The Noisy Path from Source to Citation: Measuring How Scholars Engage with Past Research (2025.acl-long)
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| Challenge: | Academic citations are widely used for evaluating research and tracing knowledge flows. |
| Approach: | They propose a computational pipeline to quantify citation fidelity at the sentence level by identifying citations in citing papers and corresponding claims in cited papers. |
| Outcome: | The proposed pipeline identifies citations in citing papers and the corresponding claims in cited papers and applies supervised models to measure fidelity at the sentence level. |
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Citation Amnesia: On The Recency Bias of NLP and Other Academic Fields (2025.coling-main)
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| Challenge: | citation age is a key factor in determining whether older works are cited in scientific journals or not. |
| Approach: | They examine the tendency of NLP to cite older work across 20 fields of study over 43 years (1980–2023) . they put NLP’s propensity to citation older work in the context of these 20 other fields to see whether differences can be observed . |
| Outcome: | The trend is strongest in NLP and ML research (-12.8% and -5.5% in citation age from previous peaks) |
Forgotten Knowledge: Examining the Citational Amnesia in NLP (2023.acl-long)
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| Challenge: | a recent study examines how far back in time we tend to cite papers . citation patterns are correlated with age, age, and other factors . |
| Approach: | They analyze citation patterns across time and examine temporal changes . they find that 62% of cited papers are from the immediate five years prior to publication . |
| Outcome: | The authors show that citing papers is the primary method of scientific writing . they show that the trend has reversed and current papers have low temporal diversity . |
On Forgetting to Cite Older Papers: An Analysis of the ACL Anthology (2020.acl-main)
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| Challenge: | a growing number of published papers are citing older work, but the rate of citations is stable . a recent paper cited work from recent years, whereas papers published 15 or more years ago are cited at a stable rate. |
| Approach: | They analyze citations in papers published at selected ACL venues between 2010 and 2019 . they find that recent papers are cited significantly more often in recent years . |
| Outcome: | The authors analyze citations in journals and conferences between 2010 and 2019 . they find that recent papers cite more recent work, but papers published 15 or more years ago are cited at a stable rate. |
In-depth Research Impact Summarization through Fine-Grained Temporal Citation Analysis (2026.acl-long)
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| Challenge: | citation counts are a shallow view that fails to capture how a paper has influenced subsequent work. |
| Approach: | They propose a task to generate nuanced, expressive, and time-aware impact summaries . they analyze fine-grained confirmatory and correction citation intents to generate summary . |
| Outcome: | The proposed task shows moderate to strong human correlation on subjective metrics such as insightfulness. |
We are Who We Cite: Bridges of Influence Between Natural Language Processing and Other Academic Fields (2023.emnlp-main)
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| Challenge: | In this paper, we quantify the degree of influence between 23 fields of study and NLP (on each other) |
| Approach: | They quantify the degree of influence between 23 fields of study and NLP on each other . they find that cross-field engagement of NLP has declined from 0.58 in 1980 to 0.31 in 2022 . |
| Outcome: | The proposed Citation Field Diversity Index (CFDI) has declined from 0.58 in 1980 to 0.31 in 2022, the authors show . |
L-CiteEval: A Suite for Evaluating Fidelity of Long-context Models (2025.acl-long)
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| Challenge: | Long-context models (LCMs) have seen remarkable advancements in recent years, facilitating tasks like long-document QA. |
| Approach: | They propose an out-of-the-box suite that can assess both generation quality and fidelity in long-context understanding tasks. |
| Outcome: | The proposed suite can assess both generation quality and fidelity in long-context understanding tasks. |
Examining Citations of Natural Language Processing Literature (2020.acl-main)
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| Challenge: | citations of NLP papers have decreased in recent years, but long papers get three times as many citation as short papers . citation data from the ACL Anthology and Google Scholar can be used to understand the field and quantify the impact of different types of papers. |
| Approach: | They extract data from the ACL Anthology and Google Scholar to examine trends in citations of NLP papers. |
| Outcome: | The results show that only about 56% of the papers in AA are cited ten or more times . CL Journal has the most cited papers, but its citation dominance has lessened . |
CiteBench: A Benchmark for Scientific Citation Text Generation (2023.emnlp-main)
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| Challenge: | Existing studies on citation text generation are based upon widely diverging task definitions, making it hard to study this task systematically. |
| Approach: | They propose a benchmark for citation text generation that unifies multiple datasets and enables standardized evaluation of citation texts across task designs and domains. |
| Outcome: | The proposed benchmark examines the performance of multiple strong baselines and enables standardized evaluation of citation text generation models across task designs and domains. |
CiteGuard: Faithful Citation Attribution for LLMs via Retrieval-Augmented Validation (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) have emerged as powerful assistants for scientific writing, but reliability of LLM alone is in doubt. |
| Approach: | They propose a retrieval-aware agent framework to provide more faithful grounding for citation validation. |
| Outcome: | The proposed framework improves over the baseline and achieves 68.1% accuracy on the CiteME benchmark, approaching human performance. |
CiteEval: Principle-Driven Citation Evaluation for Source Attribution (2025.acl-long)
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Yumo Xu, Peng Qi, Jifan Chen, Kunlun Liu, Rujun Han, Lan Liu, Bonan Min, Vittorio Castelli, Arshit Gupta, Zhiguo Wang
| Challenge: | Current evaluation frameworks rely on NLI to assess binary or ternary support from cited sources, which is suboptimal for citation evaluation. |
| Approach: | They propose a citation evaluation framework based on fine-grained citation ratings within a broad context and construct a multi-domain benchmark with high-quality human annotations. |
| Outcome: | The proposed framework provides a high-quality human annotation benchmark and a suite of model-based metrics that exhibit strong correlation with human judgments. |