2 citations · 3 across the 6 of their papers we have counts for
6 papers
Benchmarking Deflection and Hallucination in Large Vision-Language Models
Nicholas Moratelli, Christopher Davis, Leonardo F. R. Ribeiro +2
Large Vision-Language Models (LVLMs) increasingly rely on retrieval to answer knowledge-intensive multimodal questions. Existing benchmarks overlook conflicts between visual and te…
Exploring Fine-Tuning for In-Context Retrieval and Efficient KV-Caching in Long-Context Language Models
Francesco Maria Molfese, Momchil Hardalov, Rexhina Blloshmi +2
With context windows of millions of tokens, Long-Context Language Models (LCLMs) can encode entire document collections, offering a strong alternative to conventional retrieval-aug…
RAGferee: Building Contextual Reward Models for Retrieval-Augmented Generation
Andrei C. Coman, Ionut-Teodor Sorodoc, Leonardo F. R. Ribeiro +3
Existing Reward Models (RMs), typically trained on general preference data, struggle in Retrieval Augmented Generation (RAG) settings, which require judging responses for faithfuln…
The Amazon Nova Family of Models: Technical Report and Model Card
Amazon AGI, Aaron Langford, Aayush Shah +783
We present Amazon Nova, a new generation of state-of-the-art foundation models that deliver frontier intelligence and industry-leading price performance. Amazon Nova Pro is a highl…
Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Models
Adrián Bazaga, Rexhina Blloshmi, Bill Byrne +1
Large Language Models (LLMs) have emerged as powerful tools for generating coherent text, understanding context, and performing reasoning tasks. However, they struggle with tempora…
Retrieving Contextual Information for Long-Form Question Answering using Weak Supervision
Philipp Christmann, Svitlana Vakulenko, Ionut Teodor Sorodoc +2
Long-form question answering (LFQA) aims at generating in-depth answers to end-user questions, providing relevant information beyond the direct answer. However, existing retrievers…