Develops a theoretical perspective showing no hard rule can perfectly reject false unsupported trajectories while retaining true-but-unobserved ones under incomplete graph evidence, and characterizes soft grounding as KL-regularized deformation of the LLM prior.
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6 Pith papers cite this work, alongside 289 external citations. Polarity classification is still indexing.
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Constrained decoding derived from OpenAPI specifications eliminates hallucinated web API calls in LLM-generated code and substantially improves correctness across 24 models, while retrieval-augmented generation yields inconsistent, model-dependent gains.
SLoW selects low-frequency word dictionaries to boost LLM translation quality and efficiency across 100 languages from FLORES.
DIP interleaves English word translations into non-English prompts to boost multilingual reasoning on synthetic benchmarks spanning 10-200 languages.
LLMs exhibit different trade-offs between rule compliance and communicative success across prompting, generation constraints, and representation interventions, but remain substantially weaker than humans at guessing under lexical constraints.
A literature survey that organizes prompting, fine-tuning, preference optimization, and context-aware techniques for LLM-based machine translation with emphasis on low-resource languages.
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Bridging the Linguistic Divide: A Survey on Leveraging Large Language Models for Machine Translation
A literature survey that organizes prompting, fine-tuning, preference optimization, and context-aware techniques for LLM-based machine translation with emphasis on low-resource languages.