Papers with predictive performance
Through the Lens of Split Vote: Exploring Disagreement, Difficulty and Calibration in Legal Case Outcome Classification (2024.acl-long)
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| Challenge: | Existing methods for NLP calibration ignore inherent human label variation (HLV) split votes are a problem in high-stakes domains such as legal and medical decisions . |
| Approach: | They present a case outcome classification dataset with judges' vote distributions and build a taxonomy of disagreement with SV-specific subcategories. |
| Outcome: | The proposed model is compared against a judge vote distribution and assesses the alignment of perceived difficulty between models and humans. |
A Neighborhood Framework for Resource-Lean Content Flagging (2022.tacl-1)
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Sheikh Muhammad Sarwar, Dimitrina Zlatkova, Momchil Hardalov, Yoan Dinkov, Isabelle Augenstein, Preslav Nakov
| Challenge: | Existing approaches to cross-lingual content flagging with limited target language data are lacking in many languages. |
| Approach: | They propose a framework for cross-lingual content flagging with limited target- language data based on a nearest-neighbor architecture and a transformer representation in all its components. |
| Outcome: | The proposed framework outperforms previous work in terms of predictive performance on eight languages from two different datasets. |
Topic Modeling in Embedding Spaces (2020.tacl-1)
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| Challenge: | Existing topic models fail to learn interpretable topics when working with large and heavy-tailed vocabularies. |
| Approach: | They propose an embedded topic model that integrates word embeddings with a categorical distribution that is the natural parameter between the word’s embeddment and an embeddement of its assigned topic. |
| Outcome: | The embedded topic model outperforms existing topic models in terms of topic quality and predictive performance. |
Towards Federated Low-Rank Adaptation of Language Models with Rank Heterogeneity (2025.naacl-short)
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| Challenge: | Low-rank adaptation (LoRA) is an efficient alternative to full-weight adaptation in federated fine-tuning of language models, significantly reducing computational costs. |
| Approach: | They propose a low-rank adaptation method that freezes original weights and trains only the update parametrized as a product of two low-ranked matrices. |
| Outcome: | The proposed method accelerates convergence and enhances the global model’s predictive performance. |
CaBSALLM: Efficient Context-Aware Batch Annotation of Conversational Streams with Large Language Models (2026.acl-short)
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| Challenge: | Large-scale annotations of subjective, discourse-dependent social interactions remain a critical bottleneck in computational social science. |
| Approach: | They propose a pipeline that incorporates lightweight conversational context and a dynamic batching method to improve throughput and scalability. |
| Outcome: | The proposed pipeline improves throughput and scalability while preserving interpretive depth essential to complex social annotations. |
The Impact of Inference Acceleration on Bias of LLMs (2025.naacl-long)
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| Challenge: | Recent work suggests strategies to increase inference efficiency with LLMs . however, these strategies may inadvertently lead to some side-effects. |
| Approach: | They propose to optimize inference acceleration strategies such as quantization, pruning, and caching to reduce inference cost and latency while maintaining predictive performance. |
| Outcome: | The proposed strategies reduce cost and latency while maintaining predictive performance while preserving the model size. |
Extracted BERT Model Leaks More Information than You Think! (2022.emnlp-main)
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| Challenge: | Existing pre-trained language models are vulnerable to model extraction attacks . model extraction can cause severe privacy leakage even when victim models are facilitated with state-of-the-art defensive strategies. |
| Approach: | They propose to launch an attribute-inference attack against an extracted BERT model to prevent privacy leakage. |
| Outcome: | The proposed attack can cause severe privacy leakage even when victim models are facilitated with state-of-the-art defensive strategies. |
Approximate Attributions for Off-the-Shelf Siamese Transformers (2024.eacl-long)
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| Challenge: | Existing attribution methods cannot tackle Siamese encoders since they compare two inputs rather than processing a single one. |
| Approach: | They propose an attribution method specifically targeted for Siamese encoders that can be adjusted and fine-tuned to retain original model's predictive performance. |
| Outcome: | The proposed method retains the original model's predictive performance and can be applied to off-the-shelf models. |
Characterizing the Value of Information in Medical Notes (2020.findings-emnlp)
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| Challenge: | Obtaining and analyzing information is critical for the diagnosis, prognosis, treatment, and prevention of disease. |
| Approach: | They propose a probing framework to select parts of notes that enable more accurate predictions than using all notes. |
| Outcome: | The proposed framework achieves better predictive performance with only 6.8% of all tokens for readmission prediction. |
Cost-Efficient Subjective Task Annotation and Modeling through Few-Shot Annotator Adaptation (2024.findings-emnlp)
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| Challenge: | In subjective tasks, the inclusion of diverse annotators is crucial as their unique perspectives significantly influence the annotations. |
| Approach: | They propose a framework that minimizes the annotation budget while maximizing the predictive performance for each annotator. |
| Outcome: | The proposed framework surpasses the previous SOTA in capturing the annotators’ individual perspectives with as little as 25% of the original annotation budget on two datasets. |
MCLE-Mol: Empowering LLM with Molecular Comprehension and Low-Cost Continual Evolution for Interpretable Property Prediction (2026.findings-acl)
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Zhili Pu, Lantian Zhang, Hao Duan, Zhixing Zhang, Keyun Zhu, Yongqi Fan, Ruihui Hou, Tong Ruan, Yun Tang
| Challenge: | Large language models (LLMs) offer a new paradigm for molecular property prediction (MPP), yet a semantic gap between natural language and molecul representations limits their ability to capture structure–activity relationships (SAR). |
| Approach: | They propose an ML–LLM–Rule collaborative framework for MPP that injects ML-derived substructure attribution values into LLMs and calibrates them under specific chemical contexts. |
| Outcome: | The proposed framework outperforms baseline models on multiple benchmark datasets and is highly interpretable. |
Attention is not Explanation (N19-1)
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| Challenge: | Attention mechanisms have seen wide adoption in neural NLP models. |
| Approach: | They perform extensive experiments to assess the degree to which attention weights provide meaningful "explanations" they find that attention weighted inputs are often uncorrelated with gradient-based measures of feature importance . |
| Outcome: | The proposed model is based on a distribution over attended-to input units . the findings show that attention weights are often uncorrelated with features . |
Strategic Demonstration Selection for Improved Fairness in LLM In-Context Learning (2024.emnlp-main)
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| Challenge: | Recent studies highlight the effectiveness of using in-context learning (ICL) to steer large language models in processing tabular data. |
| Approach: | They propose a method that uses clustering and evolutionary strategies to curate a representative sample set from training data. |
| Outcome: | The proposed method significantly improves fairness across various metrics, showing its efficacy in real-world scenarios. |
An Empirical Study on Explanations in Out-of-Domain Settings (2022.acl-long)
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| Challenge: | Recent work in Natural Language Processing has focused on extracting faithful explanations . yet, little is known about how post-hoc explanations perform in out-of-domain settings . |
| Approach: | They propose to use a random baseline to evaluate out-of-domain post-hoc explanation faithfulness . they suggest select-then-predict models demonstrate comparable predictive performance in out- of-domain settings to full-text trained models. |
| Outcome: | The proposed models perform better in out-of-domain settings than full-text models. |
HashFormers: Towards Vocabulary-independent Pre-trained Transformers (2022.emnlp-main)
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| Challenge: | Existing pre-trained language models are vocabulary-dependent, mapping by default each token to its corresponding embedding. |
| Approach: | They propose a family of vocabulary-independent pre-trained transformers that support unlimited vocabulary . they propose to map each token to its corresponding embedding by default . |
| Outcome: | The proposed models are more memory efficient than existing models while achieving comparable performance on multiple text classification tasks. |
Enjoy the Salience: Towards Better Transformer-based Faithful Explanations with Word Salience (2021.emnlp-main)
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| Challenge: | Pretrained transformer-based language models have demonstrated state-of-the-art predictive performance when adapted into a range of language understanding tasks. |
| Approach: | They propose to use salient information extracted a priori from training data to complement the task-specific information learned by the model during fine-tuning on a downstream task. |
| Outcome: | The proposed model can provide more faithful explanations across four different feature attribution methods compared to vanilla BERT. |
Learning Bill Similarity with Annotated and Augmented Corpora of Bills (2021.emnlp-main)
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| Challenge: | despite the importance of bill-to-bill linkages, existing approaches fail to address semantic similarities across bills. |
| Approach: | They propose a 5-class classification task that closely reflects the nature of the bill generation process. |
| Outcome: | The proposed method captures similarities across legal documents at various levels of aggregation. |
CIKT: A Collaborative and Iterative Knowledge Tracing Framework with Large Language Models (2025.emnlp-main)
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| Challenge: | Knowledge Tracing (KT) aims to model a student’s learning state over time and predict their future performance. |
| Approach: | They propose a framework that harnesses Large Language Models to enhance both prediction accuracy and explainability by a synergistic optimization loop. |
| Outcome: | The proposed framework improves both prediction accuracy and explainability by using a synergistic optimization loop. |
AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science (2025.findings-emnlp)
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An Luo, Xun Xian, Jin Du, Fangqiao Tian, Ganghua Wang, Ming Zhong, Shengchun Zhao, Xuan Bi, Zirui Liu, Jiawei Zhou, Jayanth Srinivasa, Ashish Kundu, Charles Fleming, Mingyi Hong, Jie Ding
| Challenge: | Large language models (LLMs) have advanced the automation of data science workflows, yet it remains unclear whether they can critically leverage external domain knowledge as human data scientists do in practice. |
| Approach: | They propose a benchmark to evaluate how large language models handle external domain knowledge in tabular prediction tasks. |
| Outcome: | The proposed model evaluates whether it can critically leverage external domain knowledge as human data scientists do in practice. |
ReMedi: Reasoner for Medical Clinical Prediction (2026.findings-acl)
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| Challenge: | Existing approaches to predicting future clinical outcomes from EHRs focus on enhancing medical knowledge through distillation or RAG while relying on the model’s internal ability to interpret contextual information. |
| Approach: | They propose a framework for improving clinical outcome prediction from EHR using a sample regeneration mechanism that leverages ground-truth answers as hints to enhance reasoning. |
| Outcome: | Experiments on multiple EHR prediction tasks show significant gains of up to 19.9% over state-of-the-art baselines in terms of F1 score, underscoring ReMedi’s effectiveness in real-world clinical prediction. |
AutoCT: Automating Interpretable Clinical Trial Prediction with LLM Agents (2025.emnlp-main)
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| Challenge: | Clinical trials are expensive and time-consuming, and accurate trial prediction is key to advancing medical treatments. |
| Approach: | They propose a framework that combines reasoning capabilities of large language models with the explainability of classical machine learning to generate, evaluate, and refine tabular features without human input. |
| Outcome: | The proposed framework performs better than SOTA methods on clinical trial prediction tasks within a limited number of iterations. |
Beyond Query Memorization: Large Language Model Routing with Query Decomposition and Historical Matching (2026.acl-long)
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Bo Lv, Jingbo Sun, Jianwei Lv, Chen Tang, Shaojie Zhang, Nayu Liu, Guoxin Yu, Zihao Li, Qichao Zhang, Dongbin Zhao, Ping Luo, Yue Yu
| Challenge: | Existing routing methods rely on direct mapping from queries to models based on surface-level features, leading to poor generalizability on out-of-distribution data. |
| Approach: | They propose a new routing framework that recasts the routing task as a matching process of sifting similar queries from historical logs. |
| Outcome: | The proposed framework improves matching accuracy while lowering inference costs . it decouples linguistic surface forms from task-intrinsic requirements . |