Papers with stochastic process
Dialogue Planning via Brownian Bridge Stochastic Process for Goal-directed Proactive Dialogue (2023.findings-acl)
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| Challenge: | Goal-directed dialogue systems aim to proactively reach a pre-determined target through multi-turn conversations. |
| Approach: | They propose a coherent dialogue planning approach that uses a stochastic process to model the temporal dynamics of dialogue paths. |
| Outcome: | The proposed approach generates more coherent utterances and achieves the goal with a higher success rate. |
Conditional Poisson Stochastic Beams (2021.emnlp-main)
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| Challenge: | Existing methods for beam search are based on a deterministic approach, but the results are not as accurate as those used in SBS. |
| Approach: | They propose a method that turns beam search into a stochastic process by using conditional Poisson sampling design instead of taking the maximizing set at each iteration. |
| Outcome: | The proposed method produces lower variance and more efficient estimators than SBS, even showing improvements in high entropy settings. |
BBScoreV2: Learning Time-Evolution and Latent Alignment from Stochastic Representation (2025.emnlp-main)
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| Challenge: | Autoregressive generative models are gaining traction in language tasks such as text generation and machine translation. |
| Approach: | They propose a likelihood-based evaluation metric that fits transformer-based model embeddings into a stochastic process and propose it as a probability-based metric. |
| Outcome: | The proposed model embeddings induce a "clustered-to-temporal ordered" mapping of language model representations in high-dimensional space, and this structure enhances performance on tasks such as temporal consistency evaluation and AI-generated content detection. |
Martingale Foresight Sampling: A Principled Approach to Inference-Time LLM Decoding (2026.eacl-long)
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| Challenge: | Standard autoregressive decoding in large language models is short-sighted, often failing to find globally optimal reasoning paths due to token-by-token generation process. |
| Approach: | They propose a principled framework that reformulates LLM decoding as a problem of identifying an optimal stochastic process. |
| Outcome: | The proposed framework surpasses state-of-the-art methods in accuracy while significantly improving computational efficiency. |
Beyond Sampling: Self-Sorting for Long-Context Ranking (2026.findings-eacl)
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| Challenge: | Large language models (LLMs) remain unstable on long-context ranking. |
| Approach: | They propose a method that fuses explicit within-list positions with implicit cross-list preferences to score entities and return a top-k set. |
| Outcome: | Experimental results show that large language models remain unstable on long-context ranking . |
Categorial Grammar Induction with Stochastic Category Selection (2024.lrec-main)
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| Challenge: | categorial grammar inducers have been used to learn from raw data, but they use shortcuts to ensure branching behavior. |
| Approach: | They propose a grammar inducer that learns from raw data and does not rely on bias terms . they show a recall-homogeneity of 0.48 on a corpus of English child-directed speech . |
| Outcome: | The proposed model achieves a recall-homogeneity of 0.48 on a corpus of English child-directed speech . |
When is a Language Process a Language Model? (2024.findings-acl)
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| Challenge: | In some pathological situations, such a stochastic process may "leak" probability mass onto the set of infinite strings. |
| Approach: | They propose to view a language model as a discrete stochastic process X t : t = = t + . |
| Outcome: | The proposed conditions of tightness are generalized to language models and the literature. |