Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? | Paper Citation Record

Observation dba8227b-b72d-479f-bde8-1c13692362b6 · outbound

This paper cites Aligning Large Language Models with Human: A Survey.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? Aligning Large Language Models with Human: A Survey

Reference 1

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Observation 7d2ed43c-03db-40a8-bb27-85e3106ca46d · outbound

This paper cites Skywork-Reward: Bag of Tricks for Reward Modeling in LLMs.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? Skywork-Reward: Bag of Tricks for Reward Modeling in LLMs

Reference 2

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This paper cites UltraFeedback: Boosting Language Models with Scaled AI Feedback.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? UltraFeedback: Boosting Language Models with Scaled AI Feedback

Reference 3

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This paper cites A General Language Assistant as a Laboratory for Alignment.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? A General Language Assistant as a Laboratory for Alignment

Reference 4

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

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 5

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Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? Unresolved cited work

Reference 6

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Observation 90b915a2-73f1-4b6a-b678-03d3275cabb1 · outbound

This paper cites Manning, Stefano Ermon, and Chelsea Finn.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? Manning, Stefano Ermon, and Chelsea Finn

Reference 7

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Observation bdd6cb4c-e052-4261-b266-da5ac02872ac · outbound

This paper cites From Lists to Emojis: How Format Bias Affects Model Alignment.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? From Lists to Emojis: How Format Bias Affects Model Alignment

Reference 8

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This paper cites Offsetbias: Leveraging debiased data for tuning evaluators.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? Offsetbias: Leveraging debiased data for tuning evaluators

Reference 9

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Observation 0c05f996-aa3d-40b2-97b6-ba0cb90335e9 · outbound

This paper cites Disentangling length from quality in direct preference optimization.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? Disentangling length from quality in direct preference optimization

Reference 10

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Observation aaa623f8-5777-4bc0-9e76-25b3c7eaf379 · outbound

This paper cites KTO: Model Alignment as Prospect Theoretic Optimization.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? KTO: Model Alignment as Prospect Theoretic Optimization

Reference 11

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Observation 14be7723-3d69-4534-914b-b17772787e2b · outbound

This paper cites RewardBench: Evaluating Reward Models for Language Modeling.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? RewardBench: Evaluating Reward Models for Language Modeling

Reference 12

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This paper cites Predictive pipelined decoding: A compute-latency trade-off for exact LLM decoding.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? Predictive pipelined decoding: A compute-latency trade-off for exact LLM decoding

Reference 13

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This paper cites Think Big, Generate Quick: LLM-to-SLM for Fast Autoregressive Decoding.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? Think Big, Generate Quick: LLM-to-SLM for Fast Autoregressive Decoding

Reference 14

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This paper cites SAM Decoding: Speculative Decoding via Suffix Automaton.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? SAM Decoding: Speculative Decoding via Suffix Automaton

Reference 15

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This paper cites S2D: Sorted Speculative Decoding For More Efficient Deployment of Nested Large Language Models.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? S2D: Sorted Speculative Decoding For More Efficient Deployment of Nested Large Language Models

Reference 16

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Observation 29734963-f7b4-4944-8d9e-0e41c3a07c99 · outbound

This paper cites The unlocking spell on base llms: Rethinking alignment via in-context learning.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? The unlocking spell on base llms: Rethinking alignment via in-context learning

Reference 17

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Observation 70a963a9-1809-427e-9e7d-f6048fc901e8 · outbound

This paper cites Safety Alignment Should Be Made More Than Just a Few Tokens Deep.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? Safety Alignment Should Be Made More Than Just a Few Tokens Deep

Reference 18

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This paper cites Christiano, Jan Leike, Tom B.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? Christiano, Jan Leike, Tom B

Reference 19

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This paper cites GPT-4o System Card.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? GPT-4o System Card

Reference 20

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Observation 8e0cc3c2-ba1f-4e04-b503-9cce0e585656 · outbound

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

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? Gemini: A Family of Highly Capable Multimodal Models

Reference 21

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This paper cites The Llama 3 Herd of Models.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? The Llama 3 Herd of Models

Reference 22

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This paper cites On the weaknesses of reinforcement learning for neural machine translation.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? On the weaknesses of reinforcement learning for neural machine translation

Reference 23

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Observation dc0ec6a0-0fe7-4e1f-8560-d83ff9f57534 · outbound

This paper cites Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

Reference 24

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Observation f2cf9eaa-5d02-454e-91db-cc41a6877c14 · outbound

This paper cites SLiC-HF: Sequence Likelihood Calibration with Human Feedback.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? SLiC-HF: Sequence Likelihood Calibration with Human Feedback

Reference 25

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This paper cites A general theoretical paradigm to understand learning from human preferences.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? A general theoretical paradigm to understand learning from human preferences

Reference 26

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Observation 0fa5c61f-94c3-481a-8301-bcdcf867e642 · outbound

This paper cites Generalized preference optimization: A unified approach to offline alignment.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? Generalized preference optimization: A unified approach to offline alignment

Reference 27

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Observation cbfe94fd-9683-4d9b-802f-3a45d98322da · outbound

This paper cites TreeBoN: Enhancing Inference-Time Alignment with Speculative Tree-Search and Best-of-N Sampling.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? TreeBoN: Enhancing Inference-Time Alignment with Speculative Tree-Search and Best-of-N Sampling

Reference 28

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This paper cites MaxMin-RLHF: Alignment with Diverse Human Preferences.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? MaxMin-RLHF: Alignment with Diverse Human Preferences

Reference 29

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Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? Unresolved cited work

Reference 30

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This paper cites Enabling Language Models to Implicitly Learn Self-Improvement.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? Enabling Language Models to Implicitly Learn Self-Improvement

Reference 31

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This paper cites Mankowitz, Doina Precup, and Bilal Piot.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? Mankowitz, Doina Precup, and Bilal Piot

Reference 32

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This paper cites A minimaximalist approach to reinforcement learning from human feedback.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? A minimaximalist approach to reinforcement learning from human feedback

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:15:46.938379Z

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Observation 4a78435b-0abb-422c-95b9-2722526d7a4a · outbound

This paper cites Online Iterative Reinforcement Learning from Human Feedback with General Preference Model.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? Online Iterative Reinforcement Learning from Human Feedback with General Preference Model

Reference 34

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This paper cites Llm-blender: Ensembling large language models with pairwise ranking and generative fusion.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? Llm-blender: Ensembling large language models with pairwise ranking and generative fusion

Reference 35

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This paper cites Liu, and Jialu Liu.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? Liu, and Jialu Liu

Reference 36

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Observation f9c63e0b-c0fc-4171-ac2c-44ef9c55fbb5 · outbound

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Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 38

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This paper cites Helpsteer: Multi-attribute helpfulness dataset for steerlm.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? Helpsteer: Multi-attribute helpfulness dataset for steerlm

Reference 39

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This paper cites Interpretable preferences via multi- objective reward modeling and mixture-of-experts.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? Interpretable preferences via multi- objective reward modeling and mixture-of-experts

Reference 40

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Observation 213384ad-82ee-49b3-a8c8-1caa91b66d9e · outbound

This paper cites WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct

Reference 41

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Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? Let’s verify step by step

Reference 42

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Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? Process Reward Model with Q-Value Rankings

Reference 43

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This paper cites Glore: When, where, and how to improve LLM reasoning via global and local refinements.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? Glore: When, where, and how to improve LLM reasoning via global and local refinements

Reference 44

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This paper cites Defining and Characterizing Reward Hacking.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? Defining and Characterizing Reward Hacking

Reference 45

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This paper cites Arjona-Medina, Michael Gillhofer, Michael Widrich, Thomas Unterthiner, Johannes Brandstetter, and Sepp Hochreiter.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? Arjona-Medina, Michael Gillhofer, Michael Widrich, Thomas Unterthiner, Johannes Brandstetter, and Sepp Hochreiter

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:15:46.870280Z

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Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? The effects of reward misspecification: Mapping and mitigating misaligned models

Reference 47

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This paper cites Ball, Oleh Rybkin, Stephen Roberts, Tim Rocktäschel, and Edward Grefenstette.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? Ball, Oleh Rybkin, Stephen Roberts, Tim Rocktäschel, and Edward Grefenstette

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:15:46.847364Z

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Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? Correlated proxies: A new definition and improved mitigation for reward hacking, 2024

Reference 49

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This paper cites Inform: Mitigating reward hacking in RLHF via information-theoretic reward modeling.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? Inform: Mitigating reward hacking in RLHF via information-theoretic reward modeling

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:15:46.829352Z

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Observation 80e08e02-5c8f-49fd-9790-1f82d1c7f942 · outbound

This paper cites When Can Proxies Improve the Sample Complexity of Preference Learning?.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? When Can Proxies Improve the Sample Complexity of Preference Learning?

Reference 51

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Observation d803e9ca-2919-40ff-b1af-1d9199def227 · outbound

This paper cites RLHF Workflow: From Reward Modeling to Online RLHF.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? RLHF Workflow: From Reward Modeling to Online RLHF

Reference 52

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Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? Unresolved cited work

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This paper cites Patterson, Joseph Gonzalez, Urs Hölzle, Quoc V.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? Patterson, Joseph Gonzalez, Urs Hölzle, Quoc V

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:15:46.809151Z

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Observation 17ab31b6-f920-40fa-8e10-4b4deff297ea · outbound

This paper cites OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T15:15:46.451629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc602100-b4fc-47c2-9193-c4e43c8c19b9 · outbound

This paper cites Iterative preference learning from human feedback: Bridging theory and practice for rlhf under kl-constraint, 2024.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? Iterative preference learning from human feedback: Bridging theory and practice for rlhf under kl-constraint, 2024

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-07T15:15:46.455296Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 75172685-d121-4f34-a808-f78df1158f0f · outbound

This paper cites Hashimoto.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? Hashimoto

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T15:15:46.458257Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b6032cc0-7331-424c-9e6d-7ad104a6d1c8 · outbound

This paper cites Hashimoto.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? Hashimoto

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T15:15:46.462329Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1aa69f0-b56e-4868-ab57-addb7a5ebd00 · outbound

This paper cites Understanding dataset difficulty with V-usable information.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? Understanding dataset difficulty with V-usable information

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:15:46.773272Z

Source-reported events for the cited work

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Observation 0f4afe28-e1cd-4cad-a579-c1e49c088abb · outbound

This paper cites an unresolved cited work.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? Unresolved cited work

Reference 2024

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers