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Found 13 papers, 7 papers with code
Template Matters: Understanding the Role of Instruction Templates in Multimodal Language Model Evaluation and Training
Models tuned on our augmented dataset achieve the best overall performance when compared with the same scale MLMs tuned on at most 75 times the scale of our augmented dataset, highlighting the importance of instruction templates in MLM training.
ProVision: Programmatically Scaling Vision-centric Instruction Data for Multimodal Language Models
Our multi-image instruction data leads to an 8% improvement on Mantis-Eval.
Ranked #105 on
Visual Question Answering
on MM-Vet
Disentangling Likes and Dislikes in Personalized Generative Explainable Recommendation
Specifically, we construct the datasets by explicitly extracting users' positive and negative opinions from their post-purchase reviews using an LLM, and propose to evaluate systems based on whether the generated explanations 1) align well with the users' sentiments, and 2) accurately identify both positive and negative opinions of users on the target items.
LARE: Latent Augmentation using Regional Embedding with Vision-Language Model
LARE achieves robust image classification for domains in and out using augmented image embeddings to fine-tune VLMs.
Explaining Length Bias in LLM-Based Preference Evaluations
The use of large language models (LLMs) as judges, particularly in preference comparisons, has become widespread, but this reveals a notable bias towards longer responses, undermining the reliability of such evaluations.
Adaptive In-conversation Team Building for Language Model Agents
Leveraging multiple large language model (LLM) agents has shown to be a promising approach for tackling complex tasks, while the effective design of multiple agents for a particular application remains an art.
Offline Training of Language Model Agents with Functions as Learnable Weights
Researchers and practitioners have recently reframed powerful Large Language Models (LLMs) as agents, enabling them to automate complex tasks largely via the use of specialized functions.
Better Explain Transformers by Illuminating Important Information
Transformer-based models excel in various natural language processing (NLP) tasks, attracting countless efforts to explain their inner workings.
NLPBench: Evaluating Large Language Models on Solving NLP Problems
Recent developments in large language models (LLMs) have shown promise in enhancing the capabilities of natural language processing (NLP).
SCP: Spherical-Coordinate-based Learned Point Cloud Compression
In recent years, the task of learned point cloud compression has gained prominence.
Taming Small-sample Bias in Low-budget Active Learning
Instead of tuning the coefficient for each query round, which is sensitive and time-consuming, we propose the curriculum Firth bias reduction (CHAIN) that can automatically adjust the coefficient to be adaptive to the training process.
Leveraging Instance Features for Label Aggregation in Programmatic Weak Supervision
In particular, it is built on a mixture of Bayesian label models, each corresponding to a global pattern of correlation, and the coefficients of the mixture components are predicted by a Gaussian Process classifier based on instance features.
Adaptive Ranking-based Sample Selection for Weakly Supervised Class-imbalanced Text Classification
To obtain a large amount of training labels inexpensively, researchers have recently adopted the weak supervision (WS) paradigm, which leverages labeling rules to synthesize training labels rather than using individual annotations to achieve competitive results for natural language processing (NLP) tasks.
