Papers with contrastive reasoning
The Pragmatic Mind of Machines: Tracing the Emergence of Pragmatic Competence in Large Language Models (2026.eacl-long)
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| Challenge: | Current large language models (LLMs) have demonstrated emerging capabilities in social intelligence tasks, including implicature resolution and theory-of-mind reasoning. |
| Approach: | They introduce a dataset grounded in the pragmatic concept of alternatives to evaluate whether large language models can accurately infer nuanced speaker intentions. |
| Outcome: | The proposed model can infer nuanced speaker intentions by inferring the speaker’s intended meaning and explaining when and why a speaker would choose one utterance over its alternative. |
Contrastive Explanations for Model Interpretability (2021.emnlp-main)
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| Challenge: | Existing methods for producing model explanations seek all causal factors at once, making them difficult to comprehend. |
| Approach: | They propose a method to produce contrastive explanations in the latent space . they use attribution and token/span attribution to produce models that consider only contrastive reasoning . |
| Outcome: | The proposed method allows model behavior to consider only contrastive reasoning . it also uncovers which aspects of the input are useful for and against particular decisions . |
Empowering Psychotherapy with Large Language Models: Cognitive Distortion Detection through Diagnosis of Thought Prompting (2023.findings-emnlp)
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| Challenge: | Existing systems for mental health support are shallow and heuristic, e.g., analyzing emotions and generating comforting responses. |
| Approach: | They propose to use cognitive distortion detection to perform diagnosis on the patient’s speech via three stages: subjectivity assessment to separate the facts and the thoughts; contrastive reasoning to elicit the reasoning processes supporting and contradicting the thoughts and schema analysis to summarize the cognition schemas. |
| Outcome: | The proposed system improves on ChatGPT for cognitive distortion detection while generating high-quality rationales approved by human experts. |
Turning Logic Against Itself: Probing Model Defenses Through Contrastive Questions (2025.emnlp-main)
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| Challenge: | Existing safety measures detect overt malicious intent but fail to address subtle, reasoning-driven vulnerabilities. |
| Approach: | They propose a two-phase jailbreak technique that exploits contrastive reasoning to bypass safety mechanisms in large language models. |
| Outcome: | The proposed techniques achieve higher attack success rates (44%) than existing methods. |