Papers with domain-specific training
“Hold on honey, men at work”: A semi-supervised approach to detecting sexism in sitcoms (2021.acl-srw)
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| Challenge: | sexist dialogue in sitcoms is an important part of society's development, according to Sink and Mastro (2017). |
| Approach: | They propose a semi-supervised text classification model that automatically detects instances of sexism in popular sitcom dialogues. |
| Outcome: | The proposed model outperforms deep learning-based systems in detecting sexist dialogues over time and shows that sexism decreases over the years. |
Greenback Bears and Fiscal Hawks: Finance is a Jungle and Text Embeddings Must Adapt (2024.emnlp-industry)
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| Challenge: | Financial documents are filled with specialized terminology, arcane jargon, and curious acronyms that pose challenges for general-purpose text embeddings. |
| Approach: | They propose to fine tune financial text embeddings finetuned on a carefully constructed dataset of 14.3M query-passage pairs including both public and proprietary financial documents. |
| Outcome: | The proposed embeddings achieve Recall@1 of 62.8% on a held-out test set, vs. only 39.2% for the best general-purpose text embeddING from OpenAI. |
PatentVision: A multimodal method for drafting patent applications (2026.eacl-industry)
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| Challenge: | PatentVision integrates textual and visual inputs to generate patent specifications . existing systems fail to capture the nuanced interplay between textual, visual components . |
| Approach: | They propose a multimodal framework that integrates textual and visual inputs to generate patent specifications. |
| Outcome: | The proposed framework surpasses text-only methods in patent writing, the authors show . it integrates visual data to better represent intricate design features and functional connections . |
ChartGemma: Visual Instruction-tuning for Chart Reasoning in the Wild (2025.coling-industry)
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| Challenge: | Existing methods for chart understanding and reasoning are weakly aligned and rely on underlying data tables. |
| Approach: | They propose a chart-based understanding and reasoning model that is trained on instruction-tuning data generated directly from chart images. |
| Outcome: | The proposed model achieves state-of-the-art results across 5 benchmarks spanning chart summarization, question answering, and fact-checking. |
Revisiting LLMs as Zero-Shot Time Series Forecasters: Small Noise Can Break Large Models (2025.acl-short)
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| Challenge: | Large Language Models (LLMs) have shown remarkable performance across diverse tasks without domain-specific training, fueling interest in their potential for time series forecasting. |
| Approach: | They evaluate the effectiveness of LLMs as zero-shot forecasters compared to state-of-the-art domain-specific models by encoding sequences directly within prompts. |
| Outcome: | The proposed models perform well across multiple domains while reducing the need for domain-specific training. |
ENGinius: A Bilingual LLM Optimized for Plant Construction Engineering (2025.acl-industry)
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| Challenge: | Recent advances in large language models have drawn attention for their potential to automate and optimize processes across diverse sectors. |
| Approach: | They propose a specialized LLM for plant construction engineering that delivers optimized responses to plant engineers by leveraging enriched domain knowledge. |
| Outcome: | The proposed model delivers optimized responses to plant engineers by leveraging enriched domain knowledge. |
Argumentation and Domain Discourse in Scholarly Articles on the Theory of International Relations (2025.coling-main)
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Magdalena Wolska, Sassan Gholiagha, Mitja Sienknecht, Dora Kiesel, Irene Lopez Garcia, Patrick Riehmann, Matti Wiegmann, Bernd Froehlich, Katrin Girgensohn, Jürgen Neyer, Benno Stein
| Challenge: | SKILL project aims to provide students with AI tools to facilitate analysis of argumentation in scholarly articles on international relations. |
| Approach: | They propose to use AI to analyze argumentation in scholarly articles on international relations . they use a dataset, discourse analysis, and baseline experiments to examine argumentation and domain content types . |
| Outcome: | The proposed method enables educationally-relevant insight into scholarly IR discourse . it requires domain-specific training and fine-tuning on relation and content type prediction tasks. |
AXCEL: Automated eXplainable Consistency Evaluation using LLMs (2024.findings-emnlp)
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P Aditya Sreekar, Sahil Verma, Suransh Chopra, Abhishek Persad, Sarik Ghazarian, Narayanan Sadagopan
| Challenge: | Large Language Models (LLMs) are widely used for various tasks but evaluating the consistency of generated text remains a challenge. |
| Approach: | They propose a prompt-based consistency metric which provides explanations for consistency scores by providing detailed reasoning and pinpointing inconsistent text spans. |
| Outcome: | The proposed metric outperforms state-of-the-art metrics in summarization, free text generation and data-to-text conversion tasks by 8.7% and 6.2%. |
Unilaw-R1: A Large Language Model for Legal Reasoning with Reinforcement Learning and Iterative Inference (2025.emnlp-main)
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| Challenge: | Reasoning-focused large language models (LLMs) are rapidly evolving across various domains, yet their capabilities in handling complex legal problems remain underexplored. |
| Approach: | They propose a large language model tailored for legal reasoning with a 7-billion parameter scale and a two-stage training strategy combining Supervised Fine-Tuning and Reinforcement Learning. |
| Outcome: | The proposed model outperforms all models of similar scale on authoritative benchmarks and outperformed Qwen-2.5-7B-Instruct (46.6%) by an average margin of 6.6%. |