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Found 5 papers, 3 papers with code
EXAMS-V: A Multi-Discipline Multilingual Multimodal Exam Benchmark for Evaluating Vision Language Models
Solving the problems in the dataset requires advanced perception and joint reasoning over the text and the visual content of the image.
Factuality of Large Language Models in the Year 2024
Large language models (LLMs), especially when instruction-tuned for chat, have become part of our daily lives, freeing people from the process of searching, extracting, and integrating information from multiple sources by offering a straightforward answer to a variety of questions in a single place.
Beyond Size: How Gradients Shape Pruning Decisions in Large Language Models
GBLM-Pruner leverages the first-order term of the Taylor expansion, operating in a training-free manner by harnessing properly normalized gradients from a few calibration samples to determine the pruning metric, and substantially outperforms competitive counterparts like SparseGPT and Wanda in multiple benchmarks.
DKAF: KB Arbitration for Learning Task-Oriented Dialog Systems with Dialog-KB Inconsistencies
In this work, we define the novel problem of learning a TOD agent with dialog-KB inconsistencies in the training data.
Exploring Distributional Shifts in Large Language Models for Code Analysis
We systematically study how three large language models with code capabilities - CodeT5, Codex, and ChatGPT - generalize to out-of-domain data.
