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Paper Citation Record · LEDGER

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders

As of 23 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2507.18918.

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

pith.paper-citation-record.v1
2507.18918 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:12:07.565182Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+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

33 of 33 outbound references displayed

  • verified exact1
  • verified fuzzy2
  • unresolved30
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 338e9752-2541-4f5e-9eff-8556e9c50f03 · outbound

This paper cites an unresolved cited work.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders Unresolved cited work

Reference 1

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation 30dae47e-ea84-4631-bb72-8cac32d9f511 · outbound

This paper cites an unresolved cited work.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders Unresolved cited work

Reference 2

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-08-15T18:12:07.445295Z digest=sha256:efe2fb5ce060253fe6e92ee0554c234c4ba3d305a082beb14644876cae4a291f

Observation 2f8fcf33-469f-439c-ba78-92fcc59d02c0 · outbound

This paper cites an unresolved cited work.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders Unresolved cited work

Reference 3

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation 95582e47-0852-48c3-89db-470327b74b9d · outbound

This paper cites Systematic Inequalities in Language Technology Performance across the World's Languages.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders Systematic Inequalities in Language Technology Performance across the World's Languages

Reference 4

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source=arxiv_source observed=2026-08-15T18:12:07.452951Z digest=sha256:83a7610d9388fd7eb54117aca66bbd8bc81b74a5e664ef6707e312c2d6be4d4e

Observation 84951191-8638-47f1-9c88-b9a332d33934 · outbound

This paper cites an unresolved cited work.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders Unresolved cited work

Reference 5

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source=arxiv_source observed=2026-08-15T18:12:07.456879Z digest=sha256:42d9332d858ea7b04653021c23fd5ccccb51e159c9d952caa15448a1914ab477

Observation d24892a7-df8f-44cf-a001-43ec47384f1c · outbound

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

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 6

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source=arxiv_source observed=2026-08-15T18:12:07.460681Z digest=sha256:caa208d8c11d1929f9aa367653844f281289930986768705d5d9c76b40738ee0

Observation 0cd44d18-a358-4baa-b2e9-508c23dda289 · outbound

This paper cites Unsupervised Cross-lingual Representation Learning at Scale.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders Unsupervised Cross-lingual Representation Learning at Scale

Reference 7

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source=arxiv_source observed=2026-08-15T18:12:07.464739Z digest=sha256:2447e2c90ebc0c9b29ec0197824f58b201242780339fd118b1aa04cbc2ff595d

Observation c8a9de56-83c1-4a5d-9f4f-54012f242757 · outbound

This paper cites Sparse Autoencoders Find Highly Interpretable Features in Language Models.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders Sparse Autoencoders Find Highly Interpretable Features in Language Models

Reference 8

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source=arxiv_source observed=2026-08-15T18:12:07.468581Z digest=sha256:e5ff358794b33ebb4464192610bbf905197411b80f03ab2385e101bfdcba4123

Observation f9f247ba-77bc-42a9-a6ec-8edfb29ef416 · outbound

This paper cites Gemma: Open Models Based on Gemini Research and Technology.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders Gemma: Open Models Based on Gemini Research and Technology

Reference 9

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source=arxiv_source observed=2026-08-15T18:12:07.472358Z digest=sha256:437acec24c377a3e607e2e626a94446e167816adc69fd33b34947661f59b6c02

Observation 2d5cdec2-966e-4907-bb4a-70d84ddbc6a0 · outbound

This paper cites The Secret is in the Spectra: Predicting Cross-lingual Task Performance with Spectral Similarity Measures.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders The Secret is in the Spectra: Predicting Cross-lingual Task Performance with Spectral Similarity Measures

Reference 10

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Observation 43a8ccbc-ed78-492c-9528-4713f8c44c46 · outbound

This paper cites Identifying Necessary Elements for BERT's Multilinguality.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders Identifying Necessary Elements for BERT's Multilinguality

Reference 11

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local_arxiv, observed 2026-08-15T18:12:07.763933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation aa21e598-c817-4c42-80a1-e37880f20baa · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders Measuring Massive Multitask Language Understanding

Reference 12

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Unavailable: canonical work link unavailable.

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Observation d9b9f68f-ef4b-431f-abc0-b633ce3fd7a8 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders LoRA: Low-Rank Adaptation of Large Language Models

Reference 13

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T18:12:07.488131Z digest=sha256:405843d7c2133384b4b4fe624e8e658e3f5ee50e2a7b3517bda08af1ed047eaa

Observation 9610adb6-cb65-4d53-ac34-7525a812593b · outbound

This paper cites an unresolved cited work.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders Unresolved cited work

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-08-15T18:12:07.491710Z digest=sha256:923cc2f39216ad4df91366e8aaac9cb730abb0bf139b1ddbfb817214abd59731

Observation 18b34b9e-5ad8-43c2-b84c-b983a4f96d36 · outbound

This paper cites Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback

Reference 16

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source=arxiv_source observed=2026-08-15T18:12:07.499680Z digest=sha256:cd4b18748800b40c72f07dbe981a17e0113f312ae4ea4a96ec452a1ef05c82c9

Observation ac351c88-0f6b-467e-b448-a4f9f350efd8 · outbound

This paper cites Gemma Scope: Open Sparse Autoencoders Everywhere All At Once on Gemma 2.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders Gemma Scope: Open Sparse Autoencoders Everywhere All At Once on Gemma 2

Reference 17

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Observation 65ced990-2faf-40ae-9479-e8107dfd4336 · outbound

This paper cites Unraveling Babel: Exploring Multilingual Activation Patterns of LLMs and Their Applications.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders Unraveling Babel: Exploring Multilingual Activation Patterns of LLMs and Their Applications

Reference 18

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Observation cd027f01-ae11-43ac-9eea-d2075c23a12a · outbound

This paper cites Interpreting Attention Layer Outputs with Sparse Autoencoders.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders Interpreting Attention Layer Outputs with Sparse Autoencoders

Reference 19

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source=arxiv_source observed=2026-08-15T18:12:07.510290Z digest=sha256:329a438e33664727e0564ef2751925ab6d8979c62d86d9a442d72d6e13591903

Observation 3cf4eab1-5070-485f-beae-68169c7a2eca · outbound

This paper cites GPT-4 Technical Report.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders GPT-4 Technical Report

Reference 20

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source=arxiv_source observed=2026-08-15T18:12:07.514015Z digest=sha256:9a5a869827591507267a2f8c41e727ba1c8c7bee5a540b200a2cd1673a07feb9

Observation 595945d9-5b3a-4bbe-a9e5-72bf3a91ff0b · outbound

This paper cites IRCoder: Intermediate Representations Make Language Models Robust Multilingual Code Generators.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders IRCoder: Intermediate Representations Make Language Models Robust Multilingual Code Generators

Reference 21

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Observation c39cf441-6218-4598-97e6-1175032c620a · outbound

This paper cites How multilingual is Multilingual BERT?.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders How multilingual is Multilingual BERT?

Reference 22

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Observation 693ea4bc-6062-434b-bbc4-edb4fb6fb2c9 · outbound

This paper cites Gemma 2: Improving Open Language Models at a Practical Size.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders Gemma 2: Improving Open Language Models at a Practical Size

Reference 23

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Observation a4f78c07-9115-4035-9b3e-258232668620 · outbound

This paper cites ChatGPT MT: Competitive for High- (but not Low-) Resource Languages.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders ChatGPT MT: Competitive for High- (but not Low-) Resource Languages

Reference 24

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source=arxiv_source observed=2026-08-15T18:12:07.528680Z digest=sha256:3e8b937b30076748d7d76e72e96e50d9731c52079149c48078abfa2866d7d49b

Observation 56e94ebc-a771-43bf-ab5b-7e8125e9545e · outbound

This paper cites Fine-tuning Whisper on Low-Resource Languages for Real-World Applications.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders Fine-tuning Whisper on Low-Resource Languages for Real-World Applications

Reference 25

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source=arxiv_source observed=2026-08-15T18:12:07.532281Z digest=sha256:34ae731ca93cb240b1a6c7702d91b6e6d7431c8791aa50b83615fa6f2fc0a53c

Observation f6a1ed88-a265-4b69-9b33-88e91f636db2 · outbound

This paper cites Turner, Callum McDougall, Monte MacDiarmid, Alex Tamkin, Esin Durmus, Tristan Hume, Francesco Mosconi, C.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders Turner, Callum McDougall, Monte MacDiarmid, Alex Tamkin, Esin Durmus, Tristan Hume, Francesco Mosconi, C

Reference 26

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation 43c6d25f-51a1-47db-a601-b8a65e6d4c87 · outbound

This paper cites o rg Tiedemann, Mikko Aulamo, Daria Bakshandaeva, Michele Boggia, Stig-Arne Gr \.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders o rg Tiedemann, Mikko Aulamo, Daria Bakshandaeva, Michele Boggia, Stig-Arne Gr \

Reference 27

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source=arxiv_source observed=2026-08-15T18:12:07.539077Z digest=sha256:7f582bfe115454e5fbda547fceec08ddd361bc67fc2245cdab31e6ef1060829a

Observation c079181b-6477-4c7c-a75a-ad2a598e78a8 · outbound

This paper cites an unresolved cited work.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders Unresolved cited work

Reference 28

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raw_fallback, observed 2026-08-15T18:12:07.874412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-08-15T18:12:07.544343Z digest=sha256:5ee5c5bd7d6936aa7aee265143a02e71be6d0250d081a35bd9fec09f7f167ff2

Observation fc43fd43-4f86-4daf-b10a-cd8b862e6b69 · outbound

This paper cites true features.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders true features

Reference 29

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verified fuzzy
raw_fallback, observed 2026-08-15T18:12:07.862506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-08-15T18:12:07.547717Z digest=sha256:536e24408989e099dc8a958a86ce19590a58ce9eeebd13c65cbd2c65df9e552a

Observation 18aae1e9-5d63-4c40-b02a-283974f426ae · outbound

This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 30

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source=arxiv_source observed=2026-08-15T18:12:07.550986Z digest=sha256:da50fc80a22efc1874f38c65c2ff14e9417d7cb41ce5fcaf2be9477375feca81

Observation dd8fad50-0d86-4d6e-b3ee-260b2164c0b2 · outbound

This paper cites Converging to a Lingua Franca: Evolution of Linguistic Regions and Semantics Alignment in Multilingual Large Language Models.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders Converging to a Lingua Franca: Evolution of Linguistic Regions and Semantics Alignment in Multilingual Large Language Models

Reference 31

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source=arxiv_source observed=2026-08-15T18:12:07.554627Z digest=sha256:f99af447ae51a6d42e4583d5f5e3d4eff3a857b0c3e920214c9a09a275a28daf

Observation e6ffd819-0129-4a67-b9f3-6e1a4a93c22e · outbound

This paper cites Investigating Layer Importance in Large Language Models.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders Investigating Layer Importance in Large Language Models

Reference 32

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source=arxiv_source observed=2026-08-15T18:12:07.558217Z digest=sha256:a39b1407305ca35bc7a01e7524f00592c1d7f30ba4e5ba5852c5f61f2a02c2b3

Observation b16c7baa-af1b-46f6-bae1-019d67671d3d · outbound

This paper cites online" 'onlinestring :=.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders online" 'onlinestring :=

Reference 33

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source=arxiv_source observed=2026-08-15T18:12:07.561681Z digest=sha256:4141dd0522529d0759ddb9144880d6f0361472926766f3f76a683e520b79bfa0

Observation cd4e9386-d0e7-46b2-b79f-1e9d61eea54c · outbound

This paper cites write newline.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders write newline

Reference 34

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source=arxiv_source observed=2026-08-15T18:12:07.565182Z digest=sha256:852ab6b1cd563c42fb3b48b056d958d9398da560070c55101156e4d638e134ad

Pith citing papers

No inbound Pith citation observations are available.