Papers with Monte Carlo sampling
SEAD: A Surrogate-free Label-only Membership Inference Attack against Pre-trained LLMs with Semantic-Aware Density (2026.findings-acl)
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| Challenge: | Existing membership inference attacks require access to complete logits, but such access is often unavailable in real-world deployments where only the generated text is exposed. |
| Approach: | They propose a surrogate-free label-only MIA approach that directly estimates token probabilities through Monte Carlo sampling of the target model. |
| Outcome: | The proposed approach outperforms existing label-only attacks and serves as a foundational density estimator in the label-exclusive setting. |
Neural Mixed Counting Models for Dispersed Topic Discovery (2020.acl-main)
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| Challenge: | Existing methods for inference of parameter parameters are time-consuming and difficult to use. |
| Approach: | They propose two efficient neural mixed counting models that use the negative binomial distribution as the prior for dispersed topic discovery. |
| Outcome: | The proposed models outperform state-of-the-art models in terms of perplexity and topic coherence on real-world datasets. |
Using Context in Neural Machine Translation Training Objectives (2020.acl-main)
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| Challenge: | Neural Machine Translation (NMT) training is based on document-level metrics, not sentence-level BLEU. |
| Approach: | They propose to merge document-level metrics with batch-level documents to improve NMT training. |
| Outcome: | The proposed training is more robust for document-level metrics than sequence MRT and maximum-likelihood training. |
Fixing Distribution Shifts of LLM Self-Critique via On-Policy Self-Play Training (2025.acl-long)
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| Challenge: | Large language models show impressive performance in a wide range of linguistic tasks, but their performance on complex reasoning tasks is still signif-icantly lower than the human level. |
| Approach: | They propose a reinforcement learning framework to synchronize the reasoning and critique capabilities of language models by using Monte Carlo sampling to give appropriate rewards to the model's critique content. |
| Outcome: | The proposed framework improves the model's reasoning and critique capabilities by 5.40 and 3.66 points, respectively, compared to the best baseline approach. |