Papers with IP
Predicting Client Emotions and Therapist Interventions in Psychotherapy Dialogues (2024.eacl-long)
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| Challenge: | Recent studies have focused on the role of client emotions and therapist interventions in predicting treatment outcomes from psychotherapy dialogues. |
| Approach: | They propose to model the therapist-intervention-prediction-based dialogue acts at the utterance-level using a pan-theoretical schema and fine-tuned language models. |
| Outcome: | The proposed model predicts the coherence between client self-reports on emotion and utterance-level emotions. |
Information Parity: Measuring and Predicting the Multilingual Capabilities of Language Models (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) are increasingly used in user-facing applications worldwide, necessitating handling multiple languages across various tasks. |
| Approach: | They propose a metric called Information Parity (IP) that can predict an LLM’s capabilities across multiple languages in a task-agnostic manner. |
| Outcome: | The proposed metric can predict LLM’s capabilities across multiple languages in a task-agnostic manner. |
Interpretable Question Answering on Knowledge Bases and Text (P19-1)
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| Challenge: | Existing evaluation paradigms for ML based question answering models are lacking . a lack of explanation methods has been proposed for QA models . |
| Approach: | They propose an automatic evaluation paradigm for explanation methods in ML based question answering models . they adapt post hoc explanation methods such as LIME and input perturbation to the model . |
| Outcome: | The proposed evaluation paradigm compares explanation methods with human annotations. |
Hit the Nail on the Head: Parameter-Efficient Multi-task Tuning via Human Language Intervention (2024.findings-emnlp)
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| Challenge: | Recent studies show that PEFT on small pre-trained language models improves multitasking capabilities. |
| Approach: | They propose a multi-task learning framework that enables transfer of prior knowledge across tasks . they attach task descriptions to input samples and map them to task embeddings . |
| Outcome: | The proposed method improves performance on a T5 model and in decoder-only models . |