Search Results for author:
Found 18 papers, 16 papers with code
RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios
We introduce RESIN-11, a new schema-guided event extraction&prediction framework that can be applied to a large variety of newsworthy scenarios.
MultiAgentBench: Evaluating the Collaboration and Competition of LLM agents
Large Language Models (LLMs) have shown remarkable capabilities as autonomous agents, yet existing benchmarks either focus on single-agent tasks or are confined to narrow domains, failing to capture the dynamics of multi-agent coordination and competition.
Synthia: Novel Concept Design with Affordance Composition
Text-to-image (T2I) models enable rapid concept design, making them widely used in AI-driven design.
Mobile-Agent-E: Self-Evolving Mobile Assistant for Complex Tasks
By hierarchical, we mean an explicit separation of high-level planning and low-level action execution.
Infogent: An Agent-Based Framework for Web Information Aggregation
Despite seemingly performant web agents on the task-completion benchmarks, most existing methods evaluate the agents based on a presupposition: the web navigation task consists of linear sequence of actions with an end state that marks task completion.
Visually Descriptive Language Model for Vector Graphics Reasoning
Without human-annotated data, empirical results show that VDLM significantly improves state-of-the-art LMMs like GPT-4o on various multimodal perception and reasoning tasks.
Do LVLMs Understand Charts? Analyzing and Correcting Factual Errors in Chart Captioning
This work inaugurates a new domain in factual error correction for chart captions, presenting a novel evaluation mechanism, and demonstrating an effective approach to ensuring the factuality of generated chart captions.
Factual Inconsistency Detection in Chart Captioning
Image Captioning
+1
Democratizing LLMs: An Exploration of Cost-Performance Trade-offs in Self-Refined Open-Source Models
The dominance of proprietary LLMs has led to restricted access and raised information privacy concerns.
Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration
In this work, we propose Solo Performance Prompting (SPP), which transforms a single LLM into a cognitive synergist by engaging in multi-turn self-collaboration with multiple personas.
RCOT: Detecting and Rectifying Factual Inconsistency in Reasoning by Reversing Chain-of-Thought
To detect factual inconsistency, RCoT first asks LLMs to reconstruct the problem based on generated solutions.
Paxion: Patching Action Knowledge in Video-Language Foundation Models
Action knowledge involves the understanding of textual, visual, and temporal aspects of actions.
Ranked #39 on
Video Question Answering
on NExT-QA
(using extra training data)
Zemi: Learning Zero-Shot Semi-Parametric Language Models from Multiple Tasks
Notably, our proposed $\text{Zemi}_\text{LARGE}$ outperforms T0-3B by 16% on all seven evaluation tasks while being 3. 9x smaller in model size.
Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners
The goal of this work is to build flexible video-language models that can generalize to various video-to-text tasks from few examples, such as domain-specific captioning, question answering, and future event prediction.
Rethinking Task Sampling for Few-shot Vision-Language Transfer Learning
While much literature has been devoted to exploring alternative optimization strategies, we identify another essential aspect towards effective few-shot transfer learning, task sampling, which is previously only be viewed as part of data pre-processing in MAML.
NewsClaims: A New Benchmark for Claim Detection from News with Attribute Knowledge
In this work, we present NewsClaims, a new benchmark for attribute-aware claim detection in the news domain.
RESIN: A Dockerized Schema-Guided Cross-document Cross-lingual Cross-media Information Extraction and Event Tracking System
We present a new information extraction system that can automatically construct temporal event graphs from a collection of news documents from multiple sources, multiple languages (English and Spanish for our experiment), and multiple data modalities (speech, text, image and video).
The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Prediction
We introduce a new concept of Temporal Complex Event Schema: a graph-based schema representation that encompasses events, arguments, temporal connections and argument relations.
