11 accepted papers
Automated red-teaming has emerged as an essential approach for identifying vulnerabilities in large language models (LLMs). However, most existing methods rely on fixed attack templates and focus primarily on individual high-severity flaws,limiting their adaptability to evolving defenses and their a…
Open-Domain Question Answering (ODQA) systems often struggle with the quality of retrieved passages, which may contain conflicting information and be misaligned with the reader’s needs. Existing retrieval methods aim to gather relevant passages but often fail to prioritize consistent and useful info…
Understanding the mechanisms underlying Large Language Model (LLM) behavior in Retrieval-Augmented Generation (RAG) systems is critical for enhancing reliability. In this paper, we leverage Sparse Autoencoders (SAEs) within the LLaMA Scope to uncover sparse, interpretable latents that govern RAG beh…
Low-Rank Adaptation (LoRA) is a widespread parameter-efficient fine-tuning algorithm for large-scale language models. It has been commonly accepted that LoRA mostly achieves promising results in single-task, low-resource settings, and struggles to handle multi-task instruction tuning scenarios. In t…
Despite the advancements made with the retrieve-then-read pipeline on open-domain question answering task, current methods still face challenges stemming from term mismatch and limited interaction between information retrieval systems and large language models. To mitigate these issues, we propose t…
In-context learning(ICL) has gained considerable attention due to its data efficiency and task adaptability. Unfortunately, ICL suffers from the demonstration bias, i.e., its performance and robustness are severely affected by the selection and ordering of demonstrations. In this paper, we identify…
Step-by-step reasoning methods, such as the Chain-of-Thought (CoT), have been demonstrated to be highly effective in harnessing the reasoning capabilities of Large Language Models (LLMs). Recent research efforts have sought to distill LLMs into Small Language Models (SLMs), with a significant focus…
The eXtreme Multi-label Classification (XMC) aims at accurately assigning large-scale labels to instances, and is challenging for learning, managing, and predicting over the large-scale and rapidly growing set of labels. Traditional XMC methods, like one-vs-all and tree-based methods struggle with t…
Distantly-supervised named entity recognition (NER) aims at training networks with distantly-labeled data, which is automatically obtained by matching entity mentions in the raw text with entity types in a knowledge base. Distant supervision may induce incomplete and noisy labels, so recent state-of…
Estimating conditional dependence graphs and precision matrices are some of the most common problems in modern statistics and machine learning. When data are fully observed, penalized maximum likelihood-type estimators have become standard tools for estimating graphical models under sparsity conditi…
In this work, we present an additive model for space-time data that splits the data into a temporally correlated component and a spatially correlated component. We model the spatially correlated portion using a time-varying Gaussian graphical model. Under assumptions on the smoothness of changes in…