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Found 18 papers, 11 papers with code
The Hard Positive Truth about Vision-Language Compositionality
By training with both, we see improvements on existing benchmarks while simultaneously improving performance on hard positives, indicating a more robust improvement in compositionality.
Graph-Based Captioning: Enhancing Visual Descriptions by Interconnecting Region Captions
The nodes in GBC are created using, in a first stage, object detection and dense captioning tools nested recursively to uncover and describe entity nodes, further linked together in a second stage by highlighting, using new types of nodes, compositions and relations among entities.
Lookback Lens: Detecting and Mitigating Contextual Hallucinations in Large Language Models Using Only Attention Maps
We find that a linear classifier based on these lookback ratio features is as effective as a richer detector that utilizes the entire hidden states of an LLM or a text-based entailment model.
Found in the Middle: Calibrating Positional Attention Bias Improves Long Context Utilization
Large language models (LLMs), even when specifically trained to process long input contexts, struggle to capture relevant information located in the middle of their input.
DataComp-LM: In search of the next generation of training sets for language models
We introduce DataComp for Language Models (DCLM), a testbed for controlled dataset experiments with the goal of improving language models.
The Unmet Promise of Synthetic Training Images: Using Retrieved Real Images Performs Better
What additional value does the intermediate generator provide over directly training on relevant parts of the upstream data?
Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity
Large Language Models (LLMs), renowned for their remarkable performance across diverse domains, present a challenge when it comes to practical deployment due to their colossal model size.
Tool Documentation Enables Zero-Shot Tool-Usage with Large Language Models
Today, large language models (LLMs) are taught to use new tools by providing a few demonstrations of the tool's usage.
SugarCrepe: Fixing Hackable Benchmarks for Vision-Language Compositionality
In the last year alone, a surge of new benchmarks to measure compositional understanding of vision-language models have permeated the machine learning ecosystem.
Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes
Third, we reduce both the model size and the amount of data required to outperform LLMs; our finetuned 770M T5 model outperforms the few-shot prompted 540B PaLM model using only 80% of available data on a benchmark, whereas standard finetuning the same T5 model struggles to match even by using 100% of the dataset.
Understanding Programmatic Weak Supervision via Source-aware Influence Function
Programmatic Weak Supervision (PWS) aggregates the source votes of multiple weak supervision sources into probabilistic training labels, which are in turn used to train an end model.
Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data Programming
Weak Supervision (WS) techniques allow users to efficiently create large training datasets by programmatically labeling data with heuristic sources of supervision.
A Survey on Programmatic Weak Supervision
Labeling training data has become one of the major roadblocks to using machine learning.
Active Refinement for Multi-Label Learning: A Pseudo-Label Approach
The goal of multi-label learning (MLL) is to associate a given instance with its relevant labels from a set of concepts.
Evaluations and Methods for Explanation through Robustness Analysis
In this paper, we establish a novel set of evaluation criteria for such feature based explanations by robustness analysis.
On the (In)fidelity and Sensitivity of Explanations
We analyze optimal explanations with respect to both these measures, and while the optimal explanation for sensitivity is a vacuous constant explanation, the optimal explanation for infidelity is a novel combination of two popular explanation methods.
A Pseudo-Label Method for Coarse-to-Fine Multi-Label Learning with Limited Supervision
To address the need, we propose a special weakly supervised MLL problem that not only focuses on the situation of limited fine-grained supervision but also leverages the hierarchical relationship between the coarse concepts and the fine-grained ones.
On the (In)fidelity and Sensitivity for Explanations
We analyze optimal explanations with respect to both these measures, and while the optimal explanation for sensitivity is a vacuous constant explanation, the optimal explanation for infidelity is a novel combination of two popular explanation methods.
