Papers with fully fine-tuned models
Adaptive Weighted Proxy Tuning: Efficient Gray-Box Steering for Image Captioning. (2026.acl-industry)
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| Challenge: | Proxy tuning is a decoding-time approach that fails to account for instance-specific variations in model certainty and domain shift. |
| Approach: | They propose a gray-box steering framework that dynamically modulates the logit contributions of a large base model, a fine-tuned expert, and an untune . |
| Outcome: | Adaptive Weighted Proxy Tuning achieves performance parity with fine-tuned models while remaining parameter-free. |
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. |
Adversarial Robustness of Prompt-based Few-Shot Learning for Natural Language Understanding (2023.findings-acl)
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| Challenge: | Recent few-shot learning methods focus on improving downstream task performance, but there is limited understanding of the adversarial robustness of such methods. |
| Approach: | They evaluate prompt-based FSL methods against fully fine-tuned models to better understand the impact of various factors towards robustness. |
| Outcome: | The proposed methods show that they are less robust in the face of adversarial perturbations than fully fine-tuned models. |