Papers with LLM.We
Can AI Relate: Testing Large Language Model Response for Mental Health Support (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) are already being piloted for clinical use in hospitals . recent failures of the Tessa chatbot have led to doubts about their reliability in high-stakes settings. |
| Approach: | They propose safety guidelines for the potential deployment of large language models for mental health response. |
| Outcome: | The proposed framework measures equity in empathy and adherence of LLM responses to motivational interviewing theory. |
Unsupervised Text Style Transfer for Controllable Intensity (2026.findings-eacl)
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| Challenge: | Unsupervised Text Style Transfer (UTST) aims to transfer the stylistic properties of a given text without parallel text pairs. |
| Approach: | They propose a SFT-then-PPO paradigm to fine-tune an LLM with parallel data and reward functions for distinguishing stylistic intensity in hierarchical levels. |
| Outcome: | The proposed system can transfer stylistic properties without parallel text pairs even for adjacent levels of intensity. |
Ask an Expert: Leveraging Language Models to Improve Strategic Reasoning in Goal-Oriented Dialogue Models (2023.findings-acl)
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| Challenge: | Existing dialogue models may encounter scenarios which are not well-represented in the training data and produce unnatural, inappropriate, or unhelpful responses. |
| Approach: | They propose a framework in which a model is trained with access to an "expert" they propose to optimize the model to selectively utilize (or ignore) advice given context and dialogue history. |
| Outcome: | The proposed framework improves quality across all expert sizes and with fewer parameters than the dialogue model itself. |