Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-07-02T13:03:23.898325Z
Paper Citation Record · LEDGER
Quantifying the Affective Gap: A Zero-Shot Evaluation of LLMs on Fine-Grained Emotion Taxonomies
As of 16 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2607.00968.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
27 of 27 outbound references displayed
- verified exact1
- verified fuzzy24
- unresolved1
- parse uncertain0
- malformed identifier0
- metadata mismatch1
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 7908d08e-99c0-4d0e-b680-5ad339bf4450 · outbound
This paper cites Methods in predictive techniques for mental health status on social media: A critical review,.
Quantifying the Affective Gap: A Zero-Shot Evaluation of LLMs on Fine-Grained Emotion Taxonomies Methods in predictive techniques for mental health status on social media: A critical review,
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T01:31:44.268519Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 0b6f3402-7707-4721-b321-35c1dac677f4 · outbound
This paper cites Towards em- pathetic open-domain conversation models: A new benchmark and dataset,.
Quantifying the Affective Gap: A Zero-Shot Evaluation of LLMs on Fine-Grained Emotion Taxonomies Towards em- pathetic open-domain conversation models: A new benchmark and dataset,
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T01:31:44.237933Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation af111619-aab4-46d8-ace6-4507293d9a9e · outbound
This paper cites An argument for basic emotions,.
Quantifying the Affective Gap: A Zero-Shot Evaluation of LLMs on Fine-Grained Emotion Taxonomies An argument for basic emotions,
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T01:31:44.259381Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 41c5e307-7b7c-4508-867c-1755ab168a17 · outbound
This paper cites EmoBench: Evaluating the emotional in- telligence of large language models,.
Quantifying the Affective Gap: A Zero-Shot Evaluation of LLMs on Fine-Grained Emotion Taxonomies EmoBench: Evaluating the emotional in- telligence of large language models,
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T01:31:44.237685Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 6cf995c7-c907-4afd-a6e6-61b2795ce4ac · outbound
This paper cites SentimentGPT: Exploiting GPT for Advanced Sentiment Analysis and its Departure from Current Machine Learning.
Quantifying the Affective Gap: A Zero-Shot Evaluation of LLMs on Fine-Grained Emotion Taxonomies SentimentGPT: Exploiting GPT for Advanced Sentiment Analysis and its Departure from Current Machine Learning
Reference 5
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-02T13:06:58.476421Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation f30f3146-dd57-42b9-877f-7fcd3bf65526 · outbound
This paper cites Crowdsourcing a word–emotion association lexicon,.
Quantifying the Affective Gap: A Zero-Shot Evaluation of LLMs on Fine-Grained Emotion Taxonomies Crowdsourcing a word–emotion association lexicon,
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T01:31:44.255453Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 16300256-cc39-495e-bfe8-de6b0b3343b7 · outbound
This paper cites SemEval-2007 task 14: Affective text,.
Quantifying the Affective Gap: A Zero-Shot Evaluation of LLMs on Fine-Grained Emotion Taxonomies SemEval-2007 task 14: Affective text,
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T01:31:44.264811Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 003bbba8-bb3c-476c-a9fe-42917c3b4d20 · outbound
This paper cites SemEval-2018 task 1: Affect in tweets,.
Quantifying the Affective Gap: A Zero-Shot Evaluation of LLMs on Fine-Grained Emotion Taxonomies SemEval-2018 task 1: Affect in tweets,
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T01:31:44.264133Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 3a179f60-f671-4c4f-a018-0b50199b6ee7 · outbound
This paper cites SemEval- 2019 task 3: EmoContext contextual emotion detection in text,.
Quantifying the Affective Gap: A Zero-Shot Evaluation of LLMs on Fine-Grained Emotion Taxonomies SemEval- 2019 task 3: EmoContext contextual emotion detection in text,
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T01:31:44.260382Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation cd8882d5-3e41-4287-8431-198f8a42fd70 · outbound
This paper cites GoEmotions: A dataset of fine-grained emotions,.
Quantifying the Affective Gap: A Zero-Shot Evaluation of LLMs on Fine-Grained Emotion Taxonomies GoEmotions: A dataset of fine-grained emotions,
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T01:31:44.273940Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 6da72f37-fa25-4813-b876-440b07a22d26 · outbound
This paper cites BERT: Pre- training of deep bidirectional transformers for language understanding,.
Quantifying the Affective Gap: A Zero-Shot Evaluation of LLMs on Fine-Grained Emotion Taxonomies BERT: Pre- training of deep bidirectional transformers for language understanding,
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T01:31:44.271837Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation ae94b268-9c49-43dc-b64b-e681b8e200ed · outbound
This paper cites Using bert to understand tiktok users’ adhd discussion,.
Quantifying the Affective Gap: A Zero-Shot Evaluation of LLMs on Fine-Grained Emotion Taxonomies Using bert to understand tiktok users’ adhd discussion,
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T01:31:44.266731Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation fc28a810-9ddd-4ffc-9869-6e4210ef4048 · outbound
This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.
Quantifying the Affective Gap: A Zero-Shot Evaluation of LLMs on Fine-Grained Emotion Taxonomies RoBERTa: A Robustly Optimized BERT Pretraining Approach
Reference 13
- Resolution
- verified exact
- local_arxiv, observed 2026-07-02T13:06:58.473459Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation efde0a7f-ded1-4974-b691-d8472e459ce0 · outbound
This paper cites an unresolved cited work.
Quantifying the Affective Gap: A Zero-Shot Evaluation of LLMs on Fine-Grained Emotion Taxonomies Unresolved cited work
Reference 14
- Resolution
- unresolved
- raw_fallback, observed 2026-07-06T01:31:44.251301Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 29d3fadf-73b8-491b-9c3f-97c1414f72b3 · outbound
This paper cites Automatic sarcasm detection: A survey,.
Quantifying the Affective Gap: A Zero-Shot Evaluation of LLMs on Fine-Grained Emotion Taxonomies Automatic sarcasm detection: A survey,
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T01:31:44.270051Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 63018425-8400-4bb9-aaae-34b3173692db · outbound
This paper cites Cognitive prosthetic: An AI-enabled multimodal system for episodic recall in knowledge work,.
Quantifying the Affective Gap: A Zero-Shot Evaluation of LLMs on Fine-Grained Emotion Taxonomies Cognitive prosthetic: An AI-enabled multimodal system for episodic recall in knowledge work,
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T01:31:44.263118Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation abb2328b-5646-4ba5-af35-f0c11461366e · outbound
This paper cites Analyzing uncon- strained reading patterns of digital documents using eye tracking,.
Quantifying the Affective Gap: A Zero-Shot Evaluation of LLMs on Fine-Grained Emotion Taxonomies Analyzing uncon- strained reading patterns of digital documents using eye tracking,
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T01:31:44.242519Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation b6a9976b-0ed0-47e3-b9c3-524de156e37a · outbound
This paper cites Multidisciplinary reading patterns of digital documents,.
Quantifying the Affective Gap: A Zero-Shot Evaluation of LLMs on Fine-Grained Emotion Taxonomies Multidisciplinary reading patterns of digital documents,
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T01:31:44.236189Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 827e26b1-ee12-443e-938c-f216ae5dd120 · outbound
This paper cites How devices shape mental effort in digital document reading: An eye-tracking study,.
Quantifying the Affective Gap: A Zero-Shot Evaluation of LLMs on Fine-Grained Emotion Taxonomies How devices shape mental effort in digital document reading: An eye-tracking study,
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T01:31:44.249225Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 0b933b22-cfea-4660-973c-128a786c68f5 · outbound
This paper cites Language models are few-shot learners,.
Quantifying the Affective Gap: A Zero-Shot Evaluation of LLMs on Fine-Grained Emotion Taxonomies Language models are few-shot learners,
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T01:31:44.231816Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation b39e1957-5500-4298-913c-4b7431dbf36d · outbound
This paper cites Training language models to follow instructions with human feedback,.
Quantifying the Affective Gap: A Zero-Shot Evaluation of LLMs on Fine-Grained Emotion Taxonomies Training language models to follow instructions with human feedback,
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T01:31:44.255910Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 7f939095-6eaa-45be-8987-3d1195218120 · outbound
This paper cites Finetuned language models are zero-shot learners.
Quantifying the Affective Gap: A Zero-Shot Evaluation of LLMs on Fine-Grained Emotion Taxonomies Finetuned language models are zero-shot learners
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T01:31:44.223456Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 9fbd8e6d-9a2f-40d3-a12e-fc2672537044 · outbound
This paper cites emotions-dataset,.
Quantifying the Affective Gap: A Zero-Shot Evaluation of LLMs on Fine-Grained Emotion Taxonomies emotions-dataset,
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T01:31:44.272006Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 9a8af35c-1422-419f-ac86-0b24587aeaf8 · outbound
This paper cites Chain-of-thought prompting elicits reasoning in large language models,.
Quantifying the Affective Gap: A Zero-Shot Evaluation of LLMs on Fine-Grained Emotion Taxonomies Chain-of-thought prompting elicits reasoning in large language models,
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T01:31:44.273750Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 903644fd-c1c1-4a50-8fa7-a327cfbb9ad0 · outbound
This paper cites A coefficient of agreement for nominal scales.
Quantifying the Affective Gap: A Zero-Shot Evaluation of LLMs on Fine-Grained Emotion Taxonomies A coefficient of agreement for nominal scales
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T01:31:44.270274Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 664f81bf-7e02-490c-8b26-108341ee8c4c · outbound
This paper cites Note on the sampling error of the difference between correlated proportions or percentages.
Quantifying the Affective Gap: A Zero-Shot Evaluation of LLMs on Fine-Grained Emotion Taxonomies Note on the sampling error of the difference between correlated proportions or percentages
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T01:31:44.249111Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 107d5b5d-6d9a-43ef-b10d-d58612ef5fe8 · outbound
This paper cites Zero-data learning of new tasks,.
Quantifying the Affective Gap: A Zero-Shot Evaluation of LLMs on Fine-Grained Emotion Taxonomies Zero-data learning of new tasks,
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T01:31:44.267826Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Pith citing papers
No inbound Pith citation observations are available.