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Found 5 papers, 3 papers with code
Self-Explore to Avoid the Pit: Improving the Reasoning Capabilities of Language Models with Fine-grained Rewards
Training on large amounts of rationales (i. e., CoT Fine-tuning) is effective at improving the reasoning capabilities of large language models (LLMs).
FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets
Evaluation of Large Language Models (LLMs) is challenging because instruction-following necessitates alignment with human values and the required set of skills varies depending on the instruction.
Investigating the Effectiveness of Task-Agnostic Prefix Prompt for Instruction Following
In this paper, we present our finding that prepending a Task-Agnostic Prefix Prompt (TAPP) to the input improves the instruction-following ability of various Large Language Models (LLMs) during inference.
MED-SE: Medical Entity Definition-based Sentence Embedding
We propose Medical Entity Definition-based Sentence Embedding (MED-SE), a novel unsupervised contrastive learning framework designed for clinical texts, which exploits the definitions of medical entities.
Vision Transformer Equipped with Neural Resizer on Facial Expression Recognition Task
When it comes to wild conditions, Facial Expression Recognition is often challenged with low-quality data and imbalanced, ambiguous labels.
Facial Expression Recognition
Facial Expression Recognition (FER)
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