Papers with LLM generations
Constrained Decoding with Speculative Lookaheads (2025.naacl-long)
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Nishanth Sridhar Nakshatri, Shamik Roy, Rajarshi Das, Suthee Chaidaroon, Leonid Boytsov, Rashmi Gangadharaiah
| Challenge: | Constrained decoding with lookahead heuristics is effective for aligning LLM generations to human preferences, but the extensive lookaheaded roll-out operations for each generated token make it prohibitively expensive. |
| Approach: | They propose a technique that uses lookaheads to align LLMs to human preferences . they propose 2.2x to 12.15x speedup over greedy decoding . |
| Outcome: | The proposed technique achieves 2.2x to 12.15x speedup over greedy decoding without significant performance reduction. |
ZEROTOP: Zero-Shot Task-Oriented Semantic Parsing using Large Language Models (2023.emnlp-main)
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| Challenge: | Existing LLMs cannot generalize to domain-specific parsing tasks in a zero-shot setting. |
| Approach: | They propose a task-oriented parsing method that decomposes parse problem into abstractive and extractive question-answering problems. |
| Outcome: | The proposed method decomposes a parsing problem into abstractive and extractive question-answering (QA) problems. |
Improving Diversity of Commonsense Generation by Large Language Models via In-Context Learning (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have shown proficiency in enhancing the generation quality across various tasks without the need for any fine-tuning. |
| Approach: | They propose a method that diversifies the LLM generations while preserving their quality. |
| Outcome: | The proposed method can be used as training data to improve diversity in existing commonsense generators. |
Boosting Zero-Shot Crosslingual Performance using LLM-Based Augmentations with Effective Data Selection (2024.findings-acl)
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| Challenge: | Large language models generate task-specific data via zero-shot prompting and promote cross-lingual transfer for low-resource target languages. |
| Approach: | They propose to use a teacher model to label LLM generations and use their label probabilities to identify a representative subset of diverse generations that boost zero-shot accuracies while being efficient. |
| Outcome: | The proposed models generate task-specific data via zero-shot prompting and promote cross-lingual transfer for low-resource target languages. |
SOLAR: Towards Characterizing Subjectivity of Individuals through Modeling Value Conflicts and Trade-offs (2025.emnlp-main)
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| Challenge: | Existing studies suggest that Large Language Models can account for individual-level subjectivity, yet exploring whether LLMs can generate perspectives and reasoning that align well with a specific persona or demographic information has not been adequately studied. |
| Approach: | They propose a framework that observes value conflicts and trade-offs in user-generated texts to better represent subjective ground of individuals. |
| Outcome: | The proposed framework improves inference performance for users with limited data and in controversial situations. |