Multi-Source Test-Time Adaptation as Dueling Bandits for Extractive Question Answering (2023.acl-long)
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| Challenge: | Recent research on test-time adaptation suggests a possible way to improve the generalization ability of LLMs. |
| Approach: | They propose to use multi-armed bandit learning and multi-arm dueling bandits to solve a multi-source test-time model adaptation problem from user feedback. |
| Outcome: | The proposed model is more effective than other strong baselines on extractive question answering datasets. |
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| Challenge: | Explicit feedback from users can be used to continually improve system performance. |
| Approach: | They study the potential of learning from user feedback for extractive question answering by simulating feedback using supervised data. |
| Outcome: | The proposed model improves on a few examples and can be deployed in new domains without any data annotation effort. |
Red-Bandit: Test-Time Adaptation for LLM Red-Teaming via Bandit-Guided LoRA Experts (2026.acl-long)
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| Challenge: | Existing approaches to audit Large Language Models (LLMs) lack mechanisms to efficiently adapt to model-specific vulnerabilities at inference. |
| Approach: | They propose a red-teaming framework that adapts online to identify and exploit model failure modes under distinct attack styles. |
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SEMQA: Semi-Extractive Multi-Source Question Answering (2024.naacl-long)
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| Challenge: | Recent proposed long-form question answering systems have shown promising capabilities, but attributing and verifying their generated abstractive answers can be difficult. |
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Continually Improving Extractive QA via Human Feedback (2023.emnlp-main)
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| Challenge: | a study of extractive question answering systems using human feedback shows promising potential for continual learning. |
| Approach: | They study extractive question answering system by using user feedback to improve it . they design and deploy an iterative approach where users ask questions and provide feedback . |
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Test-Time Self-Adaptive Small Language Models for Question Answering (2023.findings-emnlp)
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| Challenge: | Recent instruction-finetuned large language models (LMs) have shown notable performances in various tasks, such as question-answering. |
| Approach: | They propose to use unlabeled test data to transfer smaller language models with limited knowledge. |
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Bandits Don’t Follow Rules: Balancing Multi-Facet Machine Translation with Multi-Armed Bandits (2021.findings-emnlp)
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| Challenge: | Training data for machine translation (MT) is often sourced from multiple large corpora that are multi-faceted in nature. |
| Approach: | They propose to optimize the balance between translationese and natural training data to relieve system developers from manual schedule design. |
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Open-Ended Visual Question Answering by Multi-Modal Domain Adaptation (2020.findings-emnlp)
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| Challenge: | Existing approaches to visual question answering (VQA) are not suitable for real-world applications. |
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Understanding and Improving Zero-shot Multi-hop Reasoning in Generative Question Answering (2022.coling-1)
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| Challenge: | Generative question answering (QA) models generate answers to complex questions, but their mechanism for doing so is still poorly understood. |
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Generalizing Question Answering System with Pre-trained Language Model Fine-tuning (D19-58)
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| Challenge: | Existing methods focus on improving in-domain performance, leaving open the question of how they can generalize to out-of-domain and unseen RC tasks. |
| Approach: | They propose a multi-task learning framework that learns the shared representation across different tasks and builds on a large pre-trained language model and fine-tuned on multiple RC datasets. |
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Source-Free Unsupervised Domain Adaptation for Question Answering via Prompt-Assisted Self-learning (2024.findings-naacl)
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| Challenge: | Existing SFDA methods focus on the adaptation phase, overlooking the impact of source domain training on model generalizability. |
| Approach: | They propose a source-free domain adaptation approach for Question Answering where a model trained on a domain is adapted to unlabeled target domains without additional source data. |
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