| Challenge: | Large language models excel at surface-level fluency but struggle with consistent logical inference beyond surface- level patterns. |
| Approach: | They propose to shift focus from surface-level generation to assumption-aware deeper inferences . authors argue that underspecification leads to unintentional assumptions . |
| Outcome: | The proposed model underspecification leads to unintentional assumptions, the authors argue . they also examine how to improve reasoning to enable deeper inferences, focusing on code generation and qualitative reasoning. |
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| Challenge: | Using large pre-trained language models, it is essential to research their reliability . if a human does not know the answer to a question, the socially acceptable behavior is to say 'I do not know' failing to fulfill this expectation can lead to distrust, or spread of misinformation. |
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| Challenge: | Existing studies have exploited the duality of the task pairs in machine translation and speech recognition. |
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Explaining Language Model Predictions with High-Impact Concepts (2024.findings-eacl)
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| Challenge: | Existing methods to explain large language models (LLMs) are mostly correlational and lack causal features due to compositional nature of languages. |
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Knowledge-Enriched Natural Language Generation (2021.emnlp-tutorials)
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Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language? (2020.findings-emnlp)
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| Challenge: | Existing models that generate NL explanations for tasks have been evaluated on the basis of surface-level similarities to human explanations, both through automatic metrics like BLEU and human evaluations. |
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| Challenge: | Current models learn from annotation artefacts and dataset biases, but it is unclear to what extent they are learning the task of NLI. |
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| Challenge: | This tutorial discusses cutting-edge research on NLI, including recent advance on dataset development, cutting- edge deep learning models, and highlights from recent research on using NLI to understand capabilities and limits of deep learning for language understanding and reasoning. |
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Lost in Inference: Rediscovering the Role of Natural Language Inference for Large Language Models (2025.naacl-long)
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An LLM-Based Approach for Insight Generation in Data Analysis (2025.naacl-long)
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| Challenge: | Existing approaches to generate insightful data from databases are time-consuming and resource-intensive. |
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