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Paper Citation Record · LEDGER

Towards Efficient and Effective Alignment of Large Language Models

As of 7 August 2026, this Paper Citation Record lists 100 of 240 outbound references and 0 inbound Pith citation observations for arXiv:2506.09329.

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

pith.paper-citation-record.v1
2506.09329 v1

Coverage vector

measured 100 of 240 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:55:42.739229Z

measured 100 of 100 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

100 of 240 outbound references displayed

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  • verified fuzzy0
  • unresolved99
  • parse uncertain0
  • malformed identifier0
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Outbound references

Observation 2984a716-55b6-4c05-b1c8-2b424716993a · outbound

This paper cites Explanations for commonsenseqa: New dataset and models.

Towards Efficient and Effective Alignment of Large Language Models Explanations for commonsenseqa: New dataset and models

Reference 1

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Observation 3f267181-8541-4f56-8cc2-a05b3561a99d · outbound

This paper cites Perplexed by Perplexity: Perplexity-Based Data Pruning With Small Reference Models.

Towards Efficient and Effective Alignment of Large Language Models Perplexed by Perplexity: Perplexity-Based Data Pruning With Small Reference Models

Reference 2

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Observation db1cb79b-10f6-44c7-8f30-7c5326d99938 · outbound

This paper cites arxiv dataset, 2023.

Towards Efficient and Effective Alignment of Large Language Models arxiv dataset, 2023

Reference 3

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Observation cb4f1d21-4f1b-402d-8515-1dec66a8baf6 · outbound

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

Towards Efficient and Effective Alignment of Large Language Models A General Language Assistant as a Laboratory for Alignment

Reference 4

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Observation f7fd65e2-7744-48f4-8cae-8451897ae442 · outbound

This paper cites Program Synthesis with Large Language Models.

Towards Efficient and Effective Alignment of Large Language Models Program Synthesis with Large Language Models

Reference 5

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Observation 2a2f7029-2d82-4d25-aec4-2ffc1fea85c2 · outbound

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

Towards Efficient and Effective Alignment of Large Language Models A general theoretical paradigm to understand learning from human preferences

Reference 6

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Observation 6ddee455-f96b-4ce2-bf8e-d8d01ce24816 · outbound

This paper cites an unresolved cited work.

Towards Efficient and Effective Alignment of Large Language Models Unresolved cited work

Reference 7

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Observation ae4a5deb-b684-4c43-9b55-2da9714a0fc7 · outbound

This paper cites Qwen Technical Report.

Towards Efficient and Effective Alignment of Large Language Models Qwen Technical Report

Reference 8

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Observation 472e8260-1600-4b07-bc77-c0c6e79bd3a3 · outbound

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

Towards Efficient and Effective Alignment of Large Language Models Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 9

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Observation 9141b438-901d-4072-85d8-052d26f42ca4 · outbound

This paper cites Constitutional ai: Harmlessness from ai feedback.

Towards Efficient and Effective Alignment of Large Language Models Constitutional ai: Harmlessness from ai feedback

Reference 10

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Observation bc625a8d-77c2-4461-a291-d2866151238f · outbound

This paper cites Baichuan 2: Open Large-scale Language Models.

Towards Efficient and Effective Alignment of Large Language Models Baichuan 2: Open Large-scale Language Models

Reference 11

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Observation a4795861-b5bd-4e17-ac48-cd90fe38235d · outbound

This paper cites PIQA: reasoning about physical commonsense in natural language.

Towards Efficient and Effective Alignment of Large Language Models PIQA: reasoning about physical commonsense in natural language

Reference 12

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Observation f2613a1f-022c-4fcb-ac75-997afd90fee9 · outbound

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

Towards Efficient and Effective Alignment of Large Language Models On the Opportunities and Risks of Foundation Models

Reference 13

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Observation cf7bd0d9-897a-407f-8a08-5f12ee0be21f · outbound

This paper cites Rank analysis of incomplete block designs: I.

Towards Efficient and Effective Alignment of Large Language Models Rank analysis of incomplete block designs: I

Reference 14

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Observation 243e65c4-9ddc-4074-a351-1794a3c2c60f · outbound

This paper cites On the resemblance and containment of documents.

Towards Efficient and Effective Alignment of Large Language Models On the resemblance and containment of documents

Reference 15

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Observation 9cb5a7d5-bc30-470d-a784-98838ae56b84 · outbound

This paper cites Language models are few-shot learners.

Towards Efficient and Effective Alignment of Large Language Models Language models are few-shot learners

Reference 16

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Observation ca627d7f-b4dc-44e0-b650-d68c428947e6 · outbound

This paper cites Data Diversity Matters for Robust Instruction Tuning.

Towards Efficient and Effective Alignment of Large Language Models Data Diversity Matters for Robust Instruction Tuning

Reference 17

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Observation 6833eeb0-05f9-4dac-bc72-f0be0760cb3f · outbound

This paper cites Drlc: Reinforcement learning with dense rewards from llm critic.

Towards Efficient and Effective Alignment of Large Language Models Drlc: Reinforcement learning with dense rewards from llm critic

Reference 18

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Observation dcf1a6d7-877f-4d33-8226-a98385e3554e · outbound

This paper cites Instruction Mining: Instruction Data Selection for Tuning Large Language Models.

Towards Efficient and Effective Alignment of Large Language Models Instruction Mining: Instruction Data Selection for Tuning Large Language Models

Reference 19

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Observation 48a84e31-6eba-420c-a629-95694fd796b3 · outbound

This paper cites Wit3: Web inventory of transcribed and translated talks.

Towards Efficient and Effective Alignment of Large Language Models Wit3: Web inventory of transcribed and translated talks

Reference 20

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Observation 86d7480e-1b5e-40eb-ac40-e92c2671b1f0 · outbound

This paper cites Dense reward for free in reinforcement learning from human feedback.

Towards Efficient and Effective Alignment of Large Language Models Dense reward for free in reinforcement learning from human feedback

Reference 21

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Observation f5508cc0-8a67-49c5-9e72-5f3b9356a530 · outbound

This paper cites Maybe Only 0.5% Data is Needed: A Preliminary Exploration of Low Training Data Instruction Tuning.

Towards Efficient and Effective Alignment of Large Language Models Maybe Only 0.5% Data is Needed: A Preliminary Exploration of Low Training Data Instruction Tuning

Reference 22

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Observation c2bc87fc-1d24-4014-87b0-f5b109d3af90 · outbound

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Towards Efficient and Effective Alignment of Large Language Models Controllable Text Generation with Language Constraints

Reference 23

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Observation 4fc93f12-58ed-420f-af94-e0141db37032 · outbound

This paper cites AlpaGasus: Training A Better Alpaca with Fewer Data.

Towards Efficient and Effective Alignment of Large Language Models AlpaGasus: Training A Better Alpaca with Fewer Data

Reference 24

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Observation e8374c56-37a3-4279-ad72-ec912cf5ccd9 · outbound

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Towards Efficient and Effective Alignment of Large Language Models Unresolved cited work

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Observation 3a881804-68d0-4530-bfb3-cc9bd2359f82 · outbound

This paper cites DoG- instruct: Towards premium instruction-tuning data via text-grounded instruction wrapping.

Towards Efficient and Effective Alignment of Large Language Models DoG- instruct: Towards premium instruction-tuning data via text-grounded instruction wrapping

Reference 26

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Observation 2a12aa9c-f84a-4aa2-9cd0-4835b5f0dbaa · outbound

This paper cites Improving large language models via fine-grained 111 reinforcement learning with minimum editing constraint.

Towards Efficient and Effective Alignment of Large Language Models Improving large language models via fine-grained 111 reinforcement learning with minimum editing constraint

Reference 27

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Observation 4d6f2caf-d786-4d3a-81c4-dba3f2d71162 · outbound

This paper cites Low-redundant optimization for large language model alignment.

Towards Efficient and Effective Alignment of Large Language Models Low-redundant optimization for large language model alignment

Reference 28

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Observation 80803c46-36c0-48d6-889f-552a222edb19 · outbound

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Towards Efficient and Effective Alignment of Large Language Models Replacing Language Model for Style Transfer

Reference 29

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Observation eb703fef-2a92-40c8-9583-3552aeafc88b · outbound

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Towards Efficient and Effective Alignment of Large Language Models Unresolved cited work

Reference 30

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Observation 7b778db6-1805-49c2-997e-dad6126a2f32 · outbound

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Towards Efficient and Effective Alignment of Large Language Models Gonzalez, Ion Stoica, and Eric P

Reference 31

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Observation 311459ef-77b8-4637-859b-a47450733117 · outbound

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Towards Efficient and Effective Alignment of Large Language Models Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

Reference 32

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Observation bae9199e-0e62-4fbf-aadf-6df4203faff9 · outbound

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Towards Efficient and Effective Alignment of Large Language Models Unresolved cited work

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Observation e0423424-6cce-4c0b-801a-cbe6ae3f682c · outbound

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Towards Efficient and Effective Alignment of Large Language Models Christiano, Jan Leike, Tom B

Reference 34

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Observation 29a15694-57eb-46dc-b94e-a18d5928a149 · outbound

This paper cites All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated Text.

Towards Efficient and Effective Alignment of Large Language Models All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated Text

Reference 35

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Observation d4a9d89c-da52-47f0-a837-41137e909365 · outbound

This paper cites Think you have solved question answering? try arc, the ai2 reasoning challenge, 2018.

Towards Efficient and Effective Alignment of Large Language Models Think you have solved question answering? try arc, the ai2 reasoning challenge, 2018

Reference 36

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Observation 21c9fbf6-ba16-4326-9e2f-0c82a5569734 · outbound

This paper cites The future landscape of large language models in medicine.

Towards Efficient and Effective Alignment of Large Language Models The future landscape of large language models in medicine

Reference 37

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Observation ef90d198-6b0c-4258-a85e-cdeaa7796aa6 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Towards Efficient and Effective Alignment of Large Language Models Training Verifiers to Solve Math Word Problems

Reference 38

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Observation 5da014c8-c844-4ace-b009-5c6e0c6f2402 · outbound

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Towards Efficient and Effective Alignment of Large Language Models Evaluating the Ripple Effects of Knowledge Editing in Language Models

Reference 39

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Observation 56000ff9-8ca0-450d-9a6f-ce458accef52 · outbound

This paper cites Redpajama: An open source recipe to reproduce llama training dataset, 2023.

Towards Efficient and Effective Alignment of Large Language Models Redpajama: An open source recipe to reproduce llama training dataset, 2023

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Observation f103fa79-936b-48a6-abe1-a63c7af4b37c · outbound

This paper cites Free dolly: Introducing the world’s first truly open instruction-tuned llm, 2023.

Towards Efficient and Effective Alignment of Large Language Models Free dolly: Introducing the world’s first truly open instruction-tuned llm, 2023

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source=pdf_text observed=2026-08-07T04:55:36.803603Z digest=sha256:0ea766960902a2f1ce22f8eb3206cfb9be0124025977431a939e42c08412d763

Observation 90f2194c-0d28-45a6-b9dc-84946743b6f0 · outbound

This paper cites Opencompass: A universal evaluation platform for foundation models.

Towards Efficient and Effective Alignment of Large Language Models Opencompass: A universal evaluation platform for foundation models

Reference 42

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source=pdf_text observed=2026-08-07T04:55:36.895228Z digest=sha256:ae918c5ee73e2e84e9d1c254c601d06868ec804fc0bcda89a9e5b9f58158382a

Observation ed021ca8-dc4c-448c-97f5-7cc5f5219fe0 · outbound

This paper cites Ultrafeedback: Boosting language models with high-quality feedback.

Towards Efficient and Effective Alignment of Large Language Models Ultrafeedback: Boosting language models with high-quality feedback

Reference 43

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source=pdf_text observed=2026-08-07T04:55:37.011778Z digest=sha256:9b3ba2f914a48138e35ab09fa1e0c21ede55ab57af0faf0a395fcb9ad16594a4

Observation 06bbaec5-b94b-4928-b947-72112cd1f1ff · outbound

This paper cites Knowl- edge neurons in pretrained transformers.

Towards Efficient and Effective Alignment of Large Language Models Knowl- edge neurons in pretrained transformers

Reference 44

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source=pdf_text observed=2026-08-07T04:55:37.123213Z digest=sha256:d22ff77813e1c40204edd72058e2632df1f2d8955f283649a488726c0133e90e

Observation 285a82bf-21c0-4aa3-9128-acd15a55a0ee · outbound

This paper cites Editing factual knowledge in language models.

Towards Efficient and Effective Alignment of Large Language Models Editing factual knowledge in language models

Reference 45

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source=pdf_text observed=2026-08-07T04:55:37.253184Z digest=sha256:8f896d38935108fa4ef86b47e77b05825baf5d78e9741e04e0669109282b1826

Observation 1f7ab59e-699c-43a4-a443-7ed6b901c578 · outbound

This paper cites Bert: Pre- training of deep bidirectional transformers for language understanding.

Towards Efficient and Effective Alignment of Large Language Models Bert: Pre- training of deep bidirectional transformers for language understanding

Reference 46

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source=pdf_text observed=2026-08-07T04:55:37.420192Z digest=sha256:9aef5fae5ae54075a1d49a7821dfcbc27e1b4ea88d1309646962d3923b84ce0d

Observation d2bb9c16-8a0e-46ed-80da-cfd566084634 · outbound

This paper cites Enhancing chat language models by scaling high-quality instructional conversations.

Towards Efficient and Effective Alignment of Large Language Models Enhancing chat language models by scaling high-quality instructional conversations

Reference 47

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source=pdf_text observed=2026-08-07T04:55:37.579671Z digest=sha256:4065c467a0d5ded047c4fcf0d151e238c2b5dda6f32f113b00a35efab3ecdccd

Observation 0137709e-846f-4ca1-b11d-d3229c43b3ec · outbound

This paper cites A Survey on In-context Learning.

Towards Efficient and Effective Alignment of Large Language Models A Survey on In-context Learning

Reference 48

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source=pdf_text observed=2026-08-07T04:55:37.750155Z digest=sha256:ef03cf0766dd042a63f97a19459a9f8046c46caeae0fccdc3d9154a5c505615f

Observation 1eeede9d-3cf9-4718-b778-117133eeae85 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Towards Efficient and Effective Alignment of Large Language Models An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 49

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source=pdf_text observed=2026-08-07T04:55:37.923690Z digest=sha256:df18a39b3e610aa6ca9f53704a453d10c27c546392c6b13fc3b893448ee6f238

Observation 3d846989-825f-4387-9fb5-8017ae6e716c · outbound

This paper cites Glm: General language model pretraining with autoregressive blank infilling.

Towards Efficient and Effective Alignment of Large Language Models Glm: General language model pretraining with autoregressive blank infilling

Reference 50

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source=pdf_text observed=2026-08-07T04:55:38.091691Z digest=sha256:dfa812ff23d4f30877bf3badfb28bbd8750ba86321bce9126f8ee3e30fb3e55d

Observation 94750fad-64cb-41c8-a149-6fe7389653da · outbound

This paper cites The llama 3 herd of models.

Towards Efficient and Effective Alignment of Large Language Models The llama 3 herd of models

Reference 51

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source=pdf_text observed=2026-08-07T04:55:38.245201Z digest=sha256:e570c7bd4cb28dfce52967a8161408bdc5a1cced18b1e98e4efd7c5a105e6943

Observation 88326315-d97e-440e-bab5-8670052f5ef2 · outbound

This paper cites Length- controlled alpacaeval: A simple way to debias automatic evaluators.

Towards Efficient and Effective Alignment of Large Language Models Length- controlled alpacaeval: A simple way to debias automatic evaluators

Reference 52

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source=pdf_text observed=2026-08-07T04:55:38.319172Z digest=sha256:e1e76621da0e7468cda00c1499eafd0e79a0976e817d815f4eff2745af9ea5fa

Observation c0c4a996-b02b-436b-b6bf-e7557988e64a · outbound

This paper cites Fact-checking the output of large language models via token-level uncertainty quantification.

Towards Efficient and Effective Alignment of Large Language Models Fact-checking the output of large language models via token-level uncertainty quantification

Reference 53

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source=pdf_text observed=2026-08-07T04:55:38.532163Z digest=sha256:f2344523c9c6e11ffd39b3cf474c0bb4ffef97635689fe80a3a87edae8fc205d

Observation 75ea9cfe-0741-4625-847a-1ea5d49c56ba · outbound

This paper cites Hierarchical neural story generation.

Towards Efficient and Effective Alignment of Large Language Models Hierarchical neural story generation

Reference 54

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source=pdf_text observed=2026-08-07T04:55:38.690711Z digest=sha256:f0ae98af158f0163272120fd32fedd92d8069970190e774d596b65056d6b6bfd

Observation 99594ee3-0f6e-4faa-b7e4-7a21323074c8 · outbound

This paper cites Data-Free Adversarial Distillation.

Towards Efficient and Effective Alignment of Large Language Models Data-Free Adversarial Distillation

Reference 55

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source=pdf_text observed=2026-08-07T04:55:38.892082Z digest=sha256:d95fb836d396d6786168a2917bebd45a2b92ba7ea9a5efbd4127296b96907cca

Observation 1527546f-9b1d-4030-ac77-7fc2bc4d7c9f · outbound

This paper cites Gptscore: Evaluate as you desire.

Towards Efficient and Effective Alignment of Large Language Models Gptscore: Evaluate as you desire

Reference 56

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source=pdf_text observed=2026-08-07T04:55:39.059708Z digest=sha256:d4bdd81c4820f5c60b24d8d0e124d3331f1fab645c93ef62ea243e2c5d1bdae3

Observation dc021117-b897-4423-bbb1-ab96af9c2935 · outbound

This paper cites Preference learning and ranking by pairwise comparison.

Towards Efficient and Effective Alignment of Large Language Models Preference learning and ranking by pairwise comparison

Reference 57

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source=pdf_text observed=2026-08-07T04:55:39.136611Z digest=sha256:3c210aefe2fd011ff8b3ee30cc6df229024223f1ee4933b711e9793216d94d5e

Observation 806fc54d-e1b9-4b59-9b4b-40a701927a5f · outbound

This paper cites Scaling Synthetic Data Creation with 1,000,000,000 Personas.

Towards Efficient and Effective Alignment of Large Language Models Scaling Synthetic Data Creation with 1,000,000,000 Personas

Reference 58

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source=pdf_text observed=2026-08-07T04:55:39.303687Z digest=sha256:8c9ab89b4c32c42d975f9f15b52c98b1ffd951864b6b744eb902a9b83470b6a5

Observation 45258c39-e762-4c0e-9870-47c535fa60f4 · outbound

This paper cites Koala: A dialogue model for academic research.

Towards Efficient and Effective Alignment of Large Language Models Koala: A dialogue model for academic research

Reference 59

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source=pdf_text observed=2026-08-07T04:55:39.386153Z digest=sha256:035d2f0a2a66cf8aad5444e7f6c42c05d242db157db4962b6eb81f0a6a3cca01

Observation c395c387-ca5e-4b6f-9322-8e9065136efc · outbound

This paper cites Did aristotle use a laptop? A question answering benchmark with implicit rea- soning strategies.

Towards Efficient and Effective Alignment of Large Language Models Did aristotle use a laptop? A question answering benchmark with implicit rea- soning strategies

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source=pdf_text observed=2026-08-07T04:55:39.471185Z digest=sha256:7cfe69083bff43e25ae6bceac02515f0213b44755423edf5e6dd16b5bac1a7b6

Observation 05ffb229-30b1-4835-97db-4bc9ddd22642 · outbound

This paper cites ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks.

Towards Efficient and Effective Alignment of Large Language Models ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks

Reference 61

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source=pdf_text observed=2026-08-07T04:55:39.571857Z digest=sha256:39bb03b8ec5757cde9ec91a58c55e19a04fc971705e82ea0b292381060b40169

Observation a597135c-ec96-4b60-8f72-8079c0c7de82 · outbound

This paper cites SAMSum corpus: A human-annotated dialogue dataset for abstractive summarization.

Towards Efficient and Effective Alignment of Large Language Models SAMSum corpus: A human-annotated dialogue dataset for abstractive summarization

Reference 62

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source=pdf_text observed=2026-08-07T04:55:39.649815Z digest=sha256:3da04c75f59695a62cd7d0d53a3444f41b575ce3b274461c68192a161f38becc

Observation 4b054aa4-b404-47a1-9c83-d2591fb12aba · outbound

This paper cites English gigaword.

Towards Efficient and Effective Alignment of Large Language Models English gigaword

Reference 64

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source=pdf_text observed=2026-08-07T04:55:39.744866Z digest=sha256:7dcb2176783204f3ab75322f63848f74355879b467b0663ba6b17920623135f6

Observation 5bf6af9f-735a-4f6d-bb65-7038a8c7026c · outbound

This paper cites A Survey on LLM-as-a-Judge.

Towards Efficient and Effective Alignment of Large Language Models A Survey on LLM-as-a-Judge

Reference 65

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source=pdf_text observed=2026-08-07T04:55:39.813545Z digest=sha256:facdda3e04cdde07eda674de6d97d91cbc6d7c52ff1b3e74b8109631044f94e7

Observation 67e14756-5e5a-4a91-b91d-d0c912ee003d · outbound

This paper cites The False Promise of Imitating Proprietary LLMs.

Towards Efficient and Effective Alignment of Large Language Models The False Promise of Imitating Proprietary LLMs

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source=pdf_text observed=2026-08-07T04:55:39.919359Z digest=sha256:3015c00076e07ba759bcc663b6cb28cb2217974e9f12b4690d040cc9372d3b52

Observation 17e89c8a-7a3a-467e-92e7-67759058212c · outbound

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

Towards Efficient and Effective Alignment of Large Language Models DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 67

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source=pdf_text observed=2026-08-07T04:55:40.067581Z digest=sha256:2f08742bd07b4f3eb8d46b50fc7dd0b2dbe2b3919feb6b809097fceaeaa1fc0e

Observation a1c3bdf8-4bdc-444b-ba66-8bc874dd732a · outbound

This paper cites Beyond imita- tion: Leveraging fine-grained quality signals for alignment.

Towards Efficient and Effective Alignment of Large Language Models Beyond imita- tion: Leveraging fine-grained quality signals for alignment

Reference 68

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source=pdf_text observed=2026-08-07T04:55:40.217742Z digest=sha256:465c3d9b3b0d92c107862db0ea17d1d60d29c9eab8d266eb006b6532bfaea504

Observation 7c130163-e5d2-404b-b541-1bd4e4c5ed9d · outbound

This paper cites Coopera- tive inverse reinforcement learning.

Towards Efficient and Effective Alignment of Large Language Models Coopera- tive inverse reinforcement learning

Reference 69

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source=pdf_text observed=2026-08-07T04:55:40.344439Z digest=sha256:196c3e8dff2b908f2539b2e668f54a00260f5ab460c0147790c6bba526ae961d

Observation 2a29c6f4-e4a9-45d9-9aaa-af8afe755c70 · outbound

This paper cites Aging with grace: Lifelong model editing with discrete key- value adaptors.

Towards Efficient and Effective Alignment of Large Language Models Aging with grace: Lifelong model editing with discrete key- value adaptors

Reference 70

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source=pdf_text observed=2026-08-07T04:55:40.428933Z digest=sha256:edae6cc77b99c099797373af7c5dfded095106f96fea73714c2b8ca09b7075e5

Observation 6da72c2a-db4f-40a8-8c63-0693d739ca3b · outbound

This paper cites Does localization inform editing? surprising differences in causality-based localization vs.

Towards Efficient and Effective Alignment of Large Language Models Does localization inform editing? surprising differences in causality-based localization vs

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source=pdf_text observed=2026-08-07T04:55:40.544821Z digest=sha256:89f9330a54318b631701af1575fcaa6dee7ad4207af1907a19d65546a5e526a5

Observation 26507359-384b-4a0d-96e1-0992e7bf13a5 · outbound

This paper cites Measuring massive multitask language understanding.

Towards Efficient and Effective Alignment of Large Language Models Measuring massive multitask language understanding

Reference 72

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Observation c7e206f8-3635-444a-b363-ff65ccab6c83 · outbound

This paper cites Measuring mathematical problem solving with the math dataset.

Towards Efficient and Effective Alignment of Large Language Models Measuring mathematical problem solving with the math dataset

Reference 73

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source=pdf_text observed=2026-08-07T04:55:40.743384Z digest=sha256:37b52848ae8d6612395c0ea54167ca04de7c7556bdb89a3e76e6d860ddf89fec

Observation d7a15b53-fe4c-4178-897d-94d96d60c1cc · outbound

This paper cites Knowledge distil- lation with adversarial samples supporting decision boundary.

Towards Efficient and Effective Alignment of Large Language Models Knowledge distil- lation with adversarial samples supporting decision boundary

Reference 74

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source=pdf_text observed=2026-08-07T04:55:40.833002Z digest=sha256:cb955b397b23d17b3875a729a127f0eb0a419861f98c1dc4f38d0b7cd365f6f2

Observation 7e6670d9-1673-4f20-884d-4131418befcc · outbound

This paper cites Orpo: Monolithic preference optimiza- tion without reference model.

Towards Efficient and Effective Alignment of Large Language Models Orpo: Monolithic preference optimiza- tion without reference model

Reference 75

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source=pdf_text observed=2026-08-07T04:55:40.937312Z digest=sha256:7f69b542a630c8c6ecdb2fd8abf888816126dea81cbeb1eb5e473149f64961bf

Observation 0087c7e8-a8ad-4ebe-b022-d24ee74472cd · outbound

This paper cites Parameter- efficient transfer learning for nlp.

Towards Efficient and Effective Alignment of Large Language Models Parameter- efficient transfer learning for nlp

Reference 76

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source=pdf_text observed=2026-08-07T04:55:41.059879Z digest=sha256:0042efb26b7eb731f4cb729e5c85c06cb598e7934c2d2fa40eada0d43df77074

Observation 402a4af6-40ad-40e3-a551-ea01e5a81b92 · outbound

This paper cites LoRA: Low-rank adaptation of large language models.

Towards Efficient and Effective Alignment of Large Language Models LoRA: Low-rank adaptation of large language models

Reference 77

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source=pdf_text observed=2026-08-07T04:55:41.200362Z digest=sha256:011d6b6f8443c5be4fa7537a10c4e09ad8c4b894592e322e0299fee420c611b6

Observation ee0fa57b-89d2-4bf9-b945-d8b416985796 · outbound

This paper cites Is chatgpt better than human annotators? potential and limitations of chatgpt in explaining implicit hate speech.

Towards Efficient and Effective Alignment of Large Language Models Is chatgpt better than human annotators? potential and limitations of chatgpt in explaining implicit hate speech

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source=pdf_text observed=2026-08-07T04:55:41.313569Z digest=sha256:6672951698aa78bfd225c5164ff9f1b69395073049019deb6a5b63f03b032241

Observation ee95b239-2885-4582-8cd6-4481d5800d58 · outbound

This paper cites Deep q-networks.

Towards Efficient and Effective Alignment of Large Language Models Deep q-networks

Reference 79

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source=pdf_text observed=2026-08-07T04:55:41.513883Z digest=sha256:67f106d204820823293e7f6ca0cf51a6556e18c01819b03ae6699d7c7a47e1f0

Observation 7daef58a-0866-472a-995f-316f8004916c · outbound

This paper cites C-eval: A multi-level multi-discipline chinese evaluation suite for foundation models.

Towards Efficient and Effective Alignment of Large Language Models C-eval: A multi-level multi-discipline chinese evaluation suite for foundation models

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Observation 1abe27ca-14dd-4c0e-8082-b16488d5c3ce · outbound

This paper cites Smith, Iz Beltagy, and Han- naneh Hajishirzi.

Towards Efficient and Effective Alignment of Large Language Models Smith, Iz Beltagy, and Han- naneh Hajishirzi

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source=pdf_text observed=2026-08-07T04:55:41.747410Z digest=sha256:6c8955ce36bd7ff760d01a6d582e07f2eddedea3d43685388b076a0b05d10e95

Observation 180d9a92-bf39-47c3-beb0-9ec2378a5c96 · outbound

This paper cites Aligner: Achieving efficient alignment through weak-to-strong correction.

Towards Efficient and Effective Alignment of Large Language Models Aligner: Achieving efficient alignment through weak-to-strong correction

Reference 82

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source=pdf_text observed=2026-08-07T04:55:41.824542Z digest=sha256:e7d724273629019c324699c20a9263d0a63e1bf2eae81c590ba8a1e3d8cd277f

Observation 4476b5a5-3f25-4d18-b90f-72717c8a72c8 · outbound

This paper cites Mistral 7b.

Towards Efficient and Effective Alignment of Large Language Models Mistral 7b

Reference 83

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source=pdf_text observed=2026-08-07T04:55:41.891033Z digest=sha256:2d59297f482b757352eb9a0f2f981bf05daca5b0786f0ec1412b94b818b13b24

Observation 1b7b75c7-ae68-4aa3-9b3a-c2a82a818f46 · outbound

This paper cites What disease does this patient have? a large-scale open domain ques- tion answering dataset from medical exams.

Towards Efficient and Effective Alignment of Large Language Models What disease does this patient have? a large-scale open domain ques- tion answering dataset from medical exams

Reference 84

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source=pdf_text observed=2026-08-07T04:55:41.993198Z digest=sha256:c96ae7b4a84aeeb8cd6144193e6f7181fa5998722f019837120a3d7d477ca845

Observation 122b1fbb-215f-463c-b615-4a683574074e · outbound

This paper cites Plus disease in retinopathy of prematurity: im- proving diagnosis by ranking disease severity and using quantitative image analy- sis.

Towards Efficient and Effective Alignment of Large Language Models Plus disease in retinopathy of prematurity: im- proving diagnosis by ranking disease severity and using quantitative image analy- sis

Reference 85

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source=pdf_text observed=2026-08-07T04:55:42.122707Z digest=sha256:5e48b298740c94c26dd2a81ee78044db5ad00b38e50cdaf3b79c80d519249b0c

Observation 20d8c7ec-1a6b-4cbc-b948-e40b86c96ebf · outbound

This paper cites Scaling Laws for Neural Language Models.

Towards Efficient and Effective Alignment of Large Language Models Scaling Laws for Neural Language Models

Reference 86

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source=pdf_text observed=2026-08-07T04:55:42.209573Z digest=sha256:7e42d03d76614b47556c61fb26bcc1815356b2be21a0385e53f0687f584c276f

Observation 99a690e2-d9ff-46a8-83b2-0e764f46df61 · outbound

This paper cites A survey of reinforcement learning from human feedback.

Towards Efficient and Effective Alignment of Large Language Models A survey of reinforcement learning from human feedback

Reference 87

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source=pdf_text observed=2026-08-07T04:55:42.300537Z digest=sha256:32f6efdcc6ddee8908324087ed6f9edf75ddd317482cb0ba0b94a25a55c48698

Observation 7d79b45b-d7eb-497d-961b-2158e8f2b86f · outbound

This paper cites QASC: A dataset for question answering via sentence composition.

Towards Efficient and Effective Alignment of Large Language Models QASC: A dataset for question answering via sentence composition

Reference 88

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source=pdf_text observed=2026-08-07T04:55:42.373458Z digest=sha256:78dfd351548e0a02a86e05446dc719276d5c99659bcb88382994a33cd6da394a

Observation fccded32-3950-43d9-ae9e-e1a1d997972a · outbound

This paper cites Adam: A method for stochastic optimization.

Towards Efficient and Effective Alignment of Large Language Models Adam: A method for stochastic optimization

Reference 89

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source=pdf_text observed=2026-08-07T04:55:42.435240Z digest=sha256:b67a9cabe913bb1630b57f22fd07eb05c2c61cdff42dd4a8952128e6d5e7c5dd

Observation 1037a898-9a40-4c2b-a389-d9ed6a1b4821 · outbound

This paper cites MAWPS: A math word problem repository.

Towards Efficient and Effective Alignment of Large Language Models MAWPS: A math word problem repository

Reference 90

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source=pdf_text observed=2026-08-07T04:55:42.491049Z digest=sha256:72f9b0bf0c7de2ea0247692abc11e8b836dd6261326dca1b55fc8a2df6200b17

Observation f79c7443-f797-4bc8-9e2e-e91697403498 · outbound

This paper cites Openassistant conversations-democratizing large language model alignment.

Towards Efficient and Effective Alignment of Large Language Models Openassistant conversations-democratizing large language model alignment

Reference 91

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source=pdf_text observed=2026-08-07T04:55:42.554902Z digest=sha256:ee2f3e79ba2d36dfbdecf9fbad7289913ad98a0483640c2d3e794c15919b2435

Observation 755e83e5-26cb-425e-92d1-4a0bbda2db33 · outbound

This paper cites Spoc: Search-based pseudocode to code.

Towards Efficient and Effective Alignment of Large Language Models Spoc: Search-based pseudocode to code

Reference 92

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source=pdf_text observed=2026-08-07T04:55:42.671056Z digest=sha256:c10b529f39a4bd8741a2213cc3243557bb703609b3bca8bb14e36bc111304b2f

Observation 372e1cc8-c5a1-4a4b-b150-799272fb5cfa · outbound

This paper cites MT-Eval: A Multi-Turn Capabilities Evaluation Benchmark for Large Language Models.

Towards Efficient and Effective Alignment of Large Language Models MT-Eval: A Multi-Turn Capabilities Evaluation Benchmark for Large Language Models

Reference 93

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source=pdf_text observed=2026-08-07T04:55:42.704868Z digest=sha256:c93fdf44e4b30a21ed7931afa39ebd07967db680e7f5846671a6388339d8e6d8

Observation dfbe63b4-05e6-4cb6-b276-20b88d796c00 · outbound

This paper cites Large language models in law: A survey.

Towards Efficient and Effective Alignment of Large Language Models Large language models in law: A survey

Reference 94

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source=pdf_text observed=2026-08-07T04:55:42.709802Z digest=sha256:60f4d5ce47489a40465af40fdf668a3a70ceafc50e1070f25c5d3dae8f2d4075

Observation db55201a-1d75-436c-ab20-2d0a6f6d4370 · outbound

This paper cites Ds-1000: A natural and reliable benchmark for data science code generation.

Towards Efficient and Effective Alignment of Large Language Models Ds-1000: A natural and reliable benchmark for data science code generation

Reference 95

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source=pdf_text observed=2026-08-07T04:55:42.713868Z digest=sha256:712bf3e5d3efdb9d8cb5078d6a6bae9be1aacd8aab69e8623756a5b89fc31929

Observation 94a113b0-53e8-43f2-a4fd-ce0854cdf8a7 · outbound

This paper cites RLAIF vs.

Towards Efficient and Effective Alignment of Large Language Models RLAIF vs

Reference 96

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source=pdf_text observed=2026-08-07T04:55:42.718727Z digest=sha256:ba8cedd88ce5391621e7b1409a50a72d436fb4530f8ce8a7aebbe8b6f7aed6c0

Observation 4fc3d327-5031-45a9-bc38-86ab82cea280 · outbound

This paper cites Scalable agent alignment via reward modeling: a research direction.

Towards Efficient and Effective Alignment of Large Language Models Scalable agent alignment via reward modeling: a research direction

Reference 97

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source=pdf_text observed=2026-08-07T04:55:42.722920Z digest=sha256:a0cc9a2b583e4e169ebfd8bea39b04e3c9322248c199478cbe98da64cb854d5a

Observation 7d2b17f9-d8ae-44a0-89d7-61cf01e0f55f · outbound

This paper cites Zero-shot relation extraction via reading comprehension.

Towards Efficient and Effective Alignment of Large Language Models Zero-shot relation extraction via reading comprehension

Reference 98

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source=pdf_text observed=2026-08-07T04:55:42.727112Z digest=sha256:e87f4b6fcfa91629f4a7aa0d5cb1c42b9a8ecceb21da1a439af93bc7528e9d7d

Observation b1f96cdb-4c54-4951-9213-17ec4f5c7fdc · outbound

This paper cites an unresolved cited work.

Towards Efficient and Effective Alignment of Large Language Models Unresolved cited work

Reference 99

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source=pdf_text observed=2026-08-07T04:55:42.731509Z digest=sha256:ad8e0c55e062a1f7d658b6cb01c15dfcaaf4d371144e2ad53fe417ccf94cfbfd

Observation 8ce389d0-653d-4a00-8892-d18212bab4e2 · outbound

This paper cites CMMLU: Measuring massive multitask language understanding in Chinese.

Towards Efficient and Effective Alignment of Large Language Models CMMLU: Measuring massive multitask language understanding in Chinese

Reference 100

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source=pdf_text observed=2026-08-07T04:55:42.735264Z digest=sha256:9c520561744e821fc0200c5652823d86b0a0c090bb2476dcbb902d2f2bd8e872

Observation dcb59c4d-3e81-407a-8581-e3209625dafb · outbound

This paper cites Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models.

Towards Efficient and Effective Alignment of Large Language Models Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models

Reference 101

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source=pdf_text observed=2026-08-07T04:55:42.739229Z digest=sha256:e0a8e95f5713fc9caf5377257b9e1d56c27e7d6b3798c6f3f282010b8ac911f3

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