Papers with LLM predictions
Leveraging Product Catalog Patterns for Multilingual E-commerce Product Attribute Prediction (2025.emnlp-industry)
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| Challenge: | E-commerce stores increasingly use Large Language Models to improve catalog data quality . a critical challenge is accurately predicting missing structured attribute values . |
| Approach: | They propose a retrieval-augmented system that leverages existing product catalog entries to guide LLM predictions for missing attributes. |
| Outcome: | The proposed system improves catalog data quality by 34% and accuracy by 0.8% . the proposed model can predict missing attributes in multilingual product catalogs . |
What is a protest anyway? Codebook conceptualization is still a first-order concern in LLM-era classification (2026.acl-long)
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| Challenge: | generative large language models (LLMs) are used extensively for text classification in computational social science . conceptualization of categories to classify and using LLM predictions can tempt analysts to skip conceptualization altogether. |
| Approach: | They argue that LLMs can tempt analysts to skip conceptualization altogether . they argue that conceptualization failures induce downstream inferential bias . |
| Outcome: | The proposed model can tempt analysts to skip conceptualization altogether . the proposed model is a first-order concern in the LLM-era . |
What Evidence Do Language Models Find Convincing? (2024.acl-long)
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| Challenge: | Current retrieval-augmented language models are tasked with subjective, contentious, and conflicting queries. |
| Approach: | They construct a dataset that pairs controversial queries with real-world evidence documents . they find current models rely heavily on relevance of a website to the query . |
| Outcome: | The proposed dataset pairs controversial queries with real-world evidence documents that contain different facts, arguments, and answers. |
Dynamic Uncertainty Ranking: Enhancing Retrieval-Augmented In-Context Learning for Long-Tail Knowledge in LLMs (2025.naacl-long)
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| Challenge: | Prior work has shown that in-context learning (ICL) with retriever augmentation can help LLMs better capture long-tail knowledge, reducing their reliance on pre-trained data. |
| Approach: | They propose a reinforcement learning-based dynamic uncertainty ranking method that accounts for the varying impact of each retrieved sample on LLM predictions. |
| Outcome: | The proposed method outperforms baseline models on question-answering datasets by 2.76% and 5.96% on long-tail questions that elude zero-shot inference. |
Quantifying the Persona Effect in LLM Simulations (2024.acl-long)
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| Challenge: | Large language models (LLMs) have shown remarkable promise in simulating human language and behavior. |
| Approach: | They investigate how integrating persona variables—demographic, social, and behavioral factors—impacts LLMs’ ability to simulate diverse perspectives. |
| Outcome: | The proposed model improves on a zero-shot model with persona prompting. |
Can LLM be a Personalized Judge? (2024.findings-emnlp)
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| Challenge: | a new study examines the reliability of large language models (LLMs) for personalization and role-playing evaluation without examining its validity. |
| Approach: | They investigate the reliability of LLM-as-a-Personalized-Judge for personalization . they find that personas provided to LLMs have limited predictive power . |
| Outcome: | The proposed model is less reliable than previously thought, the authors show . human annotation reveals that third-person crowd worker evaluations of personalized preferences are even worse than LLM predictions. |
Generative Multimodal Entity Linking (2024.lrec-main)
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| Challenge: | Existing Entity Linking methods focus on designing complex multimodal interaction mechanisms and require fine-tuning all model parameters. |
| Approach: | They propose a framework for multimodal entity linking based on Large Language Models (LLMs) that trains a feature mapper to enable cross-modal interactions. |
| Outcome: | The proposed framework achieves state-of-the-art on two well-established datasets with a performance gain of 7.7% on WikiDiverse and 8.8% on Wikileaks. |
LLM-Rubric: A Multidimensional, Calibrated Approach to Automated Evaluation of Natural Language Texts (2024.acl-long)
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| Challenge: | Existing frameworks for the automated evaluation of natural language texts are based on a large language model (LLM) that fails to agree with human judges and is not fully validated by the human judges. |
| Approach: | They propose a large language model (LLM) that generates a distribution over potential responses to assess multiple dimensions of interest. |
| Outcome: | The proposed framework predicts human judges' assessment of user satisfaction on a scale of 1–4 with an RMS error 0.5, a 2 improvement over the uncalibrated baseline. |
Residualized Similarity for Faithfully Explainable Authorship Verification (2025.findings-emnlp)
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Peter Zeng, Pegah Alipoormolabashi, Jihu Mun, Gourab Dey, Nikita Soni, Niranjan Balasubramanian, Owen Rambow, H. Schwartz
| Challenge: | Neural methods achieve high accuracy, but their representations lack direct interpretability. |
| Approach: | They propose a method that supplements systems using interpretable features with a neural network to improve their performance while maintaining interpretability. |
| Outcome: | The proposed method improves the performance of state-of-the-art models while maintaining interpretability. |
Survival of the Most Influential Prompts: Efficient Black-Box Prompt Search via Clustering and Pruning (2023.findings-emnlp)
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| Challenge: | Prompt-based learning has been an effective paradigm for large pretrained language models (LLMs), enabling few-shot or even zero-shot learning. |
| Approach: | They propose a black-box prompt search method that clusters and prunes the search space to focus exclusively on influential prompt tokens. |
| Outcome: | The proposed method achieves state-of-the-art performance across tasks and LLMs while significantly reducing search costs. |
Language Model Fine-Tuning on Scaled Survey Data for Predicting Distributions of Public Opinions (2025.acl-long)
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| Challenge: | Prior studies have failed to accurately predict distribution of survey responses from human subjects. |
| Approach: | They propose to fine-tune large language models to predict human response distributions by leveraging unique structural characteristics of survey data. |
| Outcome: | The proposed model can capture group-specific variability in public opinions, generalizing to unseen subpopulations, survey waves and question topics, and different survey families. |