Papers with estimation
Intuitive Fine-Tuning: Towards Simplifying Alignment into a Single Process (2025.acl-long)
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| Challenge: | Supervised Fine-Tuning (SFT) and Preference Optimization (PO) are key processes for aligning Language Models with human preferences post pre-training. |
| Approach: | They propose to combine Supervised Fine-Tuning and Preference Optimization (PO) with two sub-processes defined at token level within the Markov Decision Process (MDP) |
| Outcome: | The proposed process performs comparably or even superiorly to SFT and some typical PO methods across several tasks, particularly those requires generation, reasoning, and fact-following abilities. |
Predicting Multidimensional Subjective Ratings of Children’ Readings from the Speech Signals for the Automatic Assessment of Fluency (2020.lrec-1)
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| Challenge: | Using a novel framework, we estimate the reading performance of young readers using linguistic and phonetic features. |
| Approach: | They propose a framework for performing such an estimation that exploits multiple references performed by adults and demonstrate its efficiency using recordings of 273 pupils. |
| Outcome: | The proposed framework exploits multiple references performed by adults and shows that it is efficient. |
Conceptual structure coheres in human cognition but not in large language models (2023.emnlp-main)
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| Challenge: | Neural network models of language have long been used to develop hypotheses about conceptual representation in the mind and brain. |
| Approach: | They propose to use three techniques borrowed from cognitive psychology to estimate lexical-semantic structure in humans and a large language model. |
| Outcome: | The proposed models show that human-like models can estimate lexical-semantic structure robustly to cultural, language, and method of estimation. |
Improving Translation Quality Estimation with Bias Mitigation (2023.acl-long)
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| Challenge: | State-of-the-art translation Quality Estimation models are biased, relying on monolingual features while ignoring the bilingual semantic alignment. |
| Approach: | They propose a method to mitigate the bias of translation quality estimation models by contrastive learning between clean and noisy sentence pairs. |
| Outcome: | The proposed method improves the estimation performance while mitigating the bias. |
Comparison of Conventional Hybrid and CTC/Attention Decoders for Continuous Visual Speech Recognition (2024.lrec-main)
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| Challenge: | Recent advances have been achieved in Visual Speech Recognition (VSR) despite the lack of data, there is no clear comparison between different types of decoders for certain languages and tasks. |
| Approach: | They focused on how the conventional DNN-HMM decoder behaves depending on the amount of data used for their estimation. |
| Outcome: | The proposed model improves the CTC/Attention model in data-scarcity scenarios while requiring less training time and fewer parameters. |
RECAL: Sample-Relation Guided Confidence Calibration over Tabular Data (2023.findings-emnlp)
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| Challenge: | Various machine learning methods for tabular data lack accurate confidence estimation, which is needed for high-risk sensitive applications such as credit modeling and financial fraud detection. |
| Approach: | They propose a general post-training confidence calibration framework to calibrate the confidence of current machine learning models by employing graph neural networks to model the relationships between different samples. |
| Outcome: | The proposed framework improves the confidence estimation on tabular datasets by using graph neural networks to model the relationships between different samples. |
Valid Survey Simulations with Limited Human Data: The Roles of Prompting, Fine-Tuning, and Rectification (2026.acl-long)
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| Challenge: | Large language models (LLMs) are a cost-effective and time-consuming way to capture public opinion and behavior, but their outputs are often biased and yield invalid estimates. |
| Approach: | They propose to use large language models to generate survey responses and rectification methods that debias population estimates to find out how human responses are best allocated between them. |
| Outcome: | The proposed methods reduce bias below 5% and increase sample size by up to 14% under a fixed budget. |
VCDM: Leveraging Variational Bi-encoding and Deep Contextualized Word Representations for Improved Definition Modeling (2020.emnlp-main)
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| Challenge: | Existing approaches for definition modeling combine distributional and lexical semantics in an implicit rather than direct way. |
| Approach: | They propose a model that introduces a continuous latent variable to model the relationship between a phrase and its definition. |
| Outcome: | The proposed model achieves state-of-the-art performance on four challenging benchmarks and the first non-English corpus. |
Scaling Law for Multimodal Large Language Model Supervised Fine-Tuning (2026.acl-long)
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YiFan Zhang, Tao Yu, Feng Li, Chaoyou Fu, Yibo Hu, Kun Wang, Qingsong Wen, Zhang Zhang, Liang Wang, Rong Jin
| Challenge: | supervised fine-tuning (SFT) is crucial for multimodal large language models, yet a comprehensive scaling law is lacking . et al.: scaling laws focus on model size, pre-training tokens, and MLLM SFT data volumes . |
| Approach: | They propose two scaling laws to guide optimal model-data configuration . they propose one applicable when training data volumes are well defined by researchers . |
| Outcome: | The proposed scaling laws provide valuable recommendations for optimal resource allocation . they show that the proposed laws are more accurate than existing models . |
Estimating Large Language Model Capabilities without Labeled Test Data (2023.findings-emnlp)
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| Challenge: | Large Language Models have shown impressive ability to perform in-context learning from only a few examples, but their accuracy varies widely from task to task. |
| Approach: | They propose a method that trains a meta-model using LLM confidence scores as features to perform ICL accuracy estimation. |
| Outcome: | The proposed method improves over baselines across 7 out of 12 settings and achieves the same accuracy as evaluating on 40 sampled examples per task. |
When Instructions Multiply: Measuring and Estimating LLM Capabilities of Multiple Instructions Following (2025.findings-emnlp)
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Keno Harada, Yudai Yamazaki, Masachika Taniguchi, Edison Marrese-Taylor, Takeshi Kojima, Yusuke Iwasawa, Yutaka Matsuo
| Challenge: | a large number of languages are increasingly used to evaluate their ability to follow multiple instructions simultaneously. |
| Approach: | They propose two benchmarks to evaluate LLMs' ability to follow multiple instructions simultaneously . they use many instruction-following eval and style-aware Mostly Basic programming problems . |
| Outcome: | The proposed models predict performance on unseen instruction combinations and not used during training with 10% error. |
Agent-based Substructure Counting under Local Differential Privacy (2026.acl-long)
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| Challenge: | Recent studies have demonstrated the ability of Large Language Models (LLMs) to process graph problems. |
| Approach: | They propose to decompose substructure counting into node-level tasks distributed among node agents and embed the knowledge of distributed algorithms and DP frameworks in the curator agent and privacy controller. |
| Outcome: | Extensive experiments on 6 real-world datasets validate the effectiveness of the proposed framework for substructure counting tasks under edge local differential privacy (LDP). |
SCOPE: Compress Mathematical Reasoning Steps for Efficient Automated Process Annotation (2025.findings-acl)
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| Challenge: | Existing process annotation approaches are computationally expensive. |
| Approach: | They propose a compression-based approach that transforms reasoning steps into code and normalizes them through Abstract Syntax Tree. |
| Outcome: | The proposed method outperforms existing methods on Best-of-N strategy and ProcessBench. |
The Sparse Frontier: Sparse Attention Trade-offs in Transformer LLMs (2026.findings-acl)
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| Challenge: | Sparse attention is a promising strategy to extend long-context capabilities in LLMs . but its efficiency–accuracy trade-offs remain unclear due to the lack of comprehensive evaluation . |
| Approach: | They evaluate sparse attention methods across multiple model families and sizes . they find larger sparser models outperform smaller dense ones at equivalent cost . |
| Outcome: | The proposed methods outperform smaller sparse models at equivalent cost and improve the Pareto frontier. |
Seer Self-Consistency: Advance Budget Estimation for Adaptive Test-Time Scaling (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated exceptional performance in reasoning tasks, particularly in mathematics. |
| Approach: | They propose a dynamic self-consistency framework that integrates System 1 and System 2 reasoning to improve token efficiency and latency. |
| Outcome: | The proposed method outperforms existing methods, achieving up to 47% reduction in token consumption and 43% reduction in inference latency without significant performance loss. |