Papers with prompting framework
Large Language Models Know Your Contextual Search Intent: A Prompting Framework for Conversational Search (2023.findings-emnlp)
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| Challenge: | Existing methods for understanding users’ contextual search intent show unsatisfactory effectiveness and robustness to handle real conversational search scenarios. |
| Approach: | They propose to use large language models to generate multiple query rewrites and hypothetical responses and to aggregate them into an integrated representation that can robustly represent the user’s real contextual search intent. |
| Outcome: | The proposed framework can generate multiple query rewrites and hypothetical responses and can be used to represent the user’s real contextual search intent. |
Content-Specific Humorous Image Captioning Using Incongruity Resolution Chain-of-Thought (2024.findings-naacl)
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| Challenge: | Existing methods for generating humorous captions are generic and do not capture the content of images. |
| Approach: | They propose a framework that generates content-specific resolutions from fine details extracted from an image and integrates logit bias and negative sampling to suppress the output of generic resolutions. |
| Outcome: | The proposed framework generates humorous captions tailored to the content of specific input images. |
Sketch-of-Thought: Efficient LLM Reasoning with Adaptive Cognitive-Inspired Sketching (2025.emnlp-main)
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| Challenge: | Recent advances in large language models (LLMs) have enabled strong reasoning capabilities through Chain-of-Thought (CoT) prompting. |
| Approach: | They propose a framework that integrates cognitively inspired reasoning paradigms with linguistic constraints to reduce token usage while preserving reasoning accuracy. |
| Outcome: | The proposed framework reduces token usage while preserving reasoning accuracy across 18 reasoning datasets across multiple domains, languages, and modalities. |
Grammar as Control: Modular Language Generation for the Long Tail (2026.acl-long)
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| Challenge: | Large language models (LLMs) can bootstrap language technologies for long-tail languages . however, without structured guidance, they produce narrow, unrepresentative samples . |
| Approach: | They propose a framework that transforms descriptive grammars into explicit control mechanisms that guide LLMs to generate typologically balanced synthetic data for downstream training. |
| Outcome: | The proposed framework improves typological entropy and yields a "student-beats-teacher" effect across three low-resource languages. |