Evaluating Large Language Models for Belief Inference: Mapping Belief Networks at Scale (2025.findings-emnlp)
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| Challenge: | Beliefs are interconnected, influencing how people process and update what they think. |
| Approach: | They propose to use a finetuned GPT-4o model to infer belief structures from large-scale social media data. |
| Outcome: | The proposed model can recover belief structures from large social media data, allowing for a level of scalability and efficiency that is impossible using traditional survey methods. |
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| Challenge: | Existing methods to analyze language data for psychological causality are difficult to advance as they do not isolate cognitive mechanisms. |
| Approach: | They propose a pipeline that leverages a Large Language Model to identify causal claims made in natural language documents and applies a clustering algorithm to group causal claims based on their semantic topics. |
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GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation (2021.findings-emnlp)
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| Challenge: | Recent studies report that prompt-based direct classification eliminates the need for fine-tuning but lacks data and inference scalability. |
| Approach: | They propose a data augmentation technique that leverages large-scale language models to generate real text samples from a mixture of real samples. |
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KG-GPT: A General Framework for Reasoning on Knowledge Graphs Using Large Language Models (2023.findings-emnlp)
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| Challenge: | Using large language models for complex reasoning tasks on knowledge graphs remains unexplored. |
| Approach: | They propose a multi-purpose framework leveraging large language models for complex reasoning tasks on knowledge graphs. |
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Towards Reliable Misinformation Mitigation: Generalization, Uncertainty, and GPT-4 (2023.emnlp-main)
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Kellin Pelrine, Anne Imouza, Camille Thibault, Meilina Reksoprodjo, Caleb Gupta, Joel Christoph, Jean-François Godbout, Reihaneh Rabbany
| Challenge: | Misinformation is a critical societal challenge, and current approaches have yet to produce an effective solution. |
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Evaluating the Knowledge Base Completion Potential of GPT (2023.findings-emnlp)
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| Challenge: | Language models (LMs) have been proposed for unsupervised knowledge base completion (KBC) however, their ability to do this at scale and with high accuracy remains an open question. |
| Approach: | They propose to use language models to complete a large public KB, Wikidata, with 90% precision. |
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All Roads Lead to Rome: Graph-Based Confidence Estimation for Large Language Model Reasoning (2025.emnlp-main)
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| Challenge: | Existing methods for confidence estimation are primarily designed for factual QA tasks and fail to generalize to reasoning tasks. |
| Approach: | They propose a set of training-free, graph-based confidence estimation methods tailored to reasoning tasks that exploit graph properties such as centrality, path convergence, and path weighting. |
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Can Large Language Models Discern Evidence for Scientific Hypotheses? Case Studies in the Social Sciences (2024.lrec-main)
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| Challenge: | scholarly databases fail to aggregate, compare, contrast, and contextualize existing studies in service to a targeted research question. |
| Approach: | They propose to use large language models to discern evidence in support or refute of specific hypotheses based on abstracts. |
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Large Language Models for Psycholinguistic Plausibility Pretesting (2024.findings-eacl)
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| Challenge: | Psycholinguists typically use language models to create controlled materials . plausibility judgments are often based on coarse-grained judgements, but fine-grounded ones do not . |
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Evaluating Large Language Models for Health-related Queries with Presuppositions (2024.findings-acl)
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| Challenge: | a large number of health-related queries require factually accurate answers . however, the lack of accurate answers may cause real-world harm . |
| Approach: | They evaluate the factual accuracy and consistency of large language models using a dataset consisting of health-related queries with varying degrees of presuppositions. |
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A Survey of Confidence Estimation and Calibration in Large Language Models (2024.naacl-long)
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| Challenge: | Large language models (LLMs) have demonstrated impressive capabilities across a wide range of tasks in various domains, but they can be unreliable due to factual errors in their generations. |
| Approach: | They summarize recent advances in LLM confidence estimation and calibration and outline their main lessons learned. |
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