Papers with cross-validation
ViClaim: A Multilingual Multilabel Dataset for Automatic Claim Detection in Videos (2025.emnlp-main)
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| Challenge: | Existing efforts in misinformation detection focus on written text, leaving a significant gap in addressing the complexity of spoken text in video transcripts. |
| Approach: | They propose to annotate video transcripts in three languages and six topics using a custom annotation tool. |
| Outcome: | The proposed tool shows strong cross-validation performance but challenges for generalization to unseen topics. |
DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) with web search capabilities show significant potential for deep research. |
| Approach: | They introduce a framework for end-to-end training of LLM-based deep research agents . they implement a specialized multi-agent architecture where browsing agents extract relevant information from various webpage structures. |
| Outcome: | The proposed framework improves on open-domain research tasks by 28.9 points over prompt engineering and 7.2 points over RAG-based RL agents. |
Enhancing Job Evaluation with Data Augmentation and Text Classification (2026.acl-industry)
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| Challenge: | Recruiters rely on job titles, role descriptions, and responsibility levels to determine job grades and salary structures. |
| Approach: | They propose to semi-automate job evaluation by fine-tuning a RoBERTa model for classification and using Gemini to generate synthetic job descriptions for rare job titles. |
| Outcome: | The proposed method improves job evaluation by boosting consistency and speeding up workflows. |
Linguistically Motivated Features for Classifying Shorter Text into Fiction and Non-Fiction Genre (2022.coling-1)
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| Challenge: | linguistically motivated features are used to classify paragraph-level text into fiction and non-fiction genres. |
| Approach: | They deploy linguistically motivated features to classify paragraph-level text into fiction and non-fiction genres using a logistic regression model. |
| Outcome: | The proposed model gives 15.56% accuracy jump over baseline model . the proposed model also transfers over to another dataset, Baby BNC corpus . |
Automated Few-Shot Classification with Instruction-Finetuned Language Models (2023.findings-emnlp)
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| Challenge: | Existing few-shot learning approaches combine language models with prompts, but they often require domain knowledge and substantial guesswork. |
| Approach: | They propose a method to eliminate the need for handcrafted prompts by generating two distinct, semantically meaningful class descriptions and a selection mechanism via cross-validation. |
| Outcome: | The proposed method outperforms state-of-the-art few-shot learning methods over 12 datasets, spanning 8 classification tasks. |
Bayes Test of Precision, Recall, and F1 Measure for Comparison of Two Natural Language Processing Models (P19-1)
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| Challenge: | Existing t-tests for cross-validation (CV) are inappropriate for model comparison . existing t tests for cross validation (CV), such as 52 CV t test and F ttest, are inadequate . |
| Approach: | They propose to use a block-regularized 32 CV to compare two NLP models . they calibrate the posterior distributions of P, R, and F1 and derive an accurate interval estimation of P and R . |
| Outcome: | The proposed model could regularize the difference in certain frequency distributions over linguistic units and yield stable estimators of P, R, and F1. |
Analyzing and Modeling LLM Response Lengths with Extreme Value Theory: Anchoring Effects and Hybrid Distributions (2025.emnlp-main)
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| Challenge: | Existing approaches treat length as an incidental output property rather than a statistically regular phenomenon worthy of rigorous modeling. |
| Approach: | They propose a statistical framework for modeling and controlling large language model response lengths using extreme value theory and cross-validation on Qwen and DeepSeek architectures. |
| Outcome: | The proposed model improves tail fit and generalizability while maintaining generalizzability. |