Papers with proposal distribution
Neural Particle Smoothing for Sampling from Conditional Sequence Models (N18-1)
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| Challenge: | In structured prediction problems, labeling an input string with a length-T sequence of tags becomes intractable. |
| Approach: | They propose a sequential Monte Carlo method for sampling annotations of an input string from a given probability model. |
| Outcome: | The proposed method improves the quality of the sample. |
On Importance Sampling-Based Evaluation of Latent Language Models (2020.acl-main)
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| Challenge: | Existing approaches to evaluate language models using latent structures are intractable as they require marginalizing over the latent space. |
| Approach: | They propose to use importance sampling to evaluate latent language models . they elucidate subtle differences in how importance sampling is applied . |
| Outcome: | The proposed model performs better on tasks requiring structure and interpretability. |
Syntactic Control of Language Models by Posterior Inference (2025.findings-acl)
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| Challenge: | Syntactic control of generated text is crucial for many domain-specific applications of language models, where structural constraints, such as formality, grammatical correctness, or adherence to a given template, can significantly affect usability and readability. |
| Approach: | They propose to use posterior inference to enforce target constituency structure by sampling from a proposal distribution with a syntactic tagger to ensure that each generated token aligns with the desired syntakic structure. |
| Outcome: | The proposed method increases the F1 score from 12.31 (GPT2-large) and 35.33 (Llama3-8B) to about 93 in both cases without compromising the language model’s fluency. |