Papers with parameter training
GenPoE: Generative Passage-level Mixture of Experts for Knowledge Enhancement of LLMs (2025.findings-emnlp)
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| Challenge: | GenPoE is a passage-level mixture of experts for enhancing knowledge of large language models. |
| Approach: | They propose a novel “generative” passage-level mixture of experts (MoEs) that takes in-context retrieved passages and generates their “expert” parameters. |
| Outcome: | The proposed system is based on a novel hypernetwork which takes in-context retrieved passages and generates their "expert'' parameters. |
Tied-LoRA: Enhancing parameter efficiency of LoRA with Weight Tying (2024.naacl-long)
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| Challenge: | a new paradigm for low-rank Adaptation (LoRA) uses weight tying and selective training to improve parameter efficiency. |
| Approach: | They propose a paradigm that uses weight tying and selective training to enhance parameter efficiency of Low-rank Adaptation. |
| Outcome: | The proposed paradigm achieves comparable performance to LoRA with reduced model complexity . the proposed paradigm can be used for a variety of tasks and languages . |
Let’s Focus on Neuron: Neuron-Level Supervised Fine-tuning for Large Language Model (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) are composed of neurons that exhibit diverse behaviors and roles. |
| Approach: | They propose a novel approach that refines the granularity of parameter training down to the individual neuron, enabling a more parameter-efficient fine-tuning model. |
| Outcome: | The proposed approach exceeds the performance of full-parameter fine-tuning and PEFT and provides insights into the analysis of neurons. |
Dynamic Open-book Prompt for Conversational Recommender System (2023.findings-emnlp)
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| Challenge: | Existing methods for prompt learning use only training samples for parameter training, limiting the performance of existing methods. |
| Approach: | They propose a Dynamic Open-book Prompt approach where the open book stores user's experiences in historical data and dynamically constructs the prompt to memorize the user' s current utterance. |
| Outcome: | The proposed model improves on the existing methods on the ReDial dataset and shows that it can be used to learn contextually relevant recommendations. |
When Rules Learn: A Self-Evolving Agent for Legal Case Retrieval (2026.findings-acl)
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| Challenge: | Existing dense retrieval methods have achieved notable progress, but their effectiveness in legal case retrieval remains limited. |
| Approach: | They propose a self-evolving framework for rule-driven query rewriting that enhances BM25 without any parameter training. |
| Outcome: | The proposed framework outperforms non-evolutionary baselines, including human-designed rules and greedy rule selection, especially when powered by a high-capacity core LLM. |