Paper Citation Record · LEDGER

Challenges in Guardrailing Large Language Models for Science

As of 15 August 2026, this Paper Citation Record lists 100 of 127 outbound references and 4 inbound Pith citation observations for arXiv:2411.08181.

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

pith.paper-citation-record.v1
2411.08181 v2

Coverage vector

measured 100 of 127 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T21:54:44.405028Z

measured 104 of 104 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T18:09:42.307732Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-17T00:31:24.688676Z

Reference resolution

100 of 127 outbound references displayed

verified exact3
verified fuzzy0
unresolved97
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malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1f11e155-bff6-49d0-a7df-2f6590558924 · outbound

This paper cites Language Models are Few-Shot Learners.

Challenges in Guardrailing Large Language Models for Science Language Models are Few-Shot Learners

Reference 1

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Observation e1854571-4e60-4983-8c20-60e7cb1e3a41 · outbound

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

Challenges in Guardrailing Large Language Models for Science Improving language understanding by gener ative pre-training,

Reference 2

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Observation bc7905e3-aaa7-4dbc-8cdf-11d5e51c3cb8 · outbound

This paper cites Finetuned Language Models Are Zero-Shot Learners.

Challenges in Guardrailing Large Language Models for Science Finetuned Language Models Are Zero-Shot Learners

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-12T21:54:43.902444Z

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Observation 5d2eb26a-65b9-41c4-b733-92c83be17e51 · outbound

This paper cites On the dangers of stochastic parrots: Can language mod els be too big?.

Challenges in Guardrailing Large Language Models for Science On the dangers of stochastic parrots: Can language mod els be too big?

Reference 4

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Observation 5336d59c-e9bc-4731-b47e-2a910a254fca · outbound

This paper cites Bot-adversarial dialogue for safe conversational agents ,.

Challenges in Guardrailing Large Language Models for Science Bot-adversarial dialogue for safe conversational agents ,

Reference 5

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Observation cb7f38ab-7832-4f35-845e-3c7167ec926e · outbound

This paper cites Safeguarding Large Language Models: A Survey.

Challenges in Guardrailing Large Language Models for Science Safeguarding Large Language Models: A Survey

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T21:54:43.918501Z

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Observation cdf085b0-5d70-4207-959a-5bca7b59490b · outbound

This paper cites The social impact of natural lan guage processing,.

Challenges in Guardrailing Large Language Models for Science The social impact of natural lan guage processing,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T21:54:43.924865Z

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Observation 8ed527af-bf41-43c8-ae3b-f5cea19f3cf3 · outbound

This paper cites Dissecting racial bias in an algorithm used to manage the he alth of populations,.

Challenges in Guardrailing Large Language Models for Science Dissecting racial bias in an algorithm used to manage the he alth of populations,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-12T21:54:43.930313Z

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Observation 2c7b02a2-5467-412d-bc0e-9b2327172cd2 · outbound

This paper cites Chatgpt is fun, but not an author,.

Challenges in Guardrailing Large Language Models for Science Chatgpt is fun, but not an author,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-12T21:54:43.935711Z

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Observation 90b14c26-d1cd-4e2d-ba17-471b363a70a6 · outbound

This paper cites GPT-4 Technical Report.

Challenges in Guardrailing Large Language Models for Science GPT-4 Technical Report

Reference 10

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Observation 623fc873-5a78-4e59-bf93-8fd76f8d53a5 · outbound

This paper cites The Llama 3 Herd of Models.

Challenges in Guardrailing Large Language Models for Science The Llama 3 Herd of Models

Reference 11

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Observation d093290c-b989-4e07-9bae-3c2cdba8326a · outbound

This paper cites Introducing claude 3.5 sonnet,.

Challenges in Guardrailing Large Language Models for Science Introducing claude 3.5 sonnet,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T21:54:43.952165Z

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Observation 981ed5d0-4011-4d10-9ba3-02de9c1ca65c · outbound

This paper cites Mixtral of Experts.

Challenges in Guardrailing Large Language Models for Science Mixtral of Experts

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T21:54:43.957264Z

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Observation 19b4fe03-8f4c-4bef-a36a-5d2dc8bc797a · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Challenges in Guardrailing Large Language Models for Science Gemini: A Family of Highly Capable Multimodal Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-12T21:54:43.962576Z

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Observation e27e247e-6df7-4500-8876-10ae40c3fbde · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

Challenges in Guardrailing Large Language Models for Science On the Opportunities and Risks of Foundation Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T21:54:43.968092Z

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Observation f700533d-4fab-49c2-b9ce-722be1e99013 · outbound

This paper cites The Impact of Large Language Models on Scientific Discovery: a Preliminary Study using GPT-4.

Challenges in Guardrailing Large Language Models for Science The Impact of Large Language Models on Scientific Discovery: a Preliminary Study using GPT-4

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T21:54:43.973162Z

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Observation 8a33905f-fd9f-4800-b244-64eefe8f816a · outbound

This paper cites An Interdisciplinary Outlook on Large Language Models for Scientific Research.

Challenges in Guardrailing Large Language Models for Science An Interdisciplinary Outlook on Large Language Models for Scientific Research

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-12T21:54:43.978039Z

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Observation 8e2f1124-12f7-4259-bcc5-6bc8e2d91eee · outbound

This paper cites The ethics of chatg pt in medicine and healthcare: a systematic review on large l anguage models (llms),.

Challenges in Guardrailing Large Language Models for Science The ethics of chatg pt in medicine and healthcare: a systematic review on large l anguage models (llms),

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-12T21:54:43.982826Z

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Observation 8b7e8b1a-c1da-4308-800c-91ab4272ef8a · outbound

This paper cites Ethical Considerations and Policy Implications for Large Language Models: Guiding Responsible Development and Deployment.

Challenges in Guardrailing Large Language Models for Science Ethical Considerations and Policy Implications for Large Language Models: Guiding Responsible Development and Deployment

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-12T21:54:43.987633Z

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Observation 8a6bd30b-cd2c-4afa-9444-b7007f20520b · outbound

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

Challenges in Guardrailing Large Language Models for Science Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-12T21:54:43.992834Z

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Observation b2252999-c98c-4cff-a5d1-dca18bd0d6a4 · outbound

This paper cites NeMo Guardrails: A Toolkit for Controllable and Safe LLM Applications with Programmable Rails.

Challenges in Guardrailing Large Language Models for Science NeMo Guardrails: A Toolkit for Controllable and Safe LLM Applications with Programmable Rails

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T21:54:43.998281Z

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Observation 6eae1cb2-ff42-444e-b3ef-e97ca51068cf · outbound

This paper cites Prompti ng is programming: A query language for large language model s,.

Challenges in Guardrailing Large Language Models for Science Prompti ng is programming: A query language for large language model s,

Reference 22

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Observation 91e6a922-6d43-46aa-a96b-d0218487ab35 · outbound

This paper cites Guidance: A language f or controlling large language models,.

Challenges in Guardrailing Large Language Models for Science Guidance: A language f or controlling large language models,

Reference 23

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Observation 2777e52a-469d-4fd0-8f0f-783ada811f22 · outbound

This paper cites Guardrails: Adding guard rails to large language models,.

Challenges in Guardrailing Large Language Models for Science Guardrails: Adding guard rails to large language models,

Reference 24

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Observation 081a395c-d870-4a35-9486-fcf1779860fc · outbound

This paper cites Ai-generate d clinical summaries require more than accuracy,.

Challenges in Guardrailing Large Language Models for Science Ai-generate d clinical summaries require more than accuracy,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-12T21:54:44.021017Z

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Observation de3e4871-7e6d-4709-b562-c87d4d771b69 · outbound

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

Challenges in Guardrailing Large Language Models for Science A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-12T21:54:44.027139Z

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Observation f0513262-d87a-4f9b-8918-e4b95bd43a03 · outbound

This paper cites Building Guardrails for Large Language Models.

Challenges in Guardrailing Large Language Models for Science Building Guardrails for Large Language Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-12T21:54:44.032244Z

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Observation 6495ba8b-eac3-41c9-9f2c-e8f444fae62a · outbound

This paper cites Chainpoll: A high efficacy method for LLM hallucination detection.

Challenges in Guardrailing Large Language Models for Science Chainpoll: A high efficacy method for LLM hallucination detection

Reference 28

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Observation 327efb7a-898d-44f3-9123-1f8b00c2408f · outbound

This paper cites SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models.

Challenges in Guardrailing Large Language Models for Science SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models

Reference 29

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Observation 7deed123-454c-4c93-a9d5-4ef03c88aacf · outbound

This paper cites GPTScore: Evaluate as You Desire.

Challenges in Guardrailing Large Language Models for Science GPTScore: Evaluate as You Desire

Reference 30

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Observation 36731d93-614a-4edc-8038-8ad7e2545760 · outbound

This paper cites G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment.

Challenges in Guardrailing Large Language Models for Science G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment

Reference 31

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Observation 205b22df-a6bf-42ca-936e-5314c3cf6bf6 · outbound

This paper cites Evaluation and mitigation of the limitations of large language models in cl inical decision-making,.

Challenges in Guardrailing Large Language Models for Science Evaluation and mitigation of the limitations of large language models in cl inical decision-making,

Reference 32

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Observation 0120ee3b-f2bc-41bb-b8d2-b35ff506f87e · outbound

This paper cites An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models.

Challenges in Guardrailing Large Language Models for Science An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-12T21:54:44.064381Z

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Observation f09feebe-a1ec-4c47-9988-a30e210cc3b7 · outbound

This paper cites Mitigating un wanted biases with adversarial learning,.

Challenges in Guardrailing Large Language Models for Science Mitigating un wanted biases with adversarial learning,

Reference 34

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Observation f8375dfb-e05d-449c-8053-0ad1558a4b33 · outbound

This paper cites Extracting training data from large language models,.

Challenges in Guardrailing Large Language Models for Science Extracting training data from large language models,

Reference 35

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This paper cites The secret sharer: Evalu ating and testing unintended memorization in neural networ ks,.

Challenges in Guardrailing Large Language Models for Science The secret sharer: Evalu ating and testing unintended memorization in neural networ ks,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-12T21:54:44.080727Z

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Observation 50da8ea9-8d16-4d09-8f8c-f1f2a666b6c1 · outbound

This paper cites Privacy Issues in Large Language Models: A Survey.

Challenges in Guardrailing Large Language Models for Science Privacy Issues in Large Language Models: A Survey

Reference 37

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Observation ef7e6a3f-b883-4c26-a196-a6e72ef70aa1 · outbound

This paper cites Beyo nd memorization: Violating privacy via inference with larg e language models,.

Challenges in Guardrailing Large Language Models for Science Beyo nd memorization: Violating privacy via inference with larg e language models,

Reference 38

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Observation ea402277-1df4-47c4-b65d-9fa556d0fa5f · outbound

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Challenges in Guardrailing Large Language Models for Science Pro pile: Probing privacy leakage in large language models,

Reference 39

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Observation 49719e6e-0c60-4433-8179-6bcb896c9b5c · outbound

This paper cites SMILES-Prompting: A Novel Approach to LLM Jailbreak Attacks in Chemical Synthesis.

Challenges in Guardrailing Large Language Models for Science SMILES-Prompting: A Novel Approach to LLM Jailbreak Attacks in Chemical Synthesis

Reference 40

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Observation ca58ea15-dd3c-4a62-94a9-0b055c3653ee · outbound

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Challenges in Guardrailing Large Language Models for Science Scientific Large Language Models: A Survey on Biological & Chemical Domains

Reference 41

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Observation 84011c70-a71c-4936-93d9-20f553aa716a · outbound

This paper cites Baseline Defenses for Adversarial Attacks Against Aligned Language Models.

Challenges in Guardrailing Large Language Models for Science Baseline Defenses for Adversarial Attacks Against Aligned Language Models

Reference 42

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Observation dcb77c67-c192-4969-930b-e1afc6765e19 · outbound

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Challenges in Guardrailing Large Language Models for Science Red Teaming Language Models with Language Models

Reference 43

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Challenges in Guardrailing Large Language Models for Science On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective

Reference 44

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Observation a46a3055-390c-4224-842a-5fb4b71f9956 · outbound

This paper cites Red teaming large language models in medicine: Real-world insights on m odel behavior,.

Challenges in Guardrailing Large Language Models for Science Red teaming large language models in medicine: Real-world insights on m odel behavior,

Reference 45

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Observation 2edd5979-7ab3-48cd-8d4e-c905740f135a · outbound

This paper cites Right to be forgotten in the era of l arge language models: Implications, challenges, and solutions,.

Challenges in Guardrailing Large Language Models for Science Right to be forgotten in the era of l arge language models: Implications, challenges, and solutions,

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-12T21:54:44.131003Z

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Observation 8ae6a772-b49c-4f58-97e3-43f1fefb1b14 · outbound

This paper cites RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models.

Challenges in Guardrailing Large Language Models for Science RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-12T21:54:44.135810Z

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Observation 11d4a3d0-1e99-4f23-96b5-1fcef8acc2eb · outbound

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Challenges in Guardrailing Large Language Models for Science OPT: Open Pre-trained Transformer Language Models

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-12T21:54:44.141244Z

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Observation 1aa41a0c-1efc-4edb-9471-828b5e772b02 · outbound

This paper cites Ai transparency in the age of llm s: A human-centered research roadmap,.

Challenges in Guardrailing Large Language Models for Science Ai transparency in the age of llm s: A human-centered research roadmap,

Reference 49

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Observation a87bfae7-020c-44ed-b776-53c292a5ce15 · outbound

This paper cites On the Calibration of Large Language Models and Alignment.

Challenges in Guardrailing Large Language Models for Science On the Calibration of Large Language Models and Alignment

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-12T21:54:44.151209Z

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Observation 1e6162cc-db44-4488-9419-1945a374eb15 · outbound

This paper cites BayesFormer: Transformer with Uncertainty Estimation.

Challenges in Guardrailing Large Language Models for Science BayesFormer: Transformer with Uncertainty Estimation

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-12T21:54:44.156970Z

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Observation 660ef3d2-c520-415d-b0c0-c0b3bcf854e8 · outbound

This paper cites Large language models, scientiﬁc knowledge and factua lity: A framework to streamline human expert evaluation,.

Challenges in Guardrailing Large Language Models for Science Large language models, scientiﬁc knowledge and factua lity: A framework to streamline human expert evaluation,

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-12T21:54:44.162272Z

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Observation 26c8900b-1bc8-412f-b0e1-71e0c7b4936e · outbound

This paper cites Large Language Models in Drug Discovery and Development: From Disease Mechanisms to Clinical Trials.

Challenges in Guardrailing Large Language Models for Science Large Language Models in Drug Discovery and Development: From Disease Mechanisms to Clinical Trials

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-12T21:54:44.167106Z

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Observation 35c43ed2-6463-4137-a1ae-43aba4e055da · outbound

This paper cites Assessing Large Language Models on Climate Information.

Challenges in Guardrailing Large Language Models for Science Assessing Large Language Models on Climate Information

Reference 54

Resolution: verified exact; local_arxiv, observed 2026-08-12T21:54:45.131022Z

Source-reported events for the cited work

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Observation c838e7de-f397-46cb-8602-ac056d5740da · outbound

This paper cites Unlearning climate misinformation in la rge language models,.

Challenges in Guardrailing Large Language Models for Science Unlearning climate misinformation in la rge language models,

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-12T21:54:44.178106Z

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Observation b139aaac-4090-497b-8647-73fc4aba9aa9 · outbound

This paper cites Enhancing Large Language Models with Climate Resources.

Challenges in Guardrailing Large Language Models for Science Enhancing Large Language Models with Climate Resources

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-12T21:54:44.183664Z

Source-reported events for the cited work

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Observation fb7d8202-15a2-49fd-a3c2-29801bc999af · outbound

This paper cites Better patching using llm prom pting, via self-consistency,.

Challenges in Guardrailing Large Language Models for Science Better patching using llm prom pting, via self-consistency,

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-12T21:54:44.189009Z

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Observation bd3881d4-1d6b-4b40-99c8-f42ca03f7eb8 · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large l anguage models,.

Challenges in Guardrailing Large Language Models for Science Chain-of-thought prompting elicits reasoning in large l anguage models,

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-12T21:54:44.193811Z

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Observation 36e2bc35-f678-4881-bd9f-7c46f1349402 · outbound

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Challenges in Guardrailing Large Language Models for Science How can we kno w when language models know? on the calibration of language m odels for question answering,

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Challenges in Guardrailing Large Language Models for Science ”why should I t rust you?

Reference 60

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Challenges in Guardrailing Large Language Models for Science A uniﬁed approach to inter preting model predictions,

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Challenges in Guardrailing Large Language Models for Science Actionable auditing: Inv estigating the impact of publicly naming biased performanc e results of commercial ai products,

Reference 62

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This paper cites On Responsible Machine Learning Datasets with Fairness, Privacy, and Regulatory Norms.

Challenges in Guardrailing Large Language Models for Science On Responsible Machine Learning Datasets with Fairness, Privacy, and Regulatory Norms

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Challenges in Guardrailing Large Language Models for Science Privacy a nd fairness in federated learning: on the perspective of tra deoff,

Reference 64

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Challenges in Guardrailing Large Language Models for Science Low-cost high-po wer membership inference attacks,

Reference 65

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Challenges in Guardrailing Large Language Models for Science Membership Inference Attacks and Privacy in Topic Modeling

Reference 66

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Observation 0e9979c9-91a4-4064-84e0-032999b44d4f · outbound

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Challenges in Guardrailing Large Language Models for Science Membership Inference Attacks Against In-Context Learning

Reference 67

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Observation 2b0c6d8d-559d-4b45-8762-b094ed29ede9 · outbound

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Challenges in Guardrailing Large Language Models for Science Risks of AI Scientists: Prioritizing Safeguarding Over Autonomy

Reference 68

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Challenges in Guardrailing Large Language Models for Science Don't Stop Pretraining: Adapt Language Models to Domains and Tasks

Reference 69

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Challenges in Guardrailing Large Language Models for Science Enhancing LLM Factual Accuracy with RAG to Counter Hallucinations: A Case Study on Domain-Specific Queries in Private Knowledge-Bases

Reference 70

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Challenges in Guardrailing Large Language Models for Science Learn to Refuse: Making Large Language Models More Controllable and Reliable through Knowledge Scope Limitation and Refusal Mechanism

Reference 71

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Observation e28d23d5-a4c3-426e-9296-15cd84210a90 · outbound

This paper cites A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models.

Challenges in Guardrailing Large Language Models for Science A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models

Reference 72

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Observation 4b9aca17-4c5f-4d3f-ba5d-e866fad2e674 · outbound

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Challenges in Guardrailing Large Language Models for Science Control Risk for Potential Misuse of Artificial Intelligence in Science

Reference 73

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Challenges in Guardrailing Large Language Models for Science Compliance d isengagement in research: Development and validation of a n ew measure,

Reference 74

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Challenges in Guardrailing Large Language Models for Science Guidance for researchers and peer-review ers on the ethical use of large language models (llms) in scie ntiﬁc research workﬂows,

Reference 75

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Challenges in Guardrailing Large Language Models for Science The ethics of using artiﬁc ial intelligence in scientiﬁc research: new guidance neede d for a new tool,

Reference 76

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Challenges in Guardrailing Large Language Models for Science Unresolved cited work

Reference 77

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Challenges in Guardrailing Large Language Models for Science Unresolved cited work

Reference 78

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This paper cites Citation: A key to building respo nsible and accountable large language models,.

Challenges in Guardrailing Large Language Models for Science Citation: A key to building respo nsible and accountable large language models,

Reference 79

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Observation 934e8c4b-c2f3-4623-8984-68a6ffaed360 · outbound

This paper cites DyKnow: Dynamically Verifying Time-Sensitive Factual Knowledge in LLMs.

Challenges in Guardrailing Large Language Models for Science DyKnow: Dynamically Verifying Time-Sensitive Factual Knowledge in LLMs

Reference 80

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This paper cites User-LLM: Efficient LLM Contextualization with User Embeddings.

Challenges in Guardrailing Large Language Models for Science User-LLM: Efficient LLM Contextualization with User Embeddings

Reference 81

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Challenges in Guardrailing Large Language Models for Science A novel nih rese arch grant recommender using bert,

Reference 82

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Challenges in Guardrailing Large Language Models for Science A survey on large lan guage models for recommendation,

Reference 83

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Observation f1239faf-802b-496b-9716-1b9f2725f59d · outbound

This paper cites A uniﬁed framework of ﬁve princ iples for ai in society,.

Challenges in Guardrailing Large Language Models for Science A uniﬁed framework of ﬁve princ iples for ai in society,

Reference 84

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Challenges in Guardrailing Large Language Models for Science RARR: Researching and Revising What Language Models Say, Using Language Models

Reference 85

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This paper cites Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models.

Challenges in Guardrailing Large Language Models for Science Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models

Reference 86

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This paper cites Is Your LLM Outdated? A Deep Look at Temporal Generalization.

Challenges in Guardrailing Large Language Models for Science Is Your LLM Outdated? A Deep Look at Temporal Generalization

Reference 87

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Observation daa6b55f-64b2-4e32-9217-74f5c91e0592 · outbound

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

Challenges in Guardrailing Large Language Models for Science Retrieval-augmented generation for knowledge-intensive nlp tasks,

Reference 88

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This paper cites Updating knowledge in large language models: an empirica l evaluation,.

Challenges in Guardrailing Large Language Models for Science Updating knowledge in large language models: an empirica l evaluation,

Reference 89

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This paper cites Disinformation capabilities of large lang uage models,.

Challenges in Guardrailing Large Language Models for Science Disinformation capabilities of large lang uage models,

Reference 90

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Challenges in Guardrailing Large Language Models for Science Chameleon: Plug-and-play compositi onal reasoning with large language models,

Reference 91

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Observation 72f8a9de-81b8-4b22-95fc-112579a98c4b · outbound

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Challenges in Guardrailing Large Language Models for Science Toward adaptive reasoning in large la nguage models with thought rollback,

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Challenges in Guardrailing Large Language Models for Science Let’s sample ste p by step: Adaptive-consistency for efﬁcient reasoning and coding with llms,

Reference 93

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Challenges in Guardrailing Large Language Models for Science Do llms exhibit human-like response biases? a case study in survey design,

Reference 94

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This paper cites Measuring Implicit Bias in Explicitly Unbiased Large Language Models.

Challenges in Guardrailing Large Language Models for Science Measuring Implicit Bias in Explicitly Unbiased Large Language Models

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Challenges in Guardrailing Large Language Models for Science Knowledge Conflicts for LLMs: A Survey

Reference 96

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Observation f4fa5e97-6907-4ac1-9751-f7e1faeb8008 · outbound

This paper cites Chain-of-Verification Reduces Hallucination in Large Language Models.

Challenges in Guardrailing Large Language Models for Science Chain-of-Verification Reduces Hallucination in Large Language Models

Reference 97

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Observation 6e804b9c-6db3-4301-8413-fe4b51d78d72 · outbound

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Challenges in Guardrailing Large Language Models for Science V erify-an d-edit: A knowledge-enhanced chain-of-thought framework ,

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Challenges in Guardrailing Large Language Models for Science Untangle the KNOT: Interweaving conﬂicting knowledge an d reasoning skills in large language models,

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Challenges in Guardrailing Large Language Models for Science FacTool: Factuality Detection in Generative AI -- A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios

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Pith citing papers

Observation f2cfc8ab-0a68-4c82-8443-0e5c18e0ba3e · inbound

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SI-FACT: Mitigating Knowledge Conflict via Self-Improving Faithfulness-Aware Contrastive Tuning Challenges in Guardrailing Large Language Models for Science

Reference 2

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LLM Harms: A Taxonomy and Discussion Challenges in Guardrailing Large Language Models for Science

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Observation b58ac6c2-ae70-4137-b7b1-6794a278d5dd · inbound

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LLM Harms: A Taxonomy and Discussion Challenges in Guardrailing Large Language Models for Science

Reference 225

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Learning When to Act or Refuse: Guarding Agentic Reasoning Models for Safe Multi-Step Tool Use Challenges in Guardrailing Large Language Models for Science

Reference 2025

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