Paper Citation Record · LEDGER

Aligning Human and Machine Attention for Enhanced Supervised Learning

As of 18 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2502.06811.

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

pith.paper-citation-record.v1
2502.06811 v2

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T11:27:34.644439Z

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

15 of 15 outbound references displayed

verified exact1
verified fuzzy4
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d4c82709-7399-4fef-b01d-0d665c6938e4 · outbound

This paper cites Deriving Machine Attention from Human Rationales.

Aligning Human and Machine Attention for Enhanced Supervised Learning Deriving Machine Attention from Human Rationales

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-09T11:27:34.571573Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f6cbc4f8-579c-490e-a947-319ddca3d306 · outbound

This paper cites Using convolutional neural network with bert for intent determination.

Aligning Human and Machine Attention for Enhanced Supervised Learning Using convolutional neural network with bert for intent determination

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:27:34.905112Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bd1a608e-7eb5-4fa2-baef-fcb5cc464f09 · outbound

This paper cites Coca: Cost-effective collaborative annotation system by combining experts and amateurs.

Aligning Human and Machine Attention for Enhanced Supervised Learning Coca: Cost-effective collaborative annotation system by combining experts and amateurs

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:27:34.889332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b06bba87-b7cb-47b3-bfe5-5983a653ba28 · outbound

This paper cites A Comprehensive Overview of Large Language Models.

Aligning Human and Machine Attention for Enhanced Supervised Learning A Comprehensive Overview of Large Language Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-09T11:27:34.613422Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6ee934cc-62e8-44ef-afb1-425fd04fe834 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Aligning Human and Machine Attention for Enhanced Supervised Learning LLaMA: Open and Efficient Foundation Language Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-09T11:27:34.618941Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cef70724-f1ea-4f00-9536-d028d94c47f1 · outbound

This paper cites From Characters to Words to in Between: Do We Capture Morphology?.

Aligning Human and Machine Attention for Enhanced Supervised Learning From Characters to Words to in Between: Do We Capture Morphology?

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-09T11:27:34.720765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 62d2e4ff-5972-4236-92f3-6656adb91c00 · outbound

This paper cites Analyzing the Structure of Attention in a Transformer Language Model.

Aligning Human and Machine Attention for Enhanced Supervised Learning Analyzing the Structure of Attention in a Transformer Language Model

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-09T11:27:34.628810Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aecaf102-0c26-4a8a-ad9c-8489da5e36c2 · outbound

This paper cites HuggingFace's Transformers: State-of-the-art Natural Language Processing.

Aligning Human and Machine Attention for Enhanced Supervised Learning HuggingFace's Transformers: State-of-the-art Natural Language Processing

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-09T11:27:34.634076Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b1d6f17-bf58-4eba-b732-c53c96f7f339 · outbound

This paper cites Human attention during goal-directed reading comprehen- sion relies on task optimization.

Aligning Human and Machine Attention for Enhanced Supervised Learning Human attention during goal-directed reading comprehen- sion relies on task optimization

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:27:34.874024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c4eae1ed-def3-47e8-aa45-ae3a576f5493 · outbound

This paper cites A lexicon-based supervised attention model for neural senti- ment analysis.

Aligning Human and Machine Attention for Enhanced Supervised Learning A lexicon-based supervised attention model for neural senti- ment analysis

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:27:34.859269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0454a3fb-d433-4bfa-a78c-e7f6c0a5cea5 · outbound

This paper cites Effective Approaches to Attention-based Neural Machine Translation.

Aligning Human and Machine Attention for Enhanced Supervised Learning Effective Approaches to Attention-based Neural Machine Translation

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-09T11:27:34.608157Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be3a4967-da54-4e5f-8e49-8540e83ada8b · outbound

This paper cites Evolutionary Data Measures: Understanding the Difficulty of Text Classification Tasks.

Aligning Human and Machine Attention for Enhanced Supervised Learning Evolutionary Data Measures: Understanding the Difficulty of Text Classification Tasks

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-09T11:27:34.583284Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a794144-2f1d-4fd0-b670-ac3407f1cc95 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Aligning Human and Machine Attention for Enhanced Supervised Learning BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-09T11:27:34.588277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 38b617d2-f228-46ff-8b12-546d9f8a6783 · outbound

This paper cites Rationalizing Neural Predictions.

Aligning Human and Machine Attention for Enhanced Supervised Learning Rationalizing Neural Predictions

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-09T11:27:34.603027Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 697e0873-cf44-4af7-b830-f4022f355fcc · outbound

This paper cites What Does BERT Look At? An Analysis of BERT's Attention.

Aligning Human and Machine Attention for Enhanced Supervised Learning What Does BERT Look At? An Analysis of BERT's Attention

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-09T11:27:34.577796Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.