| Challenge: | Existing studies on compressing or distilling ensemble models have shown that they increase computational costs and reduce performance. |
| Approach: | They propose an unsupervised method that combines multiple models by selecting a majority-like output in post-processing. |
| Outcome: | The proposed method performs better than the current ensemble methods on a news-headline-generation task. |
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Dissecting Generation Modes for Abstractive Summarization Models via Ablation and Attribution (2021.acl-long)
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| Challenge: | Abstractive summarization models have made great strides in recent years, but little is known about how they actually form summaries and how to understand where their decisions come from. |
| Approach: | They propose a two-step method to interpret summarization model decisions by categorizing each decoder decision into one of several generation modes. |
| Outcome: | The proposed method can identify phrases the summarization model has memorized and determine where in the training pipeline this memorization happened, and study complex generation phenomena on a per-instance basis. |
Hit the Sweet Spot! Span-Level Ensemble for Large Language Models (2025.coling-main)
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| Challenge: | a recent study focused on sample-level and token-level ensembles, which hinder dynamic correction and enhancement of outputs during the generation process. |
| Approach: | They propose a span-level ensemble method that balances real-time adjustments and accurate ensemble decisions. |
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URG: A Unified Ranking and Generation Method for Ensembling Language Models (2024.findings-acl)
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| Challenge: | Existing approaches to rank and generate large language models have limited performance due to time-intensive nature of ranking process and lack of error propagation. |
| Approach: | They propose a framework that jointly ranks the outputs of Large Language Models and generates fine-grained fusion results. |
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AdaFuse: Adaptive Ensemble Decoding for Large Language Models (2026.acl-long)
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Chengming Cui, Tianxin Wei, Ziyi Chen, Ruizhong Qiu, Zhichen Zeng, Zhining Liu, Xuying Ning, Duo Zhou, Jingrui He
| Challenge: | Existing ensemble approaches to large language models lack flexibility for mid-generation adaptation. |
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AMRs Assemble! Learning to Ensemble with Autoregressive Models for AMR Parsing (2023.acl-short)
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| Challenge: | et al., 2013) examines the current state-of-the-art in AMR parsing . current models violate structural constraints, but they can corrupt graphs . |
| Approach: | They propose two new ensemble strategies to improve AMR parsing robustness and reduce computational time. |
| Outcome: | The proposed methods improve robustness to structural constraints while reducing computational time. |
SummaReranker: A Multi-Task Mixture-of-Experts Re-ranking Framework for Abstractive Summarization (2022.acl-long)
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| Challenge: | Sequence-to-sequence neural networks have enabled great progress in abstractive summarization. |
| Approach: | They propose to train a second-stage model performing re-ranking on a set of summary candidates by using a mixture of experts. |
| Outcome: | The proposed model outperforms the base model on CNN- DailyMail, XSum and Reddit TIFU with a base PEGASUS. |
BRIO: Bringing Order to Abstractive Summarization (2022.acl-long)
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| Challenge: | Abstractive summarization models are often trained with maximum likelihood estimation (MLE) . mLE assumes a deterministic (one-point) target distribution, but can cause performance degradation . |
| Approach: | They propose a new training paradigm which assumes a non-deterministic distribution so that different candidate summaries are assigned probability mass according to their quality. |
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Twist Decoding: Diverse Generators Guide Each Other (2022.emnlp-main)
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Jungo Kasai, Keisuke Sakaguchi, Ronan Le Bras, Hao Peng, Ximing Lu, Dragomir Radev, Yejin Choi, Noah A. Smith
| Challenge: | Using a variety of language generation models, ensembling models is challenging during inference. |
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Self-Ensemble of N-best Generation Hypotheses by Lexically Constrained Decoding (2023.emnlp-main)
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| Challenge: | Existing studies have improved generation quality by explicitly reranking N-best candidates. |
| Approach: | They propose a method that ensembles N-best hypotheses to improve natural language generation by combining high-quality fragments of N- best hypothese . they use tokens that should or should not be present in the final output as lexical constraints to improve quality of generation. |
| Outcome: | Empirical results show that the proposed method outperforms strong N-best reranking methods on paraphrase generation, summarisation, and constrained text generation. |
Self-Ensemble: Mitigating Confidence Distortion for Large Language Models (2025.findings-emnlp)
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| Challenge: | Large Language Models exhibit a confidence distortion problem on multichoice question-answering . Self-Ensemble solves this problem by splitting the choices into several groups . |
| Approach: | They propose a method that splits LLM choices into several groups and ensembles them to reach a final decision. |
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