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Found 28 papers, 13 papers with code
SMIR: Efficient Synthetic Data Pipeline To Improve Multi-Image Reasoning
However, multi-image reasoning tasks are still under-explored in the open-source community due to two main challenges: (1) scaling datasets with multiple correlated images and complex reasoning instructions is resource-intensive and maintaining quality is difficult, and (2) there is a lack of robust evaluation benchmarks for multi-image tasks.
Improving Causal Reasoning in Large Language Models: A Survey
In this survey, we provide a comprehensive review of research aimed at enhancing LLMs for causal reasoning.
Mamba-Enhanced Text-Audio-Video Alignment Network for Emotion Recognition in Conversations
MaTAV is with the advantages of aligning unimodal features to ensure consistency across different modalities and handling long input sequences to better capture contextual multimodal information.
LIONs: An Empirically Optimized Approach to Align Language Models
Alignment is a crucial step to enhance the instruction-following and conversational abilities of language models.
DECOR: Improving Coherence in L2 English Writing with a Novel Benchmark for Incoherence Detection, Reasoning, and Rewriting
To bridge this gap, we introduce DECOR, a novel benchmark that includes expert annotations for detecting incoherence in L2 English writing, identifying the underlying reasons, and rewriting the incoherent sentences.
Surveying Attitudinal Alignment Between Large Language Models Vs. Humans Towards 17 Sustainable Development Goals
This study conducts a comprehensive review and analysis of the existing literature on the attitudes of LLMs towards the 17 SDGs, emphasizing the comparison between their attitudes and support for each goal and those of humans.
kNN-ICL: Compositional Task-Oriented Parsing Generalization with Nearest Neighbor In-Context Learning
Task-Oriented Parsing (TOP) enables conversational assistants to interpret user commands expressed in natural language, transforming them into structured outputs that combine elements of both natural language and intent/slot tags.
DiactTOD: Learning Generalizable Latent Dialogue Acts for Controllable Task-Oriented Dialogue Systems
Dialogue act annotations are important to improve response generation quality in task-oriented dialogue systems.
Using Textual Interface to Align External Knowledge for End-to-End Task-Oriented Dialogue Systems
We demonstrate our paradigm in practice through MultiWOZ-Remake, including an interactive textual interface built for the MultiWOZ database and a correspondingly re-processed dataset.
Visual Instruction Tuning
Instruction tuning large language models (LLMs) using machine-generated instruction-following data has improved zero-shot capabilities on new tasks, but the idea is less explored in the multimodal field.
Ranked #4 on
Spatial Reasoning
on EmbSpatial-Bench
ARNOLD: A Benchmark for Language-Grounded Task Learning With Continuous States in Realistic 3D Scenes
To tackle these challenges, we present ARNOLD, a benchmark that evaluates language-grounded task learning with continuous states in realistic 3D scenes.
FaceChat: An Emotion-Aware Face-to-face Dialogue Framework
While current dialogue systems like ChatGPT have made significant advancements in text-based interactions, they often overlook the potential of other modalities in enhancing the overall user experience.
GLIGEN: Open-Set Grounded Text-to-Image Generation
Large-scale text-to-image diffusion models have made amazing advances.
KRLS: Improving End-to-End Response Generation in Task Oriented Dialog with Reinforced Keywords Learning
In task-oriented dialogs (TOD), reinforcement learning (RL) algorithms train a model to directly optimize response for task-related metrics.
AU-Aware Vision Transformers for Biased Facial Expression Recognition
Studies have proven that domain bias and label bias exist in different Facial Expression Recognition (FER) datasets, making it hard to improve the performance of a specific dataset by adding other datasets.
Stateful Memory-Augmented Transformers for Efficient Dialogue Modeling
Transformer encoder-decoder models have achieved great performance in dialogue generation tasks, however, their inability to process long dialogue history often leads to truncation of the context To address this problem, we propose a novel memory-augmented transformer that is compatible with existing pre-trained encoder-decoder models and enables efficient preservation of the dialogue history information.
Video-based Smoky Vehicle Detection with A Coarse-to-Fine Framework
Moreover, we present a new Coarse-to-fine Deep Smoky vehicle detection (CoDeS) framework for efficient smoky vehicle detection.
DG2: Data Augmentation Through Document Grounded Dialogue Generation
Collecting data for training dialog systems can be extremely expensive due to the involvement of human participants and need for extensive annotation.
On the Generation of Medical Dialogs for COVID-19
Training complex dialog generation models on small datasets bears high risk of overfitting.
PRAL: A Tailored Pre-Training Model for Task-Oriented Dialog Generation
However, the performance of pre-trained models on task-oriented dialog tasks is still under-explored.
Memformer: A Memory-Augmented Transformer for Sequence Modeling
Transformers have reached remarkable success in sequence modeling.
Perception Score, A Learned Metric for Open-ended Text Generation Evaluation
Automatic evaluation for open-ended natural language generation tasks remains a challenge.
On the Generation of Medical Dialogues for COVID-19
On these two datasets, we train several dialogue generation models based on Transformer, GPT, and BERT-GPT.
A Tailored Pre-Training Model for Task-Oriented Dialog Generation
The recent success of large pre-trained language models such as BERT and GPT-2 has suggested the effectiveness of incorporating language priors in downstream dialog generation tasks.
TextGAIL: Generative Adversarial Imitation Learning for Text Generation
Generative Adversarial Networks (GANs) for text generation have recently received many criticisms, as they perform worse than their MLE counterparts.
Importance-Aware Learning for Neural Headline Editing
We propose to automate this headline editing process through neural network models to provide more immediate writing support for these social media news writers.
Alternating Recurrent Dialog Model with Large-scale Pre-trained Language Models
Existing dialog system models require extensive human annotations and are difficult to generalize to different tasks.
Quantifying Intrinsic Uncertainty in Classification via Deep Dirichlet Mixture Networks
With the widespread success of deep neural networks in science and technology, it is becoming increasingly important to quantify the uncertainty of the predictions produced by deep learning.
