Large Vision-Language Models (LVLMs) are becoming increasingly popular for text-vision tasks requiring cross-modal reasoning, but often struggle with fine-grained visual discrimination. This limitation is evident in recent benchmarks like NaturalBench and D3, where closed models such as GPT-4o achieve only 39.6%, and open-source models perform below random chance (25%). We introduce Contrastive decoding with Contrast Images (CoCI), which adjusts LVLM outputs by contrasting them against outputs for similar images (Contrast Images - CIs). CoCI demonstrates strong performance across three distinct supervision regimes. First, when using naturally occurring CIs in benchmarks with curated image pairs, we achieve improvements of up to 98.9% on NaturalBench, 69.5% on D3, and 37.6% on MMVP. Second, for scenarios with modest training data (~5k samples), we show that a lightweight neural classifier can effectively select CIs from similar images at inference time, improving NaturalBench performance by up to 36.8%. Third, for scenarios with no training data, we develop a caption-matching technique that selects CIs by comparing LVLM-generated descriptions of candidate images. Notably, on VQAv2, our method improves VQA performance even in pointwise evaluation settings without explicit contrast images. Our approach demonstrates the potential for enhancing LVLMs at inference time through different CI selection approaches, each suited to different data availability scenarios.

Large Vision-Language Models (LVLMs) are an extension of Large Language Models (LLMs) that facilitate processing both image and text inputs, expanding AI capabilities. However, LVLMs struggle with object hallucinations due to their reliance on text cues and learned object co-occurrence biases. While most research quantifies these hallucinations, mitigation strategies are still lacking. Our study introduces a Language Contrastive Decoding (LCD) algorithm that adjusts LVLM outputs based on LLM distribution confidence levels, effectively reducing object hallucinations. We demonstrate the advantages of LCD in leading LVLMs, showing up to %4 improvement in POPE F1 scores and up to %36 reduction in CHAIR scores on the COCO validation set, while also improving captioning quality scores. Our method effectively improves LVLMs without needing complex post-processing or retraining, and is easily applicable to different models. Our findings highlight the potential of further exploration of LVLM-specific decoding algorithms.

In the Multi-document summarization (MDS) task, a summary is produced for a given set of documents. A recent line of research introduced the concept of damaging documents, denoting documents that should not be exposed to readers due to various reasons. In the presence of damaging documents, a summarizer is ideally expected to exclude damaging content in its output. Existing metrics evaluate a summary based on aspects such as relevance and consistency with the source documents. We propose to additionally measure the ability of MDS systems to properly handle damaging documents in their input set. To that end, we offer two novel metrics based on lexical similarity and language model likelihood. A set of experiments demonstrates the effectiveness of our metrics in measuring the ability of MDS systems to summarize a set of documents while eliminating damaging content from their summaries.

Abstraction is a core tenet of human cognition and communication. When composing natural language instructions, humans naturally evoke abstraction to convey complex procedures in an efficient and concise way. Yet, interpreting and grounding abstraction expressed in NL has not yet been systematically studied in NLP, with no accepted benchmarks specifically eliciting abstraction in NL. In this work, we set the foundation for a systematic study of processing and grounding abstraction in NLP. First, we deliver a novel abstraction elicitation method and present Hexagons, a 2D instruction-following game. Using Hexagons we collected over 4k naturally occurring visually-grounded instructions rich with diverse types of abstractions. From these data, we derive an instruction-to-execution task and assess different types of neural models. Our results show that contemporary models and modeling practices are substantially inferior to human performance, and that model performance is inversely correlated with the level of abstraction, showing less satisfying performance on higher levels of abstraction. These findings are consistent across models and setups, confirming that abstraction is a challenging phenomenon deserving further attention and study in NLP/AI research.