A Dataset for Improving the Safety of Japanese LLM Output Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:29:17.034182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8e5070fc-ede0-47ef-968b-62f442e5007f · outbound

This paper cites JSocialFact: a misinfor- mation dataset from social media for benchmarking LLM safety.

AnswerCarefully: A Dataset for Improving the Safety of Japanese LLM Output JSocialFact: a misinfor- mation dataset from social media for benchmarking LLM safety

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T11:29:14.087472Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50ad9775-4563-4de8-acd6-317acfea1bab · outbound

This paper cites GPT-4 technical report, 2024.

AnswerCarefully: A Dataset for Improving the Safety of Japanese LLM Output GPT-4 technical report, 2024

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:29:16.755003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e171592d-8d21-4485-b4d4-09aacb7b3792 · outbound

This paper cites Large-scale human evaluation of LLM safety (in Japanese).

AnswerCarefully: A Dataset for Improving the Safety of Japanese LLM Output Large-scale human evaluation of LLM safety (in Japanese)

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:29:16.526108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation be382a7c-2b54-43c8-ac68-79a9c93bbd81 · outbound

This paper cites Gemini: A family of highly capable multimodal models, 2024.

AnswerCarefully: A Dataset for Improving the Safety of Japanese LLM Output Gemini: A family of highly capable multimodal models, 2024

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T11:29:14.456053Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da3c5d52-8b6b-47f7-b631-89c647c403d6 · outbound

This paper cites Llama 2: Open foundation and fine-tuned chat models, 2023.

AnswerCarefully: A Dataset for Improving the Safety of Japanese LLM Output Llama 2: Open foundation and fine-tuned chat models, 2023

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:29:16.303653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 36eafbe0-8e45-4677-b670-6bfbb183a6df · outbound

This paper cites Do-Not-Answer: Evaluating safeguards in LLMs.

AnswerCarefully: A Dataset for Improving the Safety of Japanese LLM Output Do-Not-Answer: Evaluating safeguards in LLMs

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:29:16.035331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 27c19c1a-5550-4496-9d84-6c4786339b2c · outbound

This paper cites A Chinese Dataset for Evaluating the Safeguards in Large Language Models.

AnswerCarefully: A Dataset for Improving the Safety of Japanese LLM Output A Chinese Dataset for Evaluating the Safeguards in Large Language Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T11:29:14.775471Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6bff4682-60b8-46d1-b28a-b4bfe646b41c · outbound

This paper cites JBBQ: Japanese Bias Benchmark for Analyzing Social Biases in Large Language Models.

AnswerCarefully: A Dataset for Improving the Safety of Japanese LLM Output JBBQ: Japanese Bias Benchmark for Analyzing Social Biases in Large Language Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T11:29:15.043656Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 76600452-782e-4b5d-94a5-c31bbd0e8567 · outbound

This paper cites Xing, Hao Zhang, Joseph E.

AnswerCarefully: A Dataset for Improving the Safety of Japanese LLM Output Xing, Hao Zhang, Joseph E

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:29:15.603317Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b6abdcf6-0478-4e02-806e-adfe4b6e71f7 · outbound

This paper cites an unresolved cited work.

AnswerCarefully: A Dataset for Improving the Safety of Japanese LLM Output Unresolved cited work

Reference 184

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:29:15.828088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

Observation 5cf06d1a-4497-4622-81af-3c946d84e8ef · inbound

The Scales of Justitia: A Comprehensive Survey on Safety Evaluation of LLMs cites this paper.

The Scales of Justitia: A Comprehensive Survey on Safety Evaluation of LLMs AnswerCarefully: A Dataset for Improving the Safety of Japanese LLM Output

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-07T10:17:26.850823Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.