GFM-RAG: Graph Foundation Model for Retrieval Augmented Generation

A recent study highlights the importance of safety alignment in large language models (LLMs) as they are increasingly adapted for various tasks. The research identifies safety degradation during fine-tuning, attributing it to catastrophic forgetting, and proposes continual learning (CL) strategies to preserve safety. The evaluation of these strategies shows that they can effectively reduce attack success rates compared to standard fine-tuning methods.

A novel framework named CoopRAG has been introduced to enhance question answering by enabling cooperative interactions between a retriever and a large language model (LLM). This approach aims to mitigate issues of factual inaccuracies and hallucinations that are common in existing retrieval-augmented generation (RAG) methods. By unrolling questions into sub-questions and utilizing a reasoning chain, CoopRAG seeks to improve the accuracy of document retrieval relevant to user queries.

A new study introduces KHRONOS, a kernel-based neural surrogate model designed for efficient multi-fidelity prediction of aerodynamic fields. This model integrates sparse high-fidelity data with low-fidelity information, leveraging variational principles and tensor decomposition to enhance computational efficiency compared to traditional dense neural networks.

Recent advancements in KL-Regularized Policy Gradient algorithms have been proposed to enhance the reasoning capabilities of large language models (LLMs). The study introduces a unified derivation known as the Regularized Policy Gradient (RPG) view, which clarifies the necessary weighting for KL variants in off-policy settings, aiming to optimize the surrogate for the intended KL-regularized objective.

A new study has introduced a comprehensive pipeline for detecting and mitigating biases in textual data used to train large language models (LLMs), addressing representation bias and stereotypes as mandated by regulations like the European AI Act. The proposed pipeline includes generating word lists, quantifying representation bias, and employing sociolinguistic filtering to mitigate stereotypes.

A recent study highlights the need to address anthropocentric biases in evaluating large language models (LLMs), identifying two overlooked types: auxiliary oversight and mechanistic chauvinism. These biases can hinder the accurate assessment of LLM cognitive capacities, necessitating a more nuanced evaluation approach.

A recent study evaluated the effectiveness of deep learning models and large language models (LLMs) for vulnerability detection, focusing on models like ReVeal and LineVul across four datasets: Juliet, Devign, BigVul, and ICVul. The research highlights the gap between benchmark performance and real-world applicability, emphasizing the need for systematic evaluation in practical scenarios.

A recent study investigates the reliability of Large Language Models (LLMs) in detecting their own confabulations, which are fluent but incorrect outputs. The research focuses on how in-context information affects model behavior and whether LLMs can recognize unreliable responses. By estimating token-level uncertainty, the study aims to enhance response-level reliability predictions through controlled experiments on open QA benchmarks.