Paper Citation Record · LEDGER

Guiding LLM Decision-Making with Fairness Reward Models

As of 7 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 1 inbound Pith citation observation for arXiv:2507.11344.

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

pith.paper-citation-record.v1
2507.11344 v1

Coverage vector

measured 52 of 52 reference resolution

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One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

52 of 52 outbound references displayed

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External citation measurements

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Outbound references

Observation cc0a3585-45aa-400c-838d-eacbb62a00c1 · outbound

This paper cites Measuring Gender and Racial Biases in Large Language Models.

Guiding LLM Decision-Making with Fairness Reward Models Measuring Gender and Racial Biases in Large Language Models

Reference 1

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Observation 4590bfba-9322-4d44-873f-0f74b1f7c85a · outbound

This paper cites Machine bias.

Guiding LLM Decision-Making with Fairness Reward Models Machine bias

Reference 2

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Observation 6913c2f1-197a-417e-ad0d-12708f06defb · outbound

This paper cites Constitutional AI: Harmlessness from AI Feedback.

Guiding LLM Decision-Making with Fairness Reward Models Constitutional AI: Harmlessness from AI Feedback

Reference 3

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Observation bef5e4b2-8799-4ec8-9c16-6493857ec85b · outbound

This paper cites Fairness and Machine Learning.

Guiding LLM Decision-Making with Fairness Reward Models Fairness and Machine Learning

Reference 4

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Observation 3eb81b7a-0873-4112-a139-5ff3577d6dc2 · outbound

This paper cites Scaling test-time compute with open models, 2024.

Guiding LLM Decision-Making with Fairness Reward Models Scaling test-time compute with open models, 2024

Reference 5

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This paper cites On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency, 2021.

Guiding LLM Decision-Making with Fairness Reward Models On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency, 2021

Reference 6

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Observation dc893af8-465b-4303-8687-e6e0658b8567 · outbound

This paper cites Red-Teaming Large Language Models using Chain of Utterances for Safety-Alignment.

Guiding LLM Decision-Making with Fairness Reward Models Red-Teaming Large Language Models using Chain of Utterances for Safety-Alignment

Reference 7

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Observation 92d27a1c-2a61-4ff2-90cc-dfa72e7dab6e · outbound

This paper cites Nuanced metrics for measuring unintended bias with real data for text classification.

Guiding LLM Decision-Making with Fairness Reward Models Nuanced metrics for measuring unintended bias with real data for text classification

Reference 8

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Observation 4262d806-a84a-47fd-b2a0-ee476739ab06 · outbound

This paper cites Large Language Monkeys: Scaling Inference Compute with Repeated Sampling.

Guiding LLM Decision-Making with Fairness Reward Models Large Language Monkeys: Scaling Inference Compute with Repeated Sampling

Reference 9

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This paper cites Alphamath almost zero: Process supervision without process.

Guiding LLM Decision-Making with Fairness Reward Models Alphamath almost zero: Process supervision without process

Reference 10

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Observation dbf97b6b-53b1-4d69-a55f-9a8f909d4e79 · outbound

This paper cites Fair prediction with disparate impact: A study of bias in recidivism prediction instruments.

Guiding LLM Decision-Making with Fairness Reward Models Fair prediction with disparate impact: A study of bias in recidivism prediction instruments

Reference 11

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Observation 343f7e75-8f04-4cd2-94e2-ecf33192fb98 · outbound

This paper cites Bias in bios: A case study of semantic representation bias in a high-stakes setting.

Guiding LLM Decision-Making with Fairness Reward Models Bias in bios: A case study of semantic representation bias in a high-stakes setting

Reference 12

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This paper cites Evaluation of A frican A merican language bias in natural language generation.

Guiding LLM Decision-Making with Fairness Reward Models Evaluation of A frican A merican language bias in natural language generation

Reference 13

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Guiding LLM Decision-Making with Fairness Reward Models The accuracy, fairness, and limits of predicting recidivism

Reference 14

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This paper cites Gaebler, Sharad Goel, Aziz Huq, and Prasanna Tambe.

Guiding LLM Decision-Making with Fairness Reward Models Gaebler, Sharad Goel, Aziz Huq, and Prasanna Tambe

Reference 15

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Observation 0451b8b3-e928-4242-98f0-b59fb8bd9c21 · outbound

This paper cites Gallegos, Ryan A.

Guiding LLM Decision-Making with Fairness Reward Models Gallegos, Ryan A

Reference 16

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This paper cites Debiasing pre-trained language models via efficient fine-tuning.

Guiding LLM Decision-Making with Fairness Reward Models Debiasing pre-trained language models via efficient fine-tuning

Reference 17

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This paper cites Bias in Large Language Models: Origin, Evaluation, and Mitigation.

Guiding LLM Decision-Making with Fairness Reward Models Bias in Large Language Models: Origin, Evaluation, and Mitigation

Reference 18

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This paper cites Equality of opportunity in supervised learning.

Guiding LLM Decision-Making with Fairness Reward Models Equality of opportunity in supervised learning

Reference 19

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Observation 38241025-f83b-4642-b5c0-b8ff424c9674 · outbound

This paper cites V- ST ar: Training verifiers for self-taught reasoners.

Guiding LLM Decision-Making with Fairness Reward Models V- ST ar: Training verifiers for self-taught reasoners

Reference 20

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Observation 947a5ebd-3ab4-4f84-bdae-3aba1c02254e · outbound

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Guiding LLM Decision-Making with Fairness Reward Models Prompting Techniques for Reducing Social Bias in LLMs through System 1 and System 2 Cognitive Processes

Reference 21

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Guiding LLM Decision-Making with Fairness Reward Models Evaluating Gender Bias in Large Language Models via Chain-of-Thought Prompting

Reference 22

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Guiding LLM Decision-Making with Fairness Reward Models Gender bias and stereotypes in large language models

Reference 23

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Guiding LLM Decision-Making with Fairness Reward Models When do pre-training biases propagate to downstream tasks? a case study in text summarization

Reference 24

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Guiding LLM Decision-Making with Fairness Reward Models Let's verify step by step

Reference 25

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Guiding LLM Decision-Making with Fairness Reward Models Debiasing large language models with structured knowledge

Reference 26

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Guiding LLM Decision-Making with Fairness Reward Models Fairness-guided few-shot prompting for large language models

Reference 27

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Guiding LLM Decision-Making with Fairness Reward Models Evaluating Gender Bias Transfer between Pre-trained and Prompt-Adapted Language Models

Reference 28

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Guiding LLM Decision-Making with Fairness Reward Models GPT-4 Technical Report

Reference 29

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Guiding LLM Decision-Making with Fairness Reward Models Bias in word embeddings

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Guiding LLM Decision-Making with Fairness Reward Models BBQ : A hand-built bias benchmark for question answering

Reference 31

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Guiding LLM Decision-Making with Fairness Reward Models Divine LL a MA s: Bias, stereotypes, stigmatization, and emotion representation of religion in large language models

Reference 32

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Guiding LLM Decision-Making with Fairness Reward Models Proximal Policy Optimization Algorithms

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Guiding LLM Decision-Making with Fairness Reward Models On second thought, let ' s not think step by step! bias and toxicity in zero-shot reasoning

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Guiding LLM Decision-Making with Fairness Reward Models DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 35

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Guiding LLM Decision-Making with Fairness Reward Models Scaling LLM test-time compute optimally can be more effective than scaling parameters for reasoning

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Guiding LLM Decision-Making with Fairness Reward Models Unveiling Gender Bias in Terms of Profession Across LLMs: Analyzing and Addressing Sociological Implications

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Guiding LLM Decision-Making with Fairness Reward Models Toward self-improvement of llms via imagination, searching, and criticizing

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Guiding LLM Decision-Making with Fairness Reward Models Llama 2: Open Foundation and Fine-Tuned Chat Models

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Guiding LLM Decision-Making with Fairness Reward Models Unresolved cited work

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Guiding LLM Decision-Making with Fairness Reward Models Solving math word problems with process- and outcome-based feedback

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Guiding LLM Decision-Making with Fairness Reward Models ``kelly is a warm person, joseph is a role model'': Gender biases in LLM -generated reference letters

Reference 42

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Guiding LLM Decision-Making with Fairness Reward Models Alphazero-like tree-search can guide large language model decoding and training

Reference 43

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Guiding LLM Decision-Making with Fairness Reward Models Math-shepherd: Verify and reinforce LLM s step-by-step without human annotations

Reference 44

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Guiding LLM Decision-Making with Fairness Reward Models Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou

Reference 45

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Guiding LLM Decision-Making with Fairness Reward Models Chain-of-thought prompting elicits reasoning in large language models

Reference 46

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Guiding LLM Decision-Making with Fairness Reward Models Blueprint for an ai bill of rights: Making automated systems work for the american people, 2022

Reference 47

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This paper cites Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrieval, page 1578–1590.

Guiding LLM Decision-Making with Fairness Reward Models Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrieval, page 1578–1590

Reference 48

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Guiding LLM Decision-Making with Fairness Reward Models Tree of thoughts: Deliberate problem solving with large language models

Reference 49

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Guiding LLM Decision-Making with Fairness Reward Models Scaling Relationship on Learning Mathematical Reasoning with Large Language Models

Reference 50

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Guiding LLM Decision-Making with Fairness Reward Models Star: Bootstrapping reasoning with reasoning

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:16:37.780741Z

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Observation a49fec15-69e9-48ca-ac85-03ab78d9f5f8 · outbound

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Guiding LLM Decision-Making with Fairness Reward Models Towards effective discrimination testing for generative ai

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:16:37.675829Z

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Pith citing papers

Observation 9610f636-a3ec-4be5-8de9-6de0de29ed12 · inbound

Reward Modeling for Reinforcement Learning-Based LLM Reasoning: Design, Challenges, and Evaluation cites this paper.

Reward Modeling for Reinforcement Learning-Based LLM Reasoning: Design, Challenges, and Evaluation Guiding LLM Decision-Making with Fairness Reward Models

Reference 35

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