Papers with adaptive approach
MoCE: Adaptive Mixture of Contextualization Experts for Byte-based Neural Machine Translation (2025.naacl-long)
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| Challenge: | Byte-based machine translation systems can be used in multilingual settings. |
| Approach: | They propose a method that maps each character to specific byte(s) they propose byte-level tokenization that eliminates unknown words . |
| Outcome: | The proposed method outperforms existing methods without manual adjustment of hyper-parameters and surpasses subword-based models with fewer parameters in Ted-59 dataset. |
GEMINI: Controlling The Sentence-Level Summary Style in Abstractive Text Summarization (2023.emnlp-main)
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| Challenge: | Existing models that mimic human summarization techniques are difficult to imitate. |
| Approach: | They propose an adaptive model that integrates a rewriter and a generator to mimic the sentence rewriting and abstracting techniques. |
| Outcome: | The proposed model outperforms baselines on WikiHow and on other datasets. |
Adaptive Document Retrieval for Deep Question Answering (D18-1)
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| Challenge: | Existing methods for deep question answering do not understand the exact interplay between document retrieval and machine comprehension. |
| Approach: | They propose an adaptive document retrieval model that learns the optimal document number, conditional on the size of the corpus and the query. |
| Outcome: | The proposed model outperforms state-of-the-art methods on multiple benchmark datasets and in the context of corpora with variable sizes. |
Beyond Excess and Deficiency: Adaptive Length Bias Mitigation in Reward Models for RLHF (2025.findings-naacl)
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| Challenge: | Existing efforts to mitigate length bias in reward models have decreased accuracy . achieving an automatic proxy that perfectly replicates human judgment is challenging in practice. |
| Approach: | They propose an adaptive approach that dynamically adjusts the influence of response length in reward evaluations according to the context of the query. |
| Outcome: | The proposed approach reduces unnecessary verbosity while improving overall response quality. |
VerifiAgent: a Unified Verification Agent in Language Model Reasoning (2025.findings-emnlp)
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| Challenge: | Existing verification methods are typically model-specific or domain-restricted . Existing methods require significant computational resources and lack scalability . |
| Approach: | a unified verification agent integrates two levels of verification: meta-verification and tool-based adaptive verification. |
| Outcome: | The proposed agent outperforms baseline verification methods among reasoning tasks. |