Papers with prompted LLMs
Analogical Structure, Minimal Contextual Cues and Contrastive Distractors: Input Design for Sample-Efficient Linguistic Rule Induction (2026.eacl-long)
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| Challenge: | Recent systems that use analogical reasoning require extensive knowledge engineering and even transformer-based models show inconsistent results across complexity levels. |
| Approach: | They propose to implement analogical structure, contrastive learning, and minimal contextual cue principles into large language models that train on English verb alternations. |
| Outcome: | The proposed models learn the alternation rules with high F1 on English verb alternations. |
Supervision versus Demonstration-Based In-Context Learning for Multiword Expression Classification (2026.acl-srw)
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| Challenge: | idiomatic light verb constructions (LVCs) are challenging for multiword expression processing . they share the same surface form as fully literal verb–object combinations . |
| Approach: | They frame Turkish LVC detection as a binary classification task . they compare a supervised Turkish encoder baseline to three instruction-tuned LLMs . |
| Outcome: | The proposed method improves Turkish LVC detection on a controlled set with matched negatives and positives. |
Bootstrapping Multilingual Semantic Parsers using Large Language Models (2023.eacl-main)
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| Challenge: | Despite cross-lingual generalization, translation models require significant amounts of labeled data for many low-resource languages . brittle translation services may be due to domain mismatch between input text and general-purpose text . |
| Approach: | They propose to use large language models to translate English datasets into several languages via few-shot prompting. |
| Outcome: | The proposed method outperforms a strong translation-train baseline on 41 out of 50 languages. |
From Scoring to Explanations: Evaluating SHAP and LLM Rationales for Rubric-based Teaching Quality Assessment (2026.findings-acl)
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Ivo Bueno, Babette Bühler, Philipp Stark, Tim Fütterer, Ulrich Trautwein, Dorottya Demszky, Heather Hill, Enkelejda Kasneci
| Challenge: | a framework for sentence-level interpretability of rubric-based scoring is proposed . aaron e. smith: automated scoring models provide little insight into why scores are produced . |
| Approach: | They propose a framework for sentence-level interpretability of rubric-based scoring that combines Shapley-value attributions with rationales generated by large language models. |
| Outcome: | The proposed framework compares fine-tuned pretrained language models with large language models . it shows that fine- tuned models outperform LLMs in prediction accuracy but exhibit label compression toward mid-scale scores . |
Intent-aware Schema Generation and Refinement for Literature Review Tables (2025.findings-emnlp)
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| Challenge: | ambiguity in reference-based evaluations and lack of editing/refinement methods have slow progress on schema generation. |
| Approach: | They propose a method for augmenting unannotated table corpora with synthesized intents . they propose prompted workflows and fine-tuned models to improve schema generation . |
| Outcome: | The proposed approach significantly improves baseline performance in reconstructing reference schemas. |
Measuring scalar constructs in social science with LLMs (2025.emnlp-main)
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Hauke Licht, Rupak Sarkar, Patrick Y. Wu, Pranav Goel, Niklas Stoehr, Elliott Ash, Alexander Miserlis Hoyle
| Challenge: | Valid scalar measurement of skalar constructs is a fundamental task in text analysis. |
| Approach: | They evaluate four approaches to measuring scalar constructs using large language models . pairwise comparisons produced better measurements than prompting LLMs, they say . validation of skalar measurement enables wide range of substantive applications in social science research . |
| Outcome: | The proposed methods improve on pairwise comparisons and finetuning . the proposed methods can be used in social science research . |