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Found 17 papers, 12 papers with code
MMEKG: Multi-modal Event Knowledge Graph towards Universal Representation across Modalities
Events are fundamental building blocks of real-world happenings.
MTR-Bench: A Comprehensive Benchmark for Multi-Turn Reasoning Evaluation
To fill these gaps, we present MTR-Bench for LLMs' Multi-Turn Reasoning evaluation.
Synergistic Weak-Strong Collaboration by Aligning Preferences
To optimize this collaboration, we introduce a collaborative feedback to fine-tunes the weak model, which quantifies the influence of the weak model's contributions in the collaboration procedure and establishes preference pairs to guide preference tuning of the weak model.
InternLM-XComposer2.5-Reward: A Simple Yet Effective Multi-Modal Reward Model
Despite the promising performance of Large Vision Language Models (LVLMs) in visual understanding, they occasionally generate incorrect outputs.
Long Context vs. RAG for LLMs: An Evaluation and Revisits
Extending context windows (i. e., Long Context, LC) and using retrievers to selectively access relevant information (i. e., Retrieval-Augmented Generation, RAG) are the two main strategies to enable LLMs to incorporate extremely long external contexts.
AntiLeak-Bench: Preventing Data Contamination by Automatically Constructing Benchmarks with Updated Real-World Knowledge
Data contamination hinders fair LLM evaluation by introducing test data into newer models' training sets.
Navigating the Nuances: A Fine-grained Evaluation of Vision-Language Navigation
The framework is structured around the context-free grammar (CFG) of the task.
TART: An Open-Source Tool-Augmented Framework for Explainable Table-based Reasoning
Current Large Language Models (LLMs) exhibit limited ability to understand table structures and to apply precise numerical reasoning, which is crucial for tasks such as table question answering (TQA) and table-based fact verification (TFV).
VLMEvalKit: An Open-Source Toolkit for Evaluating Large Multi-Modality Models
Based on the evaluation results obtained with the toolkit, we host OpenVLM Leaderboard, a comprehensive leaderboard to track the progress of multi-modality learning research.
MMLongBench-Doc: Benchmarking Long-context Document Understanding with Visualizations
Moreover, 33. 2% of the questions are cross-page questions requiring evidence across multiple pages.
SciAgent: Tool-augmented Language Models for Scientific Reasoning
To make this task more practical and solvable for LLMs, we introduce a new task setting named tool-augmented scientific reasoning.
Improving Large Language Models in Event Relation Logical Prediction
More in detail, we first investigate the deficiencies of LLMs in logical reasoning across different tasks.
Towards Verifiable Generation: A Benchmark for Knowledge-aware Language Model Attribution
Although achieving great success, Large Language Models (LLMs) usually suffer from unreliable hallucinations.
Few-shot Event Detection: An Empirical Study and a Unified View
Few-shot event detection (ED) has been widely studied, while this brings noticeable discrepancies, e. g., various motivations, tasks, and experimental settings, that hinder the understanding of models for future progress. This paper presents a thorough empirical study, a unified view of ED models, and a better unified baseline.
Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!
Large Language Models (LLMs) have made remarkable strides in various tasks.
Prompt for Extraction? PAIE: Prompting Argument Interaction for Event Argument Extraction
We have conducted extensive experiments on three benchmarks, including both sentence- and document-level EAE.
Information Extraction in Low-Resource Scenarios: Survey and Perspective
Information Extraction (IE) seeks to derive structured information from unstructured texts, often facing challenges in low-resource scenarios due to data scarcity and unseen classes.
