Papers with MoP
Mixture-of-Partitions: Infusing Large Biomedical Knowledge Graphs into BERT (2021.emnlp-main)
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| Challenge: | Infusing factual knowledge into pre-trained models is fundamental for many knowledge-intensive tasks. |
| Approach: | They propose an infusion approach that partitions a large knowledge graph into smaller sub-graphs and infuses their specific knowledge into various BERT models using lightweight adapters. |
| Outcome: | The proposed approach improves the underlying BERTs and achieves new SOTA performance on six downstream tasks. |
Nested-Refinement Metamorphosis: Reflective Evolution for Efficient Optimization of Networking Problems (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) excel in network algorithm design but suffer from inefficient iterative coding and high computational costs. |
| Approach: | They propose a method to iteratively refine task descriptions and metamorphosis on algorithms to generate more effective solutions. |
| Outcome: | Experimental results show that Nested-Refinement Metamorphosis outperforms state-of-the-art approaches in performance and efficiency. |
Measuring Psychological Depth in Language Models (2024.emnlp-main)
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Fabrice Harel-Canada, Hanyu Zhou, Sreya Muppalla, Zeynep Yildiz, Miryung Kim, Amit Sahai, Nanyun Peng
| Challenge: | Current evaluations of creative stories focus on objective properties of the text, such as its style, coherence, diversity, and creativity. |
| Approach: | They propose a framework that measures an LLM's ability to produce authentic and narratively complex stories that provoke emotion, empathy, and engagement. |
| Outcome: | The proposed framework shows that humans can consistently evaluate stories based on the PDS (0.72 Krippendorff’s alpha). |
Tree-of-Prompts: Abstracting Control-Flow for Prompt Optimization (2025.findings-acl)
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| Challenge: | Existing prompt optimization methods struggle with disjoint cases in complex tasks. |
| Approach: | They propose a tree-of-prompts structure which expands child prompts from parent prompts . they propose to use a nested if-else structure to address varying similarities and complexities . |
| Outcome: | The proposed tree-of-prompts outperforms PromptAgent and MoP on Gorilla, MATH and subset of BBH benchmarks. |
Mixture-of-Personas Language Models for Population Simulation (2025.findings-acl)
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Ngoc Bui, Hieu Trung Nguyen, Shantanu Kumar, Julian Theodore, Weikang Qiu, Viet Anh Nguyen, Rex Ying
| Challenge: | Pretrained LLMs fail to capture behavioral diversity of target populations due to inherent variability across individuals and groups. |
| Approach: | They propose a probabilistic prompting method that aligns LLM responses with the target population. |
| Outcome: | Experiments show that the proposed method outperforms competing methods in alignment and diversity metrics. |