Papers with dynamic approach

5 papers
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.

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