Annotator-Centric Active Learning for Subjective NLP Tasks

• URL: http://arxiv.org/abs/2404.15720v4
• Date: Wed, 23 Oct 2024 16:12:39 GMT
• Title: Annotator-Centric Active Learning for Subjective NLP Tasks
• Authors: Michiel van der Meer, Neele Falk, Pradeep K. Murukannaiah, Enrico Liscio,
• Abstract summary: Active Learning (AL) addresses the high costs of collecting human annotations by strategically annotating the most informative samples. We introduce Annotator-Centric Active Learning (ACAL), which incorporates an annotator selection strategy following data sampling. Our objective is to efficiently approximate the full diversity of human judgments, and to assess model performance using annotator-centric metrics.
• Score: 7.766754308448708
• License:
• Abstract: Active Learning (AL) addresses the high costs of collecting human annotations by strategically annotating the most informative samples. However, for subjective NLP tasks, incorporating a wide range of perspectives in the annotation process is crucial to capture the variability in human judgments. We introduce Annotator-Centric Active Learning (ACAL), which incorporates an annotator selection strategy following data sampling. Our objective is two-fold: 1) to efficiently approximate the full diversity of human judgments, and 2) to assess model performance using annotator-centric metrics, which value minority and majority perspectives equally. We experiment with multiple annotator selection strategies across seven subjective NLP tasks, employing both traditional and novel, human-centered evaluation metrics. Our findings indicate that ACAL improves data efficiency and excels in annotator-centric performance evaluations. However, its success depends on the availability of a sufficiently large and diverse pool of annotators to sample from.

Related papers

• CompassJudger-1: All-in-one Judge Model Helps Model Evaluation and Evolution [74.41064280094064]
  textbfJudger-1 is the first open-source textbfall-in-one judge LLM. CompassJudger-1 is a general-purpose LLM that demonstrates remarkable versatility. textbfJudgerBench is a new benchmark that encompasses various subjective evaluation tasks.
  arXiv  Detail & Related papers  (2024-10-21T17:56:51Z)
• Active Evaluation Acquisition for Efficient LLM Benchmarking [18.85604491151409]
  We investigate strategies to improve evaluation efficiency by selecting a subset of examples from each benchmark using a learned policy. Our approach models the dependencies across test examples, allowing accurate prediction of the evaluation outcomes for the remaining examples. Empirical results demonstrate that our approach significantly reduces the number of evaluation prompts required.
  arXiv  Detail & Related papers  (2024-10-08T12:08:46Z)
• BAL: Balancing Diversity and Novelty for Active Learning [53.289700543331925]
  We introduce a novel framework, Balancing Active Learning (BAL), which constructs adaptive sub-pools to balance diverse and uncertain data. Our approach outperforms all established active learning methods on widely recognized benchmarks by 1.20%.
  arXiv  Detail & Related papers  (2023-12-26T08:14:46Z)
• One-Shot Learning as Instruction Data Prospector for Large Language Models [108.81681547472138]
  textscNuggets uses one-shot learning to select high-quality instruction data from extensive datasets. We show that instruction tuning with the top 1% of examples curated by textscNuggets substantially outperforms conventional methods employing the entire dataset.
  arXiv  Detail & Related papers  (2023-12-16T03:33:12Z)
• Active Transfer Learning for Efficient Video-Specific Human Pose Estimation [16.415080031134366]
  Human Pose (HP) estimation is actively researched because of its wide range of applications. We present our approach combining Active Learning (AL) and Transfer Learning (TL) to adapt HP estimators to individual video domains efficiently.
  arXiv  Detail & Related papers  (2023-11-08T21:56:29Z)
• ACTOR: Active Learning with Annotator-specific Classification Heads to Embrace Human Label Variation [35.10805667891489]
  Active learning, as an annotation cost-saving strategy, has not been fully explored in the context of learning from disagreement. We show that in the active learning setting, a multi-head model performs significantly better than a single-head model in terms of uncertainty estimation.
  arXiv  Detail & Related papers  (2023-10-23T14:26:43Z)
• Which Prompts Make The Difference? Data Prioritization For Efficient Human LLM Evaluation [9.452326973655445]
  We find that metric-based methods enhance the efficiency of human evaluations by minimizing the number of required annotations. We show that our method is effective across widely used model families, reducing instances of indecisive (or "tie") outcomes by up to 54%. This potential reduction in required human effort positions our approach as a valuable strategy in future large language model evaluations.
  arXiv  Detail & Related papers  (2023-10-22T21:48:51Z)
• ALE: A Simulation-Based Active Learning Evaluation Framework for the Parameter-Driven Comparison of Query Strategies for NLP [3.024761040393842]
  Active Learning (AL) proposes promising data points to annotators they annotate next instead of a subsequent or random sample. This method is supposed to save annotation effort while maintaining model performance. We introduce a reproducible active learning evaluation framework for the comparative evaluation of AL strategies in NLP.
  arXiv  Detail & Related papers  (2023-08-01T10:42:11Z)
• ASPEST: Bridging the Gap Between Active Learning and Selective Prediction [56.001808843574395]
  Selective prediction aims to learn a reliable model that abstains from making predictions when uncertain. Active learning aims to lower the overall labeling effort, and hence human dependence, by querying the most informative examples. In this work, we introduce a new learning paradigm, active selective prediction, which aims to query more informative samples from the shifted target domain.
  arXiv  Detail & Related papers  (2023-04-07T23:51:07Z)
• Active Learning for Abstractive Text Summarization [50.79416783266641]
  We propose the first effective query strategy for Active Learning in abstractive text summarization. We show that using our strategy in AL annotation helps to improve the model performance in terms of ROUGE and consistency scores.
  arXiv  Detail & Related papers  (2023-01-09T10:33:14Z)
• Active Pointly-Supervised Instance Segmentation [106.38955769817747]
  We present an economic active learning setting, named active pointly-supervised instance segmentation (APIS) APIS starts with box-level annotations and iteratively samples a point within the box and asks if it falls on the object. The model developed with these strategies yields consistent performance gain on the challenging MS-COCO dataset.
  arXiv  Detail & Related papers  (2022-07-23T11:25:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.