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Found 35 papers, 24 papers with code
VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training
Reinforcement Fine-Tuning (RFT) with verifiable rewards has advanced large language models but remains underexplored for Vision-Language (VL) models.
PIPE: Physics-Informed Position Encoding for Alignment of Satellite Images and Time Series
Multimodal time series forecasting is foundational in various fields, such as utilizing satellite imagery and numerical data for predicting typhoons in climate science.
S2LPP: Small-to-Large Prompt Prediction across LLMs
The performance of pre-trained Large Language Models (LLMs) is often sensitive to nuances in prompt templates, requiring careful prompt engineering, adding costs in terms of computing and human effort.
MMLongBench: Benchmarking Long-Context Vision-Language Models Effectively and Thoroughly
The rapid extension of context windows in large vision-language models has given rise to long-context vision-language models (LCVLMs), which are capable of handling hundreds of images with interleaved text tokens in a single forward pass.
Rethinking Memory in AI: Taxonomy, Operations, Topics, and Future Directions
By reframing memory systems through the lens of atomic operations and representation types, this survey provides a structured and dynamic perspective on research, benchmark datasets, and tools related to memory in AI, clarifying the functional interplay in LLMs based agents while outlining promising directions for future research\footnote{The paper list, datasets, methods and tools are available at \href{https://github. com/Elvin-Yiming-Du/Survey_Memory_in_AI}{https://github. com/Elvin-Yiming-Du/Survey\_Memory\_in\_AI}.
Neutralizing Bias in LLM Reasoning using Entailment Graphs
Extensive evaluations show that our framework can significantly reduce hallucinations from attestation bias.
ComparisonQA: Evaluating Factuality Robustness of LLMs Through Knowledge Frequency Control and Uncertainty
In addition, to avoid possible semantic shortcuts, which is a severe problem of current LLMs study, we design a two-round method for knowledge robustness measurement utilizing both correctness and uncertainty.
What Really is Commonsense Knowledge?
Commonsense datasets have been well developed in Natural Language Processing, mainly through crowdsource human annotation.
Concept-Reversed Winograd Schema Challenge: Evaluating and Improving Robust Reasoning in Large Language Models via Abstraction
While Large Language Models (LLMs) have showcased remarkable proficiency in reasoning, there is still a concern about hallucinations and unreliable reasoning issues due to semantic associations and superficial logical chains.
DivScene: Benchmarking LVLMs for Object Navigation with Diverse Scenes and Objects
In this paper, we study a new task of navigating to diverse target objects in a large number of scene types.
CLLMate: A Multimodal Benchmark for Weather and Climate Events Forecasting
Our experiments reveal the advantages and limitations of existing MLLMs and the value of CLLMate for the training and benchmarking of the WCEF task.
RoomDiffusion: A Specialized Diffusion Model in the Interior Design Industry
Recent advancements in text-to-image diffusion models have significantly transformed visual content generation, yet their application in specialized fields such as interior design remains underexplored.
CodeGraph: Enhancing Graph Reasoning of LLMs with Code
Compared to the existing methods, CodeGraph demonstrates strong performance on arithmetic problems in graph tasks and offers a more controllable and interpretable approach to the reasoning process.
KNOWCOMP POKEMON Team at DialAM-2024: A Two-Stage Pipeline for Detecting Relations in Dialogical Argument Mining
Dialogical Argument Mining(DialAM) is an important branch of Argument Mining(AM).
LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing
This study focuses on the topic of LLMs assist NLP Researchers, particularly examining the effectiveness of LLM in assisting paper (meta-)reviewing and its recognizability.
AbsInstruct: Eliciting Abstraction Ability from LLMs through Explanation Tuning with Plausibility Estimation
Abstraction ability is crucial in human intelligence, which can also benefit various tasks in NLP study.
ConstraintChecker: A Plugin for Large Language Models to Reason on Commonsense Knowledge Bases
When considering a new knowledge instance, ConstraintChecker employs a rule-based module to produce a list of constraints, then it uses a zero-shot learning module to check whether this knowledge instance satisfies all constraints.
CANDLE: Iterative Conceptualization and Instantiation Distillation from Large Language Models for Commonsense Reasoning
The sequential process of conceptualization and instantiation is essential to generalizable commonsense reasoning as it allows the application of existing knowledge to unfamiliar scenarios.
AbsPyramid: Benchmarking the Abstraction Ability of Language Models with a Unified Entailment Graph
Cognitive research indicates that abstraction ability is essential in human intelligence, which remains under-explored in language models.
Gold: A Global and Local-aware Denoising Framework for Commonsense Knowledge Graph Noise Detection
Commonsense Knowledge Graphs (CSKGs) are crucial for commonsense reasoning, yet constructing them through human annotations can be costly.
KCTS: Knowledge-Constrained Tree Search Decoding with Token-Level Hallucination Detection
Large Language Models (LLMs) have demonstrated remarkable human-level natural language generation capabilities.
TILFA: A Unified Framework for Text, Image, and Layout Fusion in Argument Mining
A main goal of Argument Mining (AM) is to analyze an author's stance.
Label-free Deep Learning Driven Secure Access Selection in Space-Air-Ground Integrated Networks
Considering the inherent co-channel interference due to spectrum sharing among multi-tier access networks in SAGIN, it can be leveraged to assist the physical layer security among heterogeneous transmissions.
A Unified Interactive Model Evaluation for Classification, Object Detection, and Instance Segmentation in Computer Vision
Existing model evaluation tools mainly focus on evaluating classification models, leaving a gap in evaluating more complex models, such as object detection.
Getting Sick After Seeing a Doctor? Diagnosing and Mitigating Knowledge Conflicts in Event Temporal Reasoning
However, knowledge conflicts arise when there is a mismatch between the actual temporal relations of events in the context and the prior knowledge or biases learned by the model.
COLA: Contextualized Commonsense Causal Reasoning from the Causal Inference Perspective
This paper proposes a new task to detect commonsense causation between two events in an event sequence (i. e., context), called contextualized commonsense causal reasoning.
CKBP v2: Better Annotation and Reasoning for Commonsense Knowledge Base Population
We show that CKBP v2 serves as a challenging and representative evaluation dataset for the CSKB Population task, while its development set aids in selecting a population model that leads to improved knowledge acquisition for downstream commonsense reasoning.
SubeventWriter: Iterative Sub-event Sequence Generation with Coherence Controller
In this paper, we propose a new task of sub-event generation for an unseen process to evaluate the understanding of the coherence of sub-event actions and objects.
Legal Element-oriented Modeling with Multi-view Contrastive Learning for Legal Case Retrieval
In addition to general topical relevance, the relevant cases also involve similar situations and legal elements, which can support the judgment of the current case.
SeATrans: Learning Segmentation-Assisted diagnosis model via Transformer
To model the segmentation-diagnosis interaction, SeA-block first embeds the diagnosis feature based on the segmentation information via the encoder, and then transfers the embedding back to the diagnosis feature space by a decoder.
Learning self-calibrated optic disc and cup segmentation from multi-rater annotations
In this paper, we propose a novel neural network framework to learn OD/OC segmentation from multi-rater annotations.
Opinions Vary? Diagnosis First!
Motivated by the observation that OD/OC segmentation is often used for the glaucoma diagnosis clinically, in this paper, we propose a novel strategy to fuse the multi-rater OD/OC segmentation labels via the glaucoma diagnosis performance.
UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation
In this paper, we take one step further to propose an ultra-simplified formulation of GCNs (dubbed UltraGCN), which skips infinite layers of message passing for efficient recommendation.
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UNBERT: User-News Matching BERT for News Recommendation
Nowadays, news recommendation has become a popular channel for users to access news of their interests.
SparTerm: Learning Term-based Sparse Representation for Fast Text Retrieval
Term-based sparse representations dominate the first-stage text retrieval in industrial applications, due to its advantage in efficiency, interpretability, and exact term matching.
