RAG-HAR: Retrieval Augmented Generation-based Human Activity Recognition

A new framework called SCOPE has been introduced to enhance long-term planning in complex text-based environments by utilizing large language models (LLMs) as one-time teachers for hierarchical planning. This approach aims to mitigate the computational costs associated with querying LLMs during training and inference, allowing for more efficient deployment. SCOPE leverages LLM-generated subgoals only at initialization, addressing the limitations of fixed parameter models.

Interpreto has been launched as a Python library aimed at enhancing the explainability of text models developed by HuggingFace, including BERT and various large language models (LLMs). This library offers two main types of explanations: attributions and concept-based explanations, making it a valuable tool for data scientists seeking to provide clarity on model decisions.

A new study has introduced a text classification algorithm utilizing large language models and neural attention mechanisms, addressing traditional methods' limitations in capturing long-range dependencies and contextual semantics. The framework involves text encoding, attention-based enhancements, and classification predictions, optimizing model parameters through cross-entropy loss.

A new dataset named CourtPressGER has been introduced, consisting of 6.4k triples that include judicial rulings, human-drafted press releases, and synthetic prompts for large language models (LLMs). This dataset aims to enhance the generation of readable summaries from complex judicial texts, addressing the communication needs of the public and experts alike.

Recent advancements in counterfactual explanations for text classification have been introduced, focusing on guiding Large Language Models (LLMs) to generate high-fidelity outputs without the need for task-specific fine-tuning. This approach enhances the quality of counterfactuals, which are crucial for model interpretability.

A recent study introduced MindShift, a benchmark for evaluating large language models' (LLMs) psychological adaptability, utilizing the Minnesota Multiphasic Personality Inventory (MMPI) to assess how well LLMs can reflect user-specified personality traits through tailored prompts. The findings indicate significant improvements in LLMs' role perception due to advancements in training datasets and alignment techniques.

A new study has introduced methods utilizing beam search to enhance consistency-based uncertainty quantification in large language models (LLMs), addressing issues with multinomial sampling that often leads to duplicates and high variance in uncertainty estimates. The research demonstrates improved performance across six question-answering datasets, establishing a theoretical lower bound for beam search effectiveness.

Recent research highlights the vulnerabilities of large language models (LLMs) to corruption through fine-tuning and inductive backdoors. Experiments demonstrated that minor adjustments in specific contexts can lead to significant behavioral shifts, such as adopting outdated knowledge or personas, exemplified by a model mimicking Hitler's biography. This raises concerns about the reliability and safety of LLMs in diverse applications.