QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting | Paper Citation Record

Observation 4c3e7d34-0056-45e7-9d49-4dcfeaedb1f7 · outbound

This paper cites Evaluating Correctness and Faithfulness of Instruction-Following Models for Question Answering.

QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Evaluating Correctness and Faithfulness of Instruction-Following Models for Question Answering

Reference 1

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Observation 39da6883-5833-4939-8bc5-ad922fb14ce0 · outbound

This paper cites How does the pre-training objective affect what large language models learn about linguistic properties?.

QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting How does the pre-training objective affect what large language models learn about linguistic properties?

Reference 2

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Unresolved cited work

Reference 3

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This paper cites PolyLM: Learning about Polysemy through Language Modeling.

QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting PolyLM: Learning about Polysemy through Language Modeling

Reference 5

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Observation e7c456e1-862b-4795-8970-5cb1ce8cd110 · outbound

This paper cites PromptNER: Prompting For Named Entity Recognition.

QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting PromptNER: Prompting For Named Entity Recognition

Reference 6

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This paper cites Schapire.

QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Schapire

Reference 7

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This paper cites rapidfuzz/rapidfuzz: Release 3.8.1, April 2024.

QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting rapidfuzz/rapidfuzz: Release 3.8.1, April 2024

Reference 8

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Observation bef8203d-e1a8-49d2-bf5d-8f0cf6391408 · outbound

This paper cites Harnessing GPT-3.5-turbo for Rhetorical Role Prediction in Legal Cases.

QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Harnessing GPT-3.5-turbo for Rhetorical Role Prediction in Legal Cases

Reference 9

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Observation c6221c2d-a212-4357-92a0-d2b5e0f59900 · outbound

This paper cites Piqa: Reasoning about physical commonsense in natural language.

QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Piqa: Reasoning about physical commonsense in natural language

Reference 10

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Observation b41a26fd-f53d-4c19-ac7f-a24afd2c3040 · outbound

This paper cites Prompting language models for linguistic structure,.

QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Prompting language models for linguistic structure,

Reference 11

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Observation acd1bc1e-e29d-434e-9f9b-e4334b7f830b · outbound

This paper cites Language Models are Few-Shot Learners.

QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Language Models are Few-Shot Learners

Reference 12

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This paper cites Re-evaluating the role of Bleu in machine translation research.

QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Re-evaluating the role of Bleu in machine translation research

Reference 13

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Observation ec179f67-b817-4a0a-9f01-abc7543741e8 · outbound

This paper cites PreCo: A large-scale dataset in preschool vocabulary for coreference resolution.

QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting PreCo: A large-scale dataset in preschool vocabulary for coreference resolution

Reference 14

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Observation 0f3d3a4e-9f07-4e89-93f1-1170dcf1a2ef · outbound

This paper cites PRompt optimization in multi-step tasks (PROMST): Integrating human feedback and heuristic-based sampling.

QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting PRompt optimization in multi-step tasks (PROMST): Integrating human feedback and heuristic-based sampling

Reference 15

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Observation deecffa9-1139-4062-a9e7-e62eaa2b2d2b · outbound

This paper cites Multiq&a: An analysis in measuring robustness via automated crowdsourcing of question perturbations and answers, 2025.

QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Multiq&a: An analysis in measuring robustness via automated crowdsourcing of question perturbations and answers, 2025

Reference 16

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Observation 969ca865-729b-4c3b-af75-80a3c3de5991 · outbound

This paper cites Fishnet: Financial intelligence from sub-querying, harmonizing, neural-conditioning, expert swarms, and task planning.

QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Fishnet: Financial intelligence from sub-querying, harmonizing, neural-conditioning, expert swarms, and task planning

Reference 17

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Observation a7e3886e-d41b-4ca8-bf2a-a6cf1ec5ca53 · outbound

This paper cites Deep reinforcement learning from human preferences.

QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Deep reinforcement learning from human preferences

Reference 18

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Observation c6ee6f37-36f6-4ba8-bf7a-d76d08b9732e · outbound

This paper cites Boolq: Exploring the surprising difficulty of natural yes/no questions.

QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Boolq: Exploring the surprising difficulty of natural yes/no questions

Reference 19

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This paper cites CLARK and RICHARD J.

QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting CLARK and RICHARD J

Reference 20

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This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 21

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Unresolved cited work

Reference 22

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This paper cites Selectively answering ambiguous questions.

QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Selectively answering ambiguous questions

Reference 23

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting hallucinations

Reference 24

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Observation 7cfa65ef-f03d-4649-b611-b4b3ccdfd1b3 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 25

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Observation ba5c1a11-3b36-46d3-8381-8aa81534249f · outbound

This paper cites Prompt- ing and evaluating large language models for proactive dialogues: Clarification, target-guided, and non-collaboration.

QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Prompt- ing and evaluating large language models for proactive dialogues: Clarification, target-guided, and non-collaboration

Reference 26

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Observation 61c2836c-e3e3-47ee-8d72-e2d23d27def9 · outbound

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves

Reference 27

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This paper cites Towards Measuring the Representation of Subjective Global Opinions in Language Models.

QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Towards Measuring the Representation of Subjective Global Opinions in Language Models

Reference 28

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Observation faacd8ce-b9aa-430d-935f-7afa3d8e33ca · outbound

This paper cites doi: 10.18653/v1/2023.findings-emnlp.711.

QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting doi: 10.18653/v1/2023.findings-emnlp.711

Reference 29

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Observation 6e4f70b8-77a8-46e1-9601-086eb494ed7c · outbound

This paper cites GPTScore: Evaluate as You Desire.

QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting GPTScore: Evaluate as You Desire

Reference 30

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This paper cites Retrieval-Augmented Generation for Large Language Models: A Survey.

QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Retrieval-Augmented Generation for Large Language Models: A Survey

Reference 31

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This paper cites On the comparative and absolute readings of superlatives, 2000.

QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting On the comparative and absolute readings of superlatives, 2000

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Unresolved cited work

Reference 33

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Polysemy—Evidence from linguistics, behavioral science, and contextualized language models

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting The kl-ucb algorithm for bounded stochastic bandits and beyond,

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Measuring massive multitask language understanding

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This paper cites Leveraging Affirmative Interpretations from Negation Improves Natural Language Understanding.

QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Leveraging Affirmative Interpretations from Negation Improves Natural Language Understanding

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This paper cites A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions.ACM Transactions on Information Systems, 43(2):1–55, January 2025.

QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions.ACM Transactions on Information Systems, 43(2):1–55, January 2025

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This paper cites Approximation to bayes risk in repeated play, 1957.

QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Approximation to bayes risk in repeated play, 1957

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Towards mitigating LLM hallucination via self reflection

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Towards mitigating hallucination in large language models via self-reflection, 2023

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Models

Reference 42

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Adaptive-RAG: Learning to adapt retrieval-augmented large language models through question complexity

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This paper cites TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension.

QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting doi: 10.18653/v1/2023.findings-emnlp.123

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Scope ambiguities in large language models

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This paper cites Presupposition: What went wrong?, 2016.

QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Presupposition: What went wrong?, 2016

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Unresolved cited work

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This paper cites Enriching rare word representations in neural language models by embedding matrix augmentation.

QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Enriching rare word representations in neural language models by embedding matrix augmentation

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Observation d3ee4595-f4e2-48bc-9179-9fbea772de6e · outbound

This paper cites Efficient algorithms for online decision problems.Journal of Computer and System Sciences, 71(3):291–307, 2005.

QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Efficient algorithms for online decision problems.Journal of Computer and System Sciences, 71(3):291–307, 2005

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Resolution: verified exact; doi, observed 2026-08-05T17:39:30.726492Z

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Unresolved cited work

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Bandit algorithms

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This paper cites Negated and misprimed probes for pretrained language models: Birds can talk, but cannot fly.

QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Negated and misprimed probes for pretrained language models: Birds can talk, but cannot fly

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Factuality Enhanced Language Models for Open-Ended Text Generation

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Aligning language models to explicitly handle ambiguity

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This paper cites Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.

QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

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Resolution: unresolved; no resolver link, observed 2026-08-05T17:39:23.217040Z

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Long-context LLMs Struggle with Long In-context Learning

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback

Reference 58

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting A comparison of most-to-least and least-to-most prompting on the acquisition of solitary play skills, 2008

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Levinson

Reference 60

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting ORANGE: a method for evaluating automatic evaluation metrics for machine translation

Reference 61

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting TruthfulQA: Measuring how models mimic human falsehoods

Reference 62

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting ReMax: A Simple, Effective, and Efficient Reinforcement Learning Method for Aligning Large Language Models

Reference 63

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Query rewriting via large language models, 2024

Reference 64

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting ROUGE: A package for automatic evaluation of summaries

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting G-eval: Nlg evaluation using gpt-4 with better human alignment, 2023

Reference 66

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Subjective topic meets LLMs: Unleashing comprehensive, reflective and creative thinking through the negation of negation

Reference 67

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting We‘re afraid language models aren‘t modeling ambiguity

Reference 68

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Self-Refine: Iterative Refinement with Self-Feedback

Reference 69

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Lost in the Middle: How Language Models Use Long Contexts

Reference 70

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting A Survey of Algorithms and Analysis for Adaptive Online Learning

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Can a suit of armor conduct electricity? a new dataset for open book question answering

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Query Rewriting for Retrieval-Augmented Large Language Models

Reference 73

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Efficient and robust algorithms for adversarial linear contextual bandits

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting RaFe: Ranking feedback improves query rewriting for RAG

Reference 75

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Openai o3 and o4-mini system card, 2025

Reference 76

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting (more) efficient reinforcement learning via posterior sampling

Reference 77

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Controlled Decoding from Language Models

Reference 78

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Bleu: a method for automatic evaluation of machine translation

Reference 79

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Task-oriented query reformulation with reinforcement learn- ing

Reference 80

Resolution: verified exact; doi, observed 2026-08-05T17:39:29.694686Z

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Superlatives in Context: Modeling the Implicit Semantics of Superlatives

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Mutual-enhanced incongruity learning network for multi-modal sarcasm detection

Reference 82

Resolution: verified exact; doi, observed 2026-08-05T17:39:29.453743Z

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Training language models to follow instructions with human feedback

Reference 83

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Squad: 100,000+ questions for machine comprehension of text, 2016

Reference 84

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer

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Resolution: unresolved; no resolver link, observed 2026-08-05T17:39:25.199857Z

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Continual learning in environments with polynomial mixing times

Reference 86

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Improving word sense disambiguation in neural machine translation with salient document context, 2023

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Language models are unsupervised multitask learners

Reference 88

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Sadock and Arnold M

Reference 89

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Know what you don’t know: Unanswerable questions for squad, 2018

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Rare Words: A Major Problem for Contextualized Embeddings And How to Fix it by Attentive Mimicking

Reference 91

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Monotone operators and the proximal point algorithm

Reference 93

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Anaphoric reference to events and actions: A representation and its advantages

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Van Der Sandt

Reference 95

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Online learning and online convex optimization

Reference 96

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-05T17:39:26.622903Z

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Proximal Policy Optimization Algorithms

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Follow the Perturbed Leader: Optimism and Fast Parallel Algorithms for Smooth Minimax Games

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Entity-aware ELMo: Learning Contextual Entity Representation for Entity Disambiguation

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Observation a4fadbc2-4881-4e56-8415-0d17a88005d9 · outbound

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Thompson

Reference 101

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QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Ai hallucinations: Chatgpt and google’s challenges

Reference 102

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