Papers with sampling efficiency
k-SemStamp: A Clustering-Based Semantic Watermark for Detection of Machine-Generated Text (2024.findings-acl)
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| Challenge: | Recent watermarked generation algorithms inject detectable signatures during language generation to facilitate post-hoc detection. |
| Approach: | They propose a watermark which assigns signatures to each watermarked sentence according to locality-sensitive hashing (LSH) they propose k-SemStamp, which uses kmeans clustering to partition the semantic space with awareness of inherent semantic structure. |
| Outcome: | The proposed watermark improves its robustness and sampling efficiency while preserving the generation quality, making it more effective for machine-generated text detection. |
Reasoning Aware Self-Consistency: Leveraging Reasoning Paths for Efficient LLM Sampling (2025.naacl-long)
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| Challenge: | Large Language Models (LLMs) generate reasoning paths before answers, but lack a systematic approach to determine optimal number of samples or select the most faithful rationale. |
| Approach: | They propose a framework that evaluates the quality of reasoning and consistency of answers for each generated sample and uses criteria-based stopping and weighted majority voting to guide early stopping decisions and rationale selection. |
| Outcome: | The proposed framework outperforms existing methods while maintaining accuracy. |
Incentivizing Parametric Knowledge via Reinforcement Learning with Verifiable Rewards for Cross-Cultural Entity Translation (2026.acl-long)
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Jiang Zhou, Xiaohu Zhao, Xinwei Wu, Tianyu Dong, Hao Wang, Yangyang Liu, Heng Liu, Linlong Xu, Longyue Wang, Weihua Luo, Deyi Xiong
| Challenge: | Current systems often fall short of this goal in settings where translation hinges on culturally grounded entities such as books, films, places, songs and idioms. |
| Approach: | They propose a framework that anchors supervision on a verifiable, entity-level reward signal and incorporates lightweight structural gates to stabilize optimization. |
| Outcome: | The proposed framework improves on XC-Translate and shows that it can learn a robust reasoning process rather than imitating reference translations. |
R-TeaFor: Regularized Teacher-Forcing for Abstractive Summarization (2022.emnlp-main)
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| Challenge: | Existing studies on teacher-forcing do not consider the pairwise relationship between training data and modified data, which provides more information during training. |
| Approach: | They propose to use teacher-forcing to address exposure bias problem by modifying training data to simulate model-generated results. |
| Outcome: | Empirically, the proposed method outperforms state-of-the-art models and can be generalized to different pre-trained models. |
Thinking with Map: Reinforced Parallel Map-Augmented Agent for Geolocalization (2026.findings-acl)
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Yuxiang Ji, Yong Wang, Ziyu Ma, Yiming Hu, Hailang Huang, Xuecai Hu, Guanhua Chen, Liaoni Wu, Xiangxiang Chu
| Challenge: | Existing large vision-language model (LVLM) approaches overlook a common strategy used by humans — using maps. |
| Approach: | They propose a method to equip a vision-language model with the ability to think with maps and optimize it using agentic reinforcement learning and parallel test-time scaling. |
| Outcome: | The proposed method outperforms open- and closed-source models on most metrics. |
InfoDiffusion: Information Entropy Aware Diffusion Process for Non-Autoregressive Text Generation (2023.findings-emnlp)
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| Challenge: | Existing text diffusion models have failed to capture the difference between the “easy-first” process and the “keyword-first" process of humans. |
| Approach: | They propose a non-autoregressive text diffusion model that incorporates a "keyinfo-first" generation strategy and a noise schedule based on the amount of text information. |
| Outcome: | The proposed model outperforms the baseline model in terms of generation quality and diversity, and higher sampling efficiency. |
SimMark: A Robust Sentence-Level Similarity-Based Watermarking Algorithm for Large Language Models (2025.emnlp-main)
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| Challenge: | Experimental results demonstrate that SimMark surpasses previous sentence-level watermarking techniques in robustness, sampling efficiency, and applicability across diverse domains. |
| Approach: | They propose a sentence-level watermarking algorithm that makes LLM outputs traceable without requiring access to model internals. |
| Outcome: | The proposed algorithm surpasses previous sentence-level watermarking techniques in robustness, sampling efficiency, and applicability across diverse domains while maintaining the text quality and fluency. |
Does RLVR Extend Reasoning Boundaries? Investigating Capability Expansion in Vision-Language Models (2026.acl-long)
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| Challenge: | Recent studies suggest that RLVR amplifies behaviors inherent to the pre-training distribution rather than inducing new capabilities. |
| Approach: | They propose a framework for RLVR that extends the spatial reasoning boundary . they use a mapping framework where the difficulty is precisely regulated by path length and number of turns . |
| Outcome: | The proposed framework extends the spatial reasoning boundary on two real-world navigation benchmarks. |