Textual Data Bias Detection and Mitigation - An Extensible Pipeline with Experimental Evaluation

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.

GFM-RAG, a novel graph foundation model for retrieval augmented generation, has been introduced to enhance the integration of knowledge into large language models (LLMs). This model utilizes an innovative graph neural network to effectively capture complex relationships between queries and knowledge, addressing limitations faced by conventional retrieval-augmented generation systems.

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 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 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.

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.

Sparse autoencoders (SAEs) have been identified as a promising method for mechanistic interpretability and concept discovery in large language models (LLMs) and large vision-language models (LVLMs). However, a recent study reveals that many SAE neurons lack interpretability or steerability, which limits their effectiveness. To address these issues, the Concept Bottleneck Sparse Autoencoders (CB-SAE) framework has been proposed to enhance the utility of these models by pruning ineffective neurons and augmenting the latent space with desired concepts.