SpiderGen: Towards Procedure Generation For Carbon Life Cycle Assessments with Generative AI

The study titled 'Can Machines Think Like Humans? A Behavioral Evaluation of LLM Agents in Dictator Games' investigates the prosocial behaviors of Large Language Model (LLM) agents. It examines how different personas influence these behaviors and benchmarks them against human actions. The findings indicate that assigning human-like identities to LLMs does not guarantee human-like decision-making, revealing significant variability in alignment with human behavior across different model architectures.

The article discusses the challenges and potential of scaling prompt optimization using LLM-provided textual gradients. While this method has proven effective for automatic prompt engineering, issues arise when increasing training data due to context-length limits and diminishing returns from long-context degradation. The authors propose a new approach called Textual Stochastic Gradient Descent with Momentum (TSGD-M), which utilizes momentum sampling to enhance training stability and scalability.

Log anomaly detection is essential for identifying system failures and preventing security breaches by recognizing irregular patterns in large volumes of log data. Traditional methods depend on training deep learning models with clean log sequences, which are often difficult and costly to obtain due to the need for human labeling. Existing automatic cleaning methods do not adequately consider the specific characteristics of logs. The proposed LogPurge framework offers a cost-effective solution by using a rule-enhanced purification process that selects normal log sequences from contaminated dat…

ReflexGrad is a new architecture designed to enhance zero-shot generalization in large language model (LLM) agents. It integrates three mechanisms: hierarchical TODO decomposition for strategic planning, history-aware causal reflection for identifying failure causes, and gradient-based optimization for systematic improvement. This approach allows agents to learn from experiences without needing task-specific training, marking a significant advancement in reinforcement learning and decision-making.

MalRAG is a novel retrieval-augmented framework designed for the fine-grained identification of open-set malicious traffic in cybersecurity. As cyber threats continuously evolve, the ability to detect both known and new types of malicious traffic is paramount. This framework utilizes a frozen large language model (LLM) to construct a comprehensive traffic knowledge database, employing adaptive retrieval and prompt engineering techniques to enhance identification capabilities.

Large Language Models (LLMs) have shown remarkable capabilities in generalizing across various domains and modalities. This study explores their potential to encode astrophysical information typically derived from scientific measurements. The research focuses on two primary questions: the impact of prompting on the codification of physical quantities by LLMs and the linguistic aspects crucial for encoding the physics represented by these measurements. Sparse autoencoders are utilized to extract interpretable features from the text.

A new framework for detecting errors in SQL generated by large language models (LLMs) has been introduced, focusing on estimating uncertainty at the node level within the query's abstract syntax tree (AST). The method employs a semantically aware labeling algorithm to assess node correctness and utilizes a classifier to predict error probabilities for each node. This approach allows for precise diagnostics, significantly improving error detection compared to traditional token log-probabilities across various databases and datasets.

Recent advancements in large language model (LLM) pruning have demonstrated state-of-the-art compression results without the need for post-training or retraining, while still maintaining high predictive performance. However, prior research predominantly focused on English text for calibration, overlooking the multilingual capabilities of modern LLMs. This paper presents a comprehensive empirical study analyzing the effects of different calibration languages on pruning multilingual models, revealing significant insights into performance and internal representation changes.