Paper Citation Record · LEDGER

Harnessing RLHF for Robust Unanswerability Recognition and Trustworthy Response Generation in LLMs

As of 7 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2507.16951.

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

pith.paper-citation-record.v1
2507.16951 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:04:16.496141Z

measured 27 of 27 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

27 of 27 outbound references displayed

verified exact3
verified fuzzy9
unresolved15
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4fe5b5eb-5043-4b83-a3c5-6e069ac6392e · outbound

This paper cites How does bert answer questions? a layer-wise analysis of transformer representations,.

Harnessing RLHF for Robust Unanswerability Recognition and Trustworthy Response Generation in LLMs How does bert answer questions? a layer-wise analysis of transformer representations,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:04:18.923089Z

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.

Observation 218392a0-71fd-4573-b069-1420b9865902 · outbound

This paper cites No Need to Pay Attention: Simple Recurrent Neural Networks Work! (for Answering "Simple" Questions).

Harnessing RLHF for Robust Unanswerability Recognition and Trustworthy Response Generation in LLMs No Need to Pay Attention: Simple Recurrent Neural Networks Work! (for Answering "Simple" Questions)

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:04:17.363984Z

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.

Observation f118e76e-284f-4179-b4f3-958cc2398eb5 · outbound

This paper cites Rethinking Visual Dependency in Long-Context Reasoning for Large Vision-Language Models.

Harnessing RLHF for Robust Unanswerability Recognition and Trustworthy Response Generation in LLMs Rethinking Visual Dependency in Long-Context Reasoning for Large Vision-Language Models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T15:04:14.065917Z

Source-reported events for the cited work

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Observation c18795c3-a97b-48cb-af59-deb021677d85 · outbound

This paper cites Evaluating the Factual Consistency of Abstractive Text Summarization.

Harnessing RLHF for Robust Unanswerability Recognition and Trustworthy Response Generation in LLMs Evaluating the Factual Consistency of Abstractive Text Summarization

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T15:04:14.168505Z

Source-reported events for the cited work

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Observation da21dd94-cc70-447c-a26d-72e934f8ef9e · outbound

This paper cites Say What I Want: Towards the Dark Side of Neural Dialogue Models.

Harnessing RLHF for Robust Unanswerability Recognition and Trustworthy Response Generation in LLMs Say What I Want: Towards the Dark Side of Neural Dialogue Models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T15:04:14.236797Z

Source-reported events for the cited work

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Observation d1067c27-58e1-45c3-ae82-4e6950686eb1 · outbound

This paper cites Weak to strong generalization for large language models with multi-capabilities,.

Harnessing RLHF for Robust Unanswerability Recognition and Trustworthy Response Generation in LLMs Weak to strong generalization for large language models with multi-capabilities,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:04:18.703532Z

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.

Observation 12ac24b9-22e2-45c1-8482-4888fcbc8259 · outbound

This paper cites InstructPatentGPT: Training patent language models to follow instructions with human feedback.

Harnessing RLHF for Robust Unanswerability Recognition and Trustworthy Response Generation in LLMs InstructPatentGPT: Training patent language models to follow instructions with human feedback

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:04:16.765780Z

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.

Observation d1630db8-117e-4d4c-b8d5-b3c9c5fd87b1 · outbound

This paper cites Uncertainty-aware Language Modeling for Selective Question Answering.

Harnessing RLHF for Robust Unanswerability Recognition and Trustworthy Response Generation in LLMs Uncertainty-aware Language Modeling for Selective Question Answering

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T15:04:14.470908Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa476e44-3da1-49a2-866c-63a9087e7582 · outbound

This paper cites Claret: Pre-training a correlation-aware context-to-event transformer for event-centric gener- ation and classification,.

Harnessing RLHF for Robust Unanswerability Recognition and Trustworthy Response Generation in LLMs Claret: Pre-training a correlation-aware context-to-event transformer for event-centric gener- ation and classification,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T15:04:14.566411Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c7968a7-4573-4648-aae1-d4a28dcbe066 · outbound

This paper cites Question generation for question answering,.

Harnessing RLHF for Robust Unanswerability Recognition and Trustworthy Response Generation in LLMs Question generation for question answering,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:04:18.547930Z

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.

Observation b9325baf-d242-4162-8820-9db0692c6428 · outbound

This paper cites Attentional transfer is all you need: Technology-aware layout pattern generation,.

Harnessing RLHF for Robust Unanswerability Recognition and Trustworthy Response Generation in LLMs Attentional transfer is all you need: Technology-aware layout pattern generation,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T15:04:14.785830Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71b974bd-19da-497c-b5a7-fcbc80106830 · outbound

This paper cites BERT: pre-training of deep bidirectional transformers for language understanding,.

Harnessing RLHF for Robust Unanswerability Recognition and Trustworthy Response Generation in LLMs BERT: pre-training of deep bidirectional transformers for language understanding,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T15:04:14.844113Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c0514d13-8fb8-448f-8bb5-994dffd3c6aa · outbound

This paper cites Eventbert: A pre- trained model for event correlation reasoning,.

Harnessing RLHF for Robust Unanswerability Recognition and Trustworthy Response Generation in LLMs Eventbert: A pre- trained model for event correlation reasoning,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T15:04:14.964860Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c73497b-a8a7-4388-a530-a3da0091ef7d · outbound

This paper cites Modeling event-pair relations in external knowledge graphs for script reasoning,.

Harnessing RLHF for Robust Unanswerability Recognition and Trustworthy Response Generation in LLMs Modeling event-pair relations in external knowledge graphs for script reasoning,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T15:04:15.060344Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f127bde3-d45c-4d88-805d-8ae8d5d6dc26 · outbound

This paper cites Language models with image descriptors are strong few- shot video-language learners,.

Harnessing RLHF for Robust Unanswerability Recognition and Trustworthy Response Generation in LLMs Language models with image descriptors are strong few- shot video-language learners,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:04:18.384960Z

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.

Observation cc75d803-936d-48a5-8733-a195d176de35 · outbound

This paper cites U-shaped and inverted-u scaling behind emergent abilities of large language models,.

Harnessing RLHF for Robust Unanswerability Recognition and Trustworthy Response Generation in LLMs U-shaped and inverted-u scaling behind emergent abilities of large language models,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:04:18.223850Z

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.

Observation a4ea7d28-45df-42ed-bdc8-15a5620a6821 · outbound

This paper cites Palm: Scaling language modeling with pathways,.

Harnessing RLHF for Robust Unanswerability Recognition and Trustworthy Response Generation in LLMs Palm: Scaling language modeling with pathways,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:04:18.058110Z

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.

Observation d967bc84-481d-4437-8040-bf113c169971 · outbound

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

Harnessing RLHF for Robust Unanswerability Recognition and Trustworthy Response Generation in LLMs Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T15:04:15.509937Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d03d3b0-9842-4b9e-bf96-3919a8b6599c · outbound

This paper cites Visual in-context learning for large vision-language models,.

Harnessing RLHF for Robust Unanswerability Recognition and Trustworthy Response Generation in LLMs Visual in-context learning for large vision-language models,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T15:04:15.609630Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ba1275a-4bbe-4f0b-9b05-f91ff74e114f · outbound

This paper cites Draw ALL Your Imagine: A Holistic Benchmark and Agent Framework for Complex Instruction-based Image Generation.

Harnessing RLHF for Robust Unanswerability Recognition and Trustworthy Response Generation in LLMs Draw ALL Your Imagine: A Holistic Benchmark and Agent Framework for Complex Instruction-based Image Generation

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T15:04:15.709414Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23607d79-2794-4266-9747-15b0b07ec2e9 · outbound

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

Harnessing RLHF for Robust Unanswerability Recognition and Trustworthy Response Generation in LLMs Retrieval-augmented generation for knowledge-intensive NLP tasks,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:04:17.708290Z

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.

Observation dd64c302-b50c-49dd-bb0f-bc9759567efb · outbound

This paper cites OLAPH: Improving Factuality in Biomedical Long-form Question Answering.

Harnessing RLHF for Robust Unanswerability Recognition and Trustworthy Response Generation in LLMs OLAPH: Improving Factuality in Biomedical Long-form Question Answering

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:04:17.107479Z

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.

Observation 87821a92-7565-4bc2-bd8b-fc5bee9b0c56 · outbound

This paper cites A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions,.

Harnessing RLHF for Robust Unanswerability Recognition and Trustworthy Response Generation in LLMs A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T15:04:16.236883Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6c90c6a-48a5-4faa-af03-a1ebbb38680d · outbound

This paper cites TruthfulQA: Measuring How Models Mimic Human Falsehoods.

Harnessing RLHF for Robust Unanswerability Recognition and Trustworthy Response Generation in LLMs TruthfulQA: Measuring How Models Mimic Human Falsehoods

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T15:04:16.384046Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01dd0901-c709-4aa1-bda5-9e17dd7df278 · outbound

This paper cites Hint-enhanced in-context learning wakes large language models up for knowledge-intensive tasks,.

Harnessing RLHF for Robust Unanswerability Recognition and Trustworthy Response Generation in LLMs Hint-enhanced in-context learning wakes large language models up for knowledge-intensive tasks,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T15:04:16.496141Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 604274ff-0252-4456-b471-2d48aaeb6816 · outbound

This paper cites Available: https://proceedings.neurips.cc/paper/2020/ hash/6b493230205f780e1bc26945df7481e5-Abstract.html.

Harnessing RLHF for Robust Unanswerability Recognition and Trustworthy Response Generation in LLMs Available: https://proceedings.neurips.cc/paper/2020/ hash/6b493230205f780e1bc26945df7481e5-Abstract.html

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:04:17.528797Z

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.

Observation 7ce6e6c7-b03f-4257-a498-aae412ce8b0f · outbound

This paper cites Available: https://jmlr.org/papers/v24/22-1144.html.

Harnessing RLHF for Robust Unanswerability Recognition and Trustworthy Response Generation in LLMs Available: https://jmlr.org/papers/v24/22-1144.html

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:04:17.849906Z

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.

Pith citing papers

No inbound Pith citation observations are available.