Papers with parameter tuning
Scalable graph-based method for individual named entity identification (D19-53)
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| Challenge: | Named entity recognition (NED) is a method for identifying named entities within a knowledge base. |
| Approach: | They propose a method for individual identification requiring few annotated data samples. |
| Outcome: | The proposed method is well-motivated for integration in real systems. |
SURE: Mutually Visible Objects and Self-generated Candidate Labels For Relation Extraction (2025.coling-main)
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| Challenge: | Joint relation extraction models face high computational complexity, complex network architectures, difficult parameter tuning and limited interpretability. |
| Approach: | They develop a candidate label marker mechanism that prioritizes strategic label selection over simple label generation. |
| Outcome: | The proposed candidate label marks improve the SOTA methods by 2.5%, 1.9%, 1.2% . the proposed candidate labels improve the performance of the proposed methods . |
Empowering parameter-efficient transfer learning by recognizing the kernel structure in self-attention (2022.findings-naacl)
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| Challenge: | Existing methods to fine-tune pre-trained language models are parameter efficient . fine- tuning the models requires multiple copies of the parameters, which is inefficient. |
| Approach: | They propose to use kernel-based adapters to tune only a few parameters while freezing the rest of the parameters. |
| Outcome: | The proposed methods achieve or improve strong performance over a diverse set of natural language generation and understanding tasks. |
Parameter Selection: Why We Should Pay More Attention to It (2021.acl-short)
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| Challenge: | supervised learning is a challenging process due to the huge number of parameter combinations. |
| Approach: | They present an example of parameter selection in supervised learning . authors use a set of frequently occurring labels without a parameter tuning . they say this illustrates the seriousness of parameter tuning in a supervised field . |
| Outcome: | The proposed study shows that without adequate attention, the research progress can be uncertain or even illusive. |
Temporal-Aware Soft Prompt Tuning for Automatic Text Dating (2025.naacl-long)
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| Challenge: | Existing methods for automatic text dating overlook the evolution of word meanings in texts spanning long periods. |
| Approach: | They propose a temporal-aware text representation that dynamically captures both semantic variance and invariance. |
| Outcome: | The proposed approach outperforms existing methods on two diachronic datasets. |
LIRE: listwise reward enhancement for preference alignment (2024.findings-acl)
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| Challenge: | prevailing approaches to preference alignment focus on pairwise comparisons, with limited exploration into multi-response scenarios. |
| Approach: | They propose a listwise reward enhancement approach that integrates offline rewards of multiple responses into a streamlined listwise framework. |
| Outcome: | The proposed approach outperforms existing methods on dialogue and summarization tasks with good transferability to out-of-distribution data. |
ATFormer: A Learned Performance Model with Transfer Learning Across Devices for Deep Learning Tensor Programs (2023.emnlp-main)
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| Challenge: | Compilation-based methods with performance models have poor measurement accuracy and transferability between platforms. |
| Approach: | They propose a compiler that automatically generates tensors and automatically tunes them for different hardware platforms. |
| Outcome: | The proposed model reduces inference time and costs on modern DNN benchmarks. |
Using J-K-fold Cross Validation To Reduce Variance When Tuning NLP Models (C18-1)
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| Challenge: | a recent study shows that performance estimations are unstable and variable . this makes it difficult to use parameter tuning and model selection . |
| Approach: | They propose to use a less variable CV method to evaluate performance . they propose lower choices of K than are typically seen in the NLP literature . |
| Outcome: | The proposed method can be used for parameter tuning and performance estimation, but it is unstable and unstable. |
Attention-Seeker: Dynamic Self-Attention Scoring for Unsupervised Keyphrase Extraction (2025.coling-main)
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| Challenge: | Unsupervised keyphrase extraction methods require large amounts of labeled data and are often domainspecific, limiting their practical applicability. |
| Approach: | They propose an unsupervised keyphrase extraction method that leverages self-attention maps from a Large Language Model to estimate the importance of candidate phrases. |
| Outcome: | The proposed method outperforms baseline models on four datasets and is highly efficient on three of four dataset. |
GradTS: A Gradient-Based Automatic Auxiliary Task Selection Method Based on Transformer Networks (2021.emnlp-main)
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| Challenge: | A key problem in multi-task learning (MTL) research is how to select high-quality auxiliary tasks automatically. |
| Approach: | They propose an automatic auxiliary task selection method based on gradient calculation in Transformer-based models that improves MT-DNN performance. |
| Outcome: | The proposed method improves MT-DNN performance on 8 natural language understanding (GLUE) tasks, while costing less than AUTOSEM and comparable GPU consumption. |
Revisiting Large Language Models as Zero-shot Relation Extractors (2023.findings-emnlp)
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| Challenge: | Recent studies show that large language models (LLMs) transfer well to new tasks out-of-the-box . relationship extraction (RE) involves a certain degree of labeled or unlabeled data even under zero-shot setting. |
| Approach: | They propose a simple prompt recursively using LLMs to transform RE inputs to QA format . they propose qq prompting and qt prompting to improve their results . |
| Outcome: | The proposed method improves on different model sizes, benchmarks and settings. |
Extract, Define, Canonicalize: An LLM-based Framework for Knowledge Graph Construction (2024.emnlp-main)
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| Challenge: | Existing methods for knowledge graph creation (KGC) are limited in their ability to scale up to text common in many real-world applications. |
| Approach: | They propose a framework for knowledge graph creation from input text using a pre-defined schema and a trained component that retrieves schema elements relevant to the input text. |
| Outcome: | The proposed framework extract-define-canonicalize extracts high-quality triplets with a succinct self-generated schema without any parameter tuning and with significantly larger schemas compared to prior works. |
Self-Ensemble: Mitigating Confidence Distortion for Large Language Models (2025.findings-emnlp)
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| Challenge: | Large Language Models exhibit a confidence distortion problem on multichoice question-answering . Self-Ensemble solves this problem by splitting the choices into several groups . |
| Approach: | They propose a method that splits LLM choices into several groups and ensembles them to reach a final decision. |
| Outcome: | The proposed method outperforms standard inference and baseline methods on MCQA. |
Debate as Optimization: Adaptive Conformal Prediction and Diverse Retrieval for Event Extraction (2024.findings-emnlp)
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| Challenge: | Experimental results show a significant performance gap between tuning-based approaches and event extraction approaches. |
| Approach: | They propose a debate as optimization system where the primary objective is to iteratively refine the large language models outputs through debating without parameter tuning. |
| Outcome: | The proposed system reduces performance gap between supervised approaches and tuning-free methods by 18.1% and 17.8% on ACE05 and 17.9% and 15.2% on CASIE respectively. |
Incomplete Prompt Jailbreaks in Large Language Models (2026.findings-acl)
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| Challenge: | Large language models (LLMs) are increasingly released as open-weight models with safeguards against harmful requests. |
| Approach: | They formalize incomplete prompt jailbreaks as incomplete prompts elicit harmful continuations . they identify two functional neurons that delay refusal until sentence termination . |
| Outcome: | The proposed model fails to generalize across content domains and attractor types . the proposed model can be used to perform more precise and robust IPJ defenses . |
CompassLLM: A Multi-Agent Approach toward Geo-Spatial Reasoning for Popular Path Query (2026.findings-acl)
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| Challenge: | Existing algorithms and machine learning methods require model training, parameter tuning, and retraining when accommodating data updates. |
| Approach: | They propose a multi-agent framework that leverages the reasoning capabilities of Large Language Models into the geo-spatial domain to solve the popular path query. |
| Outcome: | Experiments on real and synthetic datasets show that CompassLLM performs better than existing models while being cost-effective. |
POLARIS: A Gödel Agent Framework for Small Language Models through Experience-Abstracted Policy Repair (2026.findings-acl)
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| Challenge: | Gödel agent Polaris makes policy level changes with small, auditable patches that persist in the policy and are reused on unseen instances within each benchmark. |
| Approach: | They propose a Gödel agent that performs policy repair via experience abstraction . Polaris makes policy level changes with small, auditable patches that persist in the policy . |
| Outcome: | The proposed agent improves on MGSM, DROP, GPQA, and LitBench models over the base policy and competitive baselines. |