Papers with dynamic approach
AnnoPlot: Interactive Visualizations of Text Annotations (2024.eacl-demo)
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| Challenge: | Annotation projects face challenges in data quality and validity, authors argue . |
| Approach: | They propose an open-source web application that analyzes, manages, and visualizes annotated text data. |
| Outcome: | The proposed application is open-source and promotes transparency and user control . it offers comprehensive views of span annotations and category systems without training or classification model . |
Dynamic Human Evaluation for Relative Model Comparisons (2022.lrec-1)
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| Challenge: | Automated metrics have reported flaws when applied to measure quality aspects of generated text and have been shown to correlate poorly with human judgements. |
| Approach: | They propose an agent-based framework to measure the required number of human annotations when evaluating generated outputs in relative comparison settings. |
| Outcome: | The proposed model can be compared with a crowdsourced case study and a simulation with simulated human judgements. |
Neeko: Leveraging Dynamic LoRA for Efficient Multi-Character Role-Playing Agent (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have revolutionized open-domain dialogue agents but face challenges in multi-character role-playing (MCRP) scenarios. |
| Approach: | They propose a framework for efficient multi-character role-playing that employs a dynamic low-rank adapter strategy and distinct LoRA blocks for each character. |
| Outcome: | Neeko employs a dynamic low-rank adapter (LoRA) strategy, enabling it to adapt seamlessly to diverse characters. |
LlmLink: Dual LLMs for Dynamic Entity Linking on Long Narratives with Collaborative Memorisation and Prompt Optimisation (2025.coling-main)
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| Challenge: | Existing methods focus on supervised fine-tuning or limited to one-off prediction, which poses a challenge where the context is long. |
| Approach: | They propose a dynamic approach to CoREFerence resolution in chunked long narratives by deploying dual Large Language Models. |
| Outcome: | The proposed model achieves performance gains over existing models and fine-tuning approaches on long narrative datasets, significantly reducing the resources required for inference and training. |
Inflated Excellence or True Performance? Rethinking Medical Diagnostic Benchmarks with Dynamic Evaluation (2026.acl-long)
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| Challenge: | Current evaluations of large language models (LLMs) are limited in capturing key challenges of clinical diagnostic scenarios. |
| Approach: | They propose a dynamic benchmark for medical diagnostics that provides a stress test of diagnostic robustness. |
| Outcome: | The proposed model provides a stress test of diagnostic robustness and veracity, helpfulness and consistency. |