Papers with average Exact Match score

2 papers
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.
Outcome: The proposed framework improves the BERT-Large baseline by 8.39 and 7.22 respectively.
LR²Bench: Evaluating Long-chain Reflective Reasoning Capabilities of Large Language Models via Constraint Satisfaction Problems (2025.findings-acl)

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Challenge: Recent advances in o1-like models have significantly enhanced the reasoning abilities of Large Language Models (LLMs).
Approach: They propose a benchmark to evaluate the Long-chain Reflective Reasoning capabilities of Large Language Models.
Outcome: The proposed benchmark evaluates the Long-chain Reflective Reasoning capabilities of Large Language Models (LLMs) it consists of 850 samples across six Constraint Satisfaction Problems (CSPs)

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