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Daniel Hershcovich

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EAAI Journal 2026 Journal Article

ScholarLens: Tracking the growing penetration of Large Language Models in scholarly writing and peer review

Li Zhou
Ruijie Zhang
Xunlian Dai
Daniel Hershcovich
Haizhou Li

Although the widespread use of Large Language Models (LLMs) brings convenience, it also raises concerns about the credibility of academic research and scholarly processes. To better understand the extent and characteristics of LLM use in scholarly writing and peer review, the penetration of LLMs across academic workflows is evaluated from multiple perspectives and dimensions, providing compelling evidence of their growing influence. A framework consisting of two components is proposed: ScholarLens, a curated dataset of human-written and LLM-generated content across scholarly writing and peer review for multi-perspective evaluation, and LLMetrica, a tool for assessing LLM penetration using rule-based metrics and model-based detectors for multi-dimensional evaluation. The effectiveness of LLMetrica is demonstrated through experiments, revealing the increasing role of LLMs in scholarly processes. These findings emphasize the need for transparency, accountability, and ethical practices in the use of LLMs to maintain academic credibility.

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AAAI Conference 2021 Conference Paper

Joint Semantic Analysis with Document-Level Cross-Task Coherence Rewards

Rahul Aralikatte
Mostafa Abdou
Heather C Lent
Daniel Hershcovich
Anders Søgaard

Coreference resolution and semantic role labeling are NLP tasks that capture different aspects of semantics, indicating respectively, which expressions refer to the same entity, and what semantic roles expressions serve in the sentence. However, they are often closely interdependent, and both generally necessitate natural language understanding. Do they form a coherent abstract representation of documents? We present a neural network architecture for joint coreference resolution and semantic role labeling for English, and train graph neural networks to model the coherence of the combined shallow semantic graph. Using the resulting coherence score as a reward for our joint semantic analyzer, we use reinforcement learning to encourage global coherence over the document and between semantic annotations. This leads to improvements on both tasks in multiple datasets from different domains, and across a range of encoders of different expressivity, calling, we believe, for a more holistic approach to semantics in NLP.

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