Papers with sampling strategies
Forum 4.0: An Open-Source User Comment Analysis Framework (2021.eacl-demos)
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Marlo Haering, Jakob Smedegaard Andersen, Chris Biemann, Wiebke Loosen, Benjamin Milde, Tim Pietz, Christian Stöcker, Gregor Wiedemann, Olaf Zukunft, Walid Maalej
| Challenge: | Using Forum 4.0, we analyze, aggregate, and visualize user comments based on labels defined by domain experts. |
| Approach: | They introduce an open-source framework to semi-automatically analyze, aggregate, and visualize user comments based on labels defined by domain experts. |
| Outcome: | The proposed framework can analyze, aggregate, and visualize user comments based on labels defined by domain experts. |
Error Classification of Large Language Models on Math Word Problems: A Dynamically Adaptive Framework (2025.findings-emnlp)
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| Challenge: | Current error classification methods rely on static and predefined categories to capture error patterns. |
| Approach: | They propose a framework for automated dynamic error classification in mathematical reasoning that incorporates common error patterns as explicit guidance. |
| Outcome: | The proposed framework reduces human bias and fine-grained analysis of error patterns. |
Back-Translation Sampling by Targeting Difficult Words in Neural Machine Translation (D18-1)
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| Challenge: | Neural machine translation (NMT) uses a sequence-to-sequence model to generate synthetic data. |
| Approach: | They propose a method that adds synthetic data to sentences with high prediction loss during training and a variety of sampling strategies targeting difficult-to-predict words. |
| Outcome: | The proposed method improves translation quality by up to 1.7 and 1.2 Bleu points over back-translation using random sampling for German-English and English-German, respectively. |
How to Fine-Tune Safely on a Budget: Model Adaptation Using Minimal Resources (2025.emnlp-industry)
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Anh C. Pham, Mihir Thalanki, Michael Sun, Aditya Chaloo, Ankita Gupta, Tian Xia, Aditya Mate, Ehi Nosakhare, Soundararajan Srinivasan
| Challenge: | Existing methods for fine-tuning safety examples are underdeveloped. |
| Approach: | They hypothesize that the effectiveness of a safety example is governed by its instruction-response behavior and its semantic diversity across harm categories. |
| Outcome: | The proposed method reduces harmfulness by up to 41% while adding only 0.05% more data to the fine-tuning set. |
Looking at the Unseen: Effective Sampling of Non-Related Propositions for Argument Mining (2025.coling-main)
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| Challenge: | Argument mining is the task of automatically identifying argumentative structures in natural language documents. |
| Approach: | They propose to use context and semantic similarity to sample non-related propositions . argument mining is the task of automatically identifying argumentative structures in natural language documents . |
| Outcome: | The proposed sampling strategies improve the performance of argument mining tasks. |
Salient Span Masking for Temporal Understanding (2023.eacl-main)
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| Challenge: | Salient Span Masking (SSM) has shown to be effective for closed-book question answering . authors of this study found that SSM alone improves performance on temporal tasks . |
| Approach: | They introduce Temporal Span Masking (TSM) to improve performance on temporal tasks . they find that SSM alone improves the downstream performance by +5.8 points . |
| Outcome: | The proposed approach improves performance on three temporal tasks by +5.8 points . the additional targeted spans achieved by adding the TSM task are the best . |
Evaluation Dataset and Methodology for Extracting Application-Specific Taxonomies from the Wikipedia Knowledge Graph (2020.lrec-1)
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| Challenge: | Recent efforts to extract hierarchical relations from unstructured text have been challenging. |
| Approach: | They propose an iterative method to extract an application-specific gold standard dataset from a Wikipedia knowledge graph and an evaluation framework to assess the quality of noisy automatically extracted taxonomies. |
| Outcome: | The proposed method reduces manual work and provides a first gold standard dataset and evaluation framework. |
Meta-Learning for Effective Multi-task and Multilingual Modelling (2021.eacl-main)
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| Challenge: | Existing studies on multitask and multilingual learning have shown that learning cross-lingual embeddings can benefit multiple tasks and languages. |
| Approach: | They propose a meta-learning approach to learn interactions between tasks and languages . they also investigate the role of different sampling strategies used during meta-learned model . |
| Outcome: | The proposed model improves on five different tasks and six different languages from the XTREME multilingual benchmark dataset. |
Large-Scale and Multi-Perspective Opinion Summarization with Diverse Review Subsets (2023.findings-emnlp)
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| Challenge: | Existing methods for opinion summarization are deficient in epitomizing extensive reviews and offering opinion summaries from various angles. |
| Approach: | They propose a supervised opinion summarization framework that takes sentiment orientation into account and trains the summarizer to learn from sub-optimal and optimal review subsets. |
| Outcome: | The proposed framework generates pros, cons, and verdict summaries from hundreds of input reviews. |
Iterative Constrained Back-Translation for Unsupervised Domain Adaptation of Machine Translation (2022.coling-1)
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| Challenge: | Existing back-translation methods focus on in-domain lexical knowledge, which may lead to poor translation of unseen in- domain words. |
| Approach: | They propose an iterative constrained back-translation method to incorporate in-domain lexical knowledge into synthetic parallel data from BT. |
| Outcome: | The proposed method improves the BLEU score by up to 3.08 on four domains. |
Modeling Noise in Paraphrase Detection (2022.lrec-1)
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| Challenge: | Noisy labels in training data are challenging and can lead to incorrect decisions . large pre-trained language models have achieved great results in many NLP tasks . |
| Approach: | They propose to use a linear noise model to augment pre-trained language models to account for label noise in fine-tuning. |
| Outcome: | The proposed model can be applied without further knowledge about annotation quality and label confidence of training examples and their results are compared with other models. |
Eeny, meeny, miny, moe. How to choose data for morphological inflection. (2022.emnlp-main)
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| Challenge: | Data scarcity is a major bottleneck for many natural language processing tasks . active learning aims to reduce the cost of data annotation by selecting the most informative examples to label. |
| Approach: | They propose to use oracle experiments to select data that is most informative for the model. |
| Outcome: | The proposed sampling strategies show that they improve on the oracle experiment and the 10-cycle iteration using Natügu as a case study. |
Frequency Balanced Datasets Lead to Better Language Models (2023.findings-emnlp)
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| Challenge: | Existing evidence that high-frequency tokens in pretraining data might bias learning, causing undesired effects, is not clear. |
| Approach: | They propose a sampling algorithm that iteratively assesses token frequencies and removes sentences that contain still high-frequency tokens, resulting in a balanced dataset. |
| Outcome: | The proposed method reduces the amount of pre-training data required for training attention-based transformer language models by up to three times. |
RRNorm: A Novel Framework for Chinese Disease Diagnoses Normalization via LLM-Driven Terminology Component Recognition and Reconstruction (2024.findings-acl)
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| Challenge: | Clinical Terminology Normalization (CTN) aims at finding standard terms from a given termbase for mentions extracted from clinical texts. |
| Approach: | They propose a method that leverages reasoning capability of large language models to recognize components of terms and automate decomposition. |
| Outcome: | The proposed strategy achieves state-of-the-art on the experimental dataset. |
Enough Is Enough! a Case Study on the Effect of Data Size for Evaluation Using Universal Dependencies (2024.lrec-main)
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| Challenge: | a recent study suggests that large datasets require large amounts of simulated data and scores to perform model evaluation. |
| Approach: | They propose to use dependency parsing to optimize annotation efforts by using a sample size of 5,000 tokens for in-language in-domain datasets. |
| Outcome: | The proposed method can be applied to other tasks, including classification and translation tasks. |
Language of Thought Shapes Output Diversity in Large Language Models (2026.acl-long)
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| Challenge: | Using multiple thinking languages, we show that controlling the language used during model thinking provides a novel and structural source of output diversity. |
| Approach: | They propose to control the language used during model thinking to provide a novel source of output diversity. |
| Outcome: | The proposed methods show that controlling the language used during model thinking provides a novel and structural source of output diversity. |
PQR: Improving Dense Retrieval via Potential Query Modeling (2025.acl-long)
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| Challenge: | Existing training data is sparse, with each document associated with one or a few labeled queries. |
| Approach: | They propose a training-free potential query retrieval framework to address this problem . they use a Gaussian mixture distribution to model all potential queries for a document . |
| Outcome: | The proposed method is able to capture comprehensive semantic information from a document with multiple queries. |
RAID: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectors (2024.acl-long)
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Liam Dugan, Alyssa Hwang, Filip Trhlík, Andrew Zhu, Josh Magnus Ludan, Hainiu Xu, Daphne Ippolito, Chris Callison-Burch
| Challenge: | Existing methods for detecting machine-generated text are often insufficiently robust and lack benchmark datasets. |
| Approach: | They evaluate the out-of-domain and adversarial robustness of 8 open- and 4 closed-source detectors using RAID benchmark datasets. |
| Outcome: | The proposed detectors are fooled by adversarial attacks, repetition penalties, and unseen generative models. |
DB-Explore: Automated Database Exploration and Instruction Synthesis for Text-to-SQL (2025.findings-emnlp)
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| Challenge: | Recent text-to-SQL systems that use large language models struggle with complex database structures and domain-specific queries. |
| Approach: | a framework that aligns large language models with database knowledge is proposed . DB-Explore constructs database graphs to capture complex relational schemas . |
| Outcome: | a new framework outperforms existing text-to-SQL systems by outperforming existing systems. |
Linguistic and Embedding-Based Profiling of Texts Generated by Humans and Large Language Models (2025.emnlp-main)
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| Challenge: | Recent studies have focused on using LLMs to classify text as either human-written or machine-generated . |
| Approach: | They characterize human-written and machine-generated texts using a set of linguistic features across different linguistic levels such as morphology, syntax, and semantics. |
| Outcome: | The proposed model reveals that human-written texts exhibit simpler syntactic structures and more diverse semantic content. |
Parallel Test-Time Scaling for Latent Reasoning Models (2026.acl-long)
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| Challenge: | Parallel test-time scaling is a pivotal approach for enhancing large language models. |
| Approach: | They propose two uncertainty-inspired stochastic strategies for parallel test-time scaling for latent reasoning models and a Latent Reward Model for aggregation. |
| Outcome: | The proposed model scales well with compute and enables effective trajectory selection. |