GRADA: Graph-based Reranker against Adversarial Documents Attack
Retrieval Augmented Generation (RAG) frameworks improve the accuracy of large language models (LLMs) by integrating external knowledge from retrieved …
FLARE: Faithful Logic-Aided Reasoning and Exploration
We introduce Faithful Logic-Aided Reasoning and Exploration (FLARE), a novel interpretable approach for traversing the problem space using task decomp…
DeCoRe: Decoding by Contrasting Retrieval Heads to Mitigate Hallucinations
Large Language Models (LLMs) often hallucinate, producing unfaithful or factually incorrect outputs by misrepresenting the provided context. We propos…
Neurosymbolic Diffusion Models
Neurosymbolic (NeSy) predictors combine neural perception with symbolic reasoning to solve tasks like visual reasoning. However, standard NeSy predict…
MMLongBench: Benchmarking Long-Context Vision-Language Models Effectively and Thoroughly
We introduce MMLongBench, the first benchmark covering a diverse set of long-context vision-language tasks to evaluate long-context vision-language mo…
Neurosymbolic Reasoning Shortcuts under the Independence Assumption
The ubiquitous independence assumption among symbolic concepts in neurosymbolic (NeSy) predictors is a convenient simplification that speeds up probab…
Noiser: Bounded Input Perturbations for Attributing Large Language Models
Feature attribution (FA) methods are common post-hoc approaches that explain how Large Language Models (LLMs) make predictions. Generating faithful at…
An Analysis of Decoding Methods for LLM-based Agents for Faithful Multi-Hop Question Answering
Large Language Models (LLMs) frequently produce factually inaccurate outputs—a phenomenon known as hallucination—which limits their accuracy in knowle…
Q-Filters: Leveraging QK Geometry for Efficient KV Cache Compression
Autoregressive language models rely on a Key-Value (KV) Cache to avoid re-computing past hidden states during generation. As model sizes and context l…