Search Results for author:
Found 45 papers, 22 papers with code
SPAN: Spatial Pyramid Attention Network for Image Manipulation Localization
Tehchniques for manipulating images are advancing rapidly; while these are helpful for many useful tasks, they also pose a threat to society with their ability to create believable misinformation.
Ranked #7 on
Image Manipulation Detection
on CocoGlide
RADAR: Benchmarking Language Models on Imperfect Tabular Data
Language models (LMs) are increasingly being deployed to perform autonomous data analyses.
Aligning Large Language Models with Implicit Preferences from User-Generated Content
Learning from preference feedback is essential for aligning large language models (LLMs) with human values and improving the quality of generated responses.
Enhancing Chart-to-Code Generation in Multimodal Large Language Models via Iterative Dual Preference Learning
Chart-to-code generation, the process of converting chart images into executable plotting scripts, provides a lossless representation of chart information, requiring models to accurately capture and summarize all visual and structural elements.
GraphMaster: Automated Graph Synthesis via LLM Agents in Data-Limited Environments
To address these issues, we introduce GraphMaster, the first multi-agent framework specifically designed for graph data synthesis in data-limited environments.
Rethinking Graph Structure Learning in the Era of LLMs
For Question 2, we propose decoupled and training-free model design principles for LLM integration, shifting the focus from computation-intensive fine-tuning to more efficient inference.
SpaceVLLM: Endowing Multimodal Large Language Model with Spatio-Temporal Video Grounding Capability
Moreover, we propose a Query-Guided Space Decoder to establish a corresponding connection between the queries and spatial coordinates.
Remote Sensing Semantic Segmentation Quality Assessment based on Vision Language Model
This makes the evaluation of semantic segmentation quality in such scenarios an issue to be resolved.
IHEval: Evaluating Language Models on Following the Instruction Hierarchy
The instruction hierarchy, which establishes a priority order from system messages to user messages, conversation history, and tool outputs, is essential for ensuring consistent and safe behavior in language models (LMs).
Adaptivity can help exponentially for shadow tomography
In recent years there has been significant interest in understanding the statistical complexity of learning from quantum data under the constraint that one can only make unentangled measurements.
MultiChartQA: Benchmarking Vision-Language Models on Multi-Chart Problems
Multimodal Large Language Models (MLLMs) have demonstrated impressive abilities across various tasks, including visual question answering and chart comprehension, yet existing benchmarks for chart-related tasks fall short in capturing the complexity of real-world multi-chart scenarios.
On the sample complexity of purity and inner product estimation
Our protocol can be modified to estimate the purity of an unknown quantum state $\rho$ using $k$-qubit quantum memory with the same complexity.
Enhancing Mathematical Reasoning in LLMs by Stepwise Correction
Best-of-N decoding methods instruct large language models (LLMs) to generate multiple solutions, score each using a scoring function, and select the highest scored as the final answer to mathematical reasoning problems.
TOWER: Tree Organized Weighting for Evaluating Complex Instructions
Evaluating the ability of large language models (LLMs) to follow complex human-written instructions is essential for their deployment in real-world applications.
RepoGraph: Enhancing AI Software Engineering with Repository-level Code Graph
Unlike traditional function-level or file-level coding tasks, AI software engineering requires not only basic coding proficiency but also advanced skills in managing and interacting with code repositories.
Leopard: A Vision Language Model For Text-Rich Multi-Image Tasks
Tasks involving multiple text-rich images are especially challenging, as they require not only understanding the content of individual images but reasoning about inter-relationships and logical flows across multiple visual inputs.
Stabilizer bootstrapping: A recipe for efficient agnostic tomography and magic estimation
We study the task of agnostic tomography: given copies of an unknown $n$-qubit state $\rho$ which has fidelity $\tau$ with some state in a given class $C$, find a state which has fidelity $\ge \tau - \epsilon$ with $\rho$.
BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions
In addition, using multiple tools to solve a task needs compositional reasoning by accurately understanding complex instructions.
Ranked #1 on
Code Generation
on BigCodeBench-Instruct
Hallucination Mitigation Prompts Long-term Video Understanding
Although existing methods achieve a balance between memory and information by aggregating frames, they inevitably introduce the severe hallucination issue.
Learn Beyond The Answer: Training Language Models with Reflection for Mathematical Reasoning
Supervised fine-tuning enhances the problem-solving abilities of language models across various mathematical reasoning tasks.
Analyzing Temporal Complex Events with Large Language Models? A Benchmark towards Temporal, Long Context Understanding
We refer to the complex events composed of many news articles over an extended period as Temporal Complex Event (TCE).
MathChat: Benchmarking Mathematical Reasoning and Instruction Following in Multi-Turn Interactions
We evaluate the performance of various SOTA LLMs on the MathChat benchmark, and we observe that while these models excel in single turn question answering, they significantly underperform in more complex scenarios that require sustained reasoning and dialogue understanding.
Large Language Models Can Self-Correct with Key Condition Verification
The condition can be an entity in an open-domain question or a numeric value in a math question, which requires minimal effort (via prompting) to identify.
Quantum-Classical Separations in Shallow-Circuit-Based Learning with and without Noises
In addition, for quantum devices with constant noise strength, we prove that no super-polynomial classical-quantum separation exists for any classification task defined by shallow Clifford circuits, independent of the structures of the circuits that specify the learning models.
Describe-then-Reason: Improving Multimodal Mathematical Reasoning through Visual Comprehension Training
Open-source multimodal large language models (MLLMs) excel in various tasks involving textual and visual inputs but still struggle with complex multimodal mathematical reasoning, lagging behind proprietary models like GPT-4V(ision) and Gemini-Pro.
LabelAId: Just-in-time AI Interventions for Improving Human Labeling Quality and Domain Knowledge in Crowdsourcing Systems
Crowdsourcing platforms have transformed distributed problem-solving, yet quality control remains a persistent challenge.
Chain-of-Layer: Iteratively Prompting Large Language Models for Taxonomy Induction from Limited Examples
Automatic taxonomy induction is crucial for web search, recommendation systems, and question answering.
PLUG: Leveraging Pivot Language in Cross-Lingual Instruction Tuning
Instruction tuning has remarkably advanced large language models (LLMs) in understanding and responding to diverse human instructions.
Auto-Instruct: Automatic Instruction Generation and Ranking for Black-Box Language Models
Large language models (LLMs) can perform a wide range of tasks by following natural language instructions, without the necessity of task-specific fine-tuning.
Exploring and Characterizing Large Language Models For Embedded System Development and Debugging
We leverage this finding to study how human programmers interact with these tools, and develop an human-AI based software engineering workflow for building embedded systems.
Exploring Contrast Consistency of Open-Domain Question Answering Systems on Minimally Edited Questions
Contrast consistency, the ability of a model to make consistently correct predictions in the presence of perturbations, is an essential aspect in NLP.
Pre-training Language Models for Comparative Reasoning
Comparative reasoning is a process of comparing objects, concepts, or entities to draw conclusions, which constitutes a fundamental cognitive ability.
Improving Language Models via Plug-and-Play Retrieval Feedback
ReFeed first generates initial outputs, then utilizes a retrieval model to acquire relevant information from large document collections, and finally incorporates the retrieved information into the in-context demonstration for output refinement, thereby addressing the limitations of LLMs in a more efficient and cost-effective manner.
Large Language Models are Built-in Autoregressive Search Engines
To overcome this limitation, recent autoregressive search engines replace the dual-encoder architecture by directly generating identifiers for relevant documents in the candidate pool.
StarCoder: may the source be with you!
The BigCode community, an open-scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder and StarCoderBase: 15. 5B parameter models with 8K context length, infilling capabilities and fast large-batch inference enabled by multi-query attention.
Ranked #56 on
Code Generation
on MBPP
Retrieval Augmentation for Commonsense Reasoning: A Unified Approach
However, applying such methods to commonsense reasoning tasks faces two unique challenges, i. e., the lack of a general large-scale corpus for retrieval and a corresponding effective commonsense retriever.
A Unified Encoder-Decoder Framework with Entity Memory
The entity knowledge is stored in the memory as latent representations, and the memory is pre-trained on Wikipedia along with encoder-decoder parameters.
On the Relationship Between Counterfactual Explainer and Recommender
To enable explainability, recent techniques such as ACCENT and FIA are looking for counterfactual explanations that are specific historical actions of a user, the removal of which leads to a change to the recommendation result.
A Survey of Multi-task Learning in Natural Language Processing: Regarding Task Relatedness and Training Methods
Multi-task learning (MTL) has become increasingly popular in natural language processing (NLP) because it improves the performance of related tasks by exploiting their commonalities and differences.
Diversifying Content Generation for Commonsense Reasoning with Mixture of Knowledge Graph Experts
A set of knowledge experts seek diverse reasoning on KG to encourage various generation outputs.
Knowledge-Aware Procedural Text Understanding with Multi-Stage Training
In this work, we focus on the task of procedural text understanding, which aims to comprehend such documents and track entities' states and locations during a process.
SPAN: Spatial Pyramid Attention Network forImage Manipulation Localization
We present a novel framework, Spatial Pyramid Attention Network (SPAN) for detection and localization of multiple types of image manipulations.
Learning Attribute-Structure Co-Evolutions in Dynamic Graphs
In this work, we present a novel framework called CoEvoGNN for modeling dynamic attributed graph sequence.
DCA: Diversified Co-Attention towards Informative Live Video Commenting
In this paper, we aim to collect diversified information from video and text for informative comment generation.
Cross-Modal Commentator: Automatic Machine Commenting Based on Cross-Modal Information
Automatic commenting of online articles can provide additional opinions and facts to the reader, which improves user experience and engagement on social media platforms.
