Papers with mutation
Breeding Machine Translations: Evolutionary approach to survive and thrive in the world of automated evaluation (2023.acl-long)
Copied to clipboard
| Challenge: | a genetic algorithm (GA) based method improves MT quality and identifies weaknesses in evaluation metrics. |
| Approach: | They propose a genetic algorithm-based method for modifying n-best lists produced by a machine translation system using a fitness function. |
| Outcome: | The proposed method improves translation quality and identifies weaknesses in evaluation metrics. |
Literature Retrieval for Precision Medicine with Neural Matching and Faceted Summarization (2020.findings-emnlp)
Copied to clipboard
| Challenge: | IR for precision medicine often involves looking for multiple pieces of evidence that characterize a patient case. |
| Approach: | They propose a document reranking approach that combines neural query-document matching and text summarization toward such retrieval scenarios. |
| Outcome: | The proposed approach achieves state-of-the-art performance on NIST's TREC-PM track dataset. |
EvoAgent: Towards Automatic Multi-Agent Generation via Evolutionary Algorithms (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing work on extending specialized agents to multi-agent systems is dependent on human-designed frameworks, limiting the functional scope and scalability of agent systems. |
| Approach: | They propose a generic method to automatically extend specialized agents to multi-agent systems via evolutionary algorithm . they consider existing agent frameworks as the initial individual and apply evolutionary operators to generate multiple agents with diverse settings. |
| Outcome: | The proposed method can extend specialized agents to multi-agent systems . it can generate multiple agents with diverse settings, and improves performance across tasks . |
Knowledge Fusion By Evolving Weights of Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | Experimental results on mainstream language models show that Evolver outperforms previous state-of-the-art models by large margins due to the high training costs of large language models. |
| Approach: | They propose a method to integrate multiple models from diverse training scenarios into a unified model. |
| Outcome: | The proposed method outperforms state-of-the-art models on mainstream language models by large margins. |
DPGA-TextSyn: Differentially Private Genetic Algorithm for Synthetic Text Generation (2025.findings-acl)
Copied to clipboard
Zhonghao Sun, Zhiliang Tian, Yiping Song, Yuyi Si, Juhua Zhang, Minlie Huang, Kai Lu, Zeyu Xiong, Xinwang Liu, Dongsheng Li
| Challenge: | Existing methods to fine-tune large language models pose privacy risks . researchers have synthesized data with strong generation capabilities closed-source LLMs to alleviate this problem . |
| Approach: | They propose to combine general LLMs with genetic algorithm to produce relevant and diverse synthetic text under differential privacy constraints. |
| Outcome: | The proposed method significantly improves the performance of the model in downstream tasks while ensuring privacy. |
InstOptima: Evolutionary Multi-objective Instruction Optimization via Large Language Model-based Instruction Operators (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies focus on automating instruction generation but do not consider other objectives that impact instruction quality. |
| Approach: | They propose an approach that treats instruction generation as an evolutionary multi-objective optimization problem. |
| Outcome: | The proposed approach improves fine-tuning performance and the generation of high-quality instructions. |
LCO: LLM-based Constraint Optimization for Safer Agentic LLMs in Real-world Tasks (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing defense methods are insufficient to address in-context reward hacking (ICRH), where LLMs iteratively optimize their behavior to maximize proxy objectives, resulting in harmful side effects. |
| Approach: | They propose a framework that reduces in-context reward hacking (ICRH) through repeated interactions with the environment. |
| Outcome: | The proposed framework reduces ICRH without model fine-tuning while maintaining task performance. |
Where Did It Go Wrong? Capability-Oriented Failure Attribution for Vision-and-Language Navigation Agents (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing testing methods are system-level and provide limited insight into which capability deficiencies cause task failures. |
| Approach: | They propose a capability-oriented testing approach that enables failure detection and attribution by seed selection and mutation. |
| Outcome: | The proposed method detects more failure cases and pinpoints capability-level deficiencies than state-of-the-art baselines, providing more interpretable and actionable guidance for improving embodied agents. |