| Challenge: | Large language models excel in a wide range of tasks, but generating factually accurate outputs remains a challenge. |
| Approach: | They propose a method that dynamically adjusts model contributions at each decoding step based on uncertainty. |
| Outcome: | The proposed method significantly improves factual accuracy and reliability over existing methods. |
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Decoding Uncertainty: The Impact of Decoding Strategies for Uncertainty Estimation in Large Language Models (2025.findings-emnlp)
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| Challenge: | Decoding strategies affect the probability distribution underlying the output of a language model and can therefore affect both generation quality and uncertainty. |
| Approach: | They investigate the impact of decoding strategies on uncertainty estimation in large language models . |
| Outcome: | The proposed methods improve the uncertainty estimation of large language models by reducing repetition. |
Adaptive Contrastive Search: Uncertainty-Guided Decoding for Open-Ended Text Generation (2024.findings-emnlp)
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| Challenge: | Existing approaches to decode text to the most probable sequence have been proposed to address these challenges by improving coherence, diversity, and resemblance to human-generated text. |
| Approach: | They propose a novel decoding strategy that extends contrastive search by incorporating an adaptive degeneration penalty informed by the model’s estimated uncertainty at each generation step. |
| Outcome: | The proposed approach improves creativity and coherence while maintaining coherency across model architectures, languages, and datasets. |
GUARD: Glocal Uncertainty-Aware Robust Decoding for Effective and Efficient Open-Ended Text Generation (2025.findings-emnlp)
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Yuanhao Ding, Esteban Garces Arias, Meimingwei Li, Julian Rodemann, Matthias Aßenmacher, Danlu Chen, Gaojuan Fan, Christian Heumann, Chongsheng Zhang
| Challenge: | GUARD is a self-adaptive decoding method that balances coherence with diversity in open-ended text generation. |
| Approach: | They propose a self-adaptive decoding method that balances coherence and diversity . they combine global entropy estimates with local entropic deviations to integrate uncertainty . |
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Explaining and Improving Contrastive Decoding by Extrapolating the Probabilities of a Huge and Hypothetical LM (2024.emnlp-main)
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| Challenge: | Contrastive decoding (CD) improves the next-token distribution of a large expert language model (LM) using a small amateur LM. |
| Approach: | They propose a new unsupervised decoding method called Asymptotic Probability Decoding (APD) that extrapolates the probability curves from the LMs of different sizes to infer the asymptototic probabilities from an infinitely large LM. |
| Outcome: | The proposed method improves the next-token distribution of a large expert language model using a small amateur LM. |
Speculative Contrastive Decoding (2024.acl-short)
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| Challenge: | Large language models (LLMs) exhibit exceptional performance in language tasks, yet their auto-regressive inference is limited due to high computational requirements and is sub-optimal due to the exposure bias. |
| Approach: | They propose a decoding approach that leverages predictions from smaller language models to achieve both decoding acceleration and quality improvement. |
| Outcome: | The proposed method achieves both decoding acceleration and quality improvement on four diverse language tasks. |
Uncertainty-Aware Contrastive Sentence Embedding With Local Context Representation for Text Classification (2026.findings-acl)
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| Challenge: | Existing models for text classification are based on encoder-only transformers and generative pre-trained transformers. |
| Approach: | They propose an uncertainty-aware contrastive sentence embedding approach that addresses language ambiguity and inter-class separability for a text classification task. |
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ALW: Adaptive Layer-Wise contrastive decoding enhancing reasoning ability in Large Language Models (2025.findings-acl)
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| Challenge: | Existing research has demonstrated that contrast decoding of two different models can improve text quality in open-ended text generation but with limited gains on reasoning tasks. |
| Approach: | They propose a framework that dynamically disentangles noise in shallow layers from critical signals in deep layers to enhance reasoning ability. |
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CoCoA: Confidence- and Context-Aware Adaptive Decoding for Resolving Knowledge Conflicts in Large Language Models (2025.emnlp-main)
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| Challenge: | Existing contrastive decoding methods that handle conflict lack adaptability and can degrade performance in low conflict settings. |
| Approach: | They propose a token-level algorithm for principled conflict resolution and enhanced faithfulness that resolves conflict by utilizing confidence-aware measures and the generalized divergence between parametric and contextual distributions. |
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Entropy Guided Extrapolative Decoding to Improve Factuality in Large Language Models (2025.coling-main)
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| Challenge: | Large language models (LLMs) exhibit impressive natural language capabilities but suffer from hallucination – generating content that does not align with realworld facts. |
| Approach: | They propose to extrapolate critical token probabilities beyond the last layer to improve decoding by manipulating the predicted distributions at inference time. |
| Outcome: | The proposed methods surpass state-of-the-art on multiple datasets by large margins. |
Achieving Stronger Generation via Simple Contrastive Tuning (2024.findings-emnlp)
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| Challenge: | Recent years have witnessed remarkable progress in large language models (LLMs). |
| Approach: | They propose a framework for contrastive decoding to enhance instruction-tuned models. |
| Outcome: | The proposed framework improves model performance without additional data or computational resources. |