Search Results for author:
Found 15 papers, 8 papers with code
BLINK: Multimodal Large Language Models Can See but Not Perceive
We introduce Blink, a new benchmark for multimodal language models (LLMs) that focuses on core visual perception abilities not found in other evaluations.
Training Language Models to Generate Text with Citations via Fine-grained Rewards
While recent Large Language Models (LLMs) have proven useful in answering user queries, they are prone to hallucination, and their responses often lack credibility due to missing references to reliable sources.
Visual Program Distillation: Distilling Tools and Programmatic Reasoning into Vision-Language Models
We propose Visual Program Distillation (VPD), an instruction tuning framework that produces a vision-language model (VLM) capable of solving complex visual tasks with a single forward pass.
Ranked #1 on
Meme Classification
on Hateful Memes
DreamSync: Aligning Text-to-Image Generation with Image Understanding Feedback
Then, it uses two VLMs to select the best generation: a Visual Question Answering model that measures the alignment of generated images to the text, and another that measures the generation's aesthetic quality.
Davidsonian Scene Graph: Improving Reliability in Fine-grained Evaluation for Text-to-Image Generation
With extensive experimentation and human evaluation on a range of model configurations (LLM, VQA, and T2I), we empirically demonstrate that DSG addresses the challenges noted above.
Fine-Grained Human Feedback Gives Better Rewards for Language Model Training
We introduce Fine-Grained RLHF, a framework that enables training and learning from reward functions that are fine-grained in two respects: (1) density, providing a reward after every segment (e. g., a sentence) is generated; and (2) incorporating multiple reward models associated with different feedback types (e. g., factual incorrectness, irrelevance, and information incompleteness).
TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering
We introduce TIFA (Text-to-Image Faithfulness evaluation with question Answering), an automatic evaluation metric that measures the faithfulness of a generated image to its text input via visual question answering (VQA).
PromptCap: Prompt-Guided Image Captioning for VQA with GPT-3
PromptCap outperforms generic captions by a large margin and achieves state-of-the-art accuracy on knowledge-based VQA tasks (60. 4% on OK-VQA and 59. 6% on A-OKVQA).
One Embedder, Any Task: Instruction-Finetuned Text Embeddings
Our analysis suggests that INSTRUCTOR is robust to changes in instructions, and that instruction finetuning mitigates the challenge of training a single model on diverse datasets.
PromptCap: Prompt-Guided Task-Aware Image Captioning
PromptCap outperforms generic captions by a large margin and achieves state-of-the-art accuracy on knowledge-based VQA tasks (60. 4% on OK-VQA and 59. 6% on A-OKVQA).
Ranked #1 on
Visual Question Answering
on TextVQA test-standard
Binding Language Models in Symbolic Languages
We propose Binder, a training-free neural-symbolic framework that maps the task input to a program, which (1) allows binding a unified API of language model (LM) functionalities to a programming language (e. g., SQL, Python) to extend its grammar coverage and thus tackle more diverse questions, (2) adopts an LM as both the program parser and the underlying model called by the API during execution, and (3) requires only a few in-context exemplar annotations.
Ranked #4 on
Table-based Fact Verification
on TabFact
Unsupervised Learning of Hierarchical Conversation Structure
Human conversations can evolve in many different ways, creating challenges for automatic understanding and summarization.
In-Context Learning for Few-Shot Dialogue State Tracking
In this work, we propose an in-context learning (ICL) framework for zero-shot and few-shot learning DST, where a large pre-trained language model (LM) takes a test instance and a few exemplars as input, and directly decodes the dialogue state without any parameter updates.
Acoustic span embeddings for multilingual query-by-example search
In this work, we generalize AWE training to spans of words, producing acoustic span embeddings (ASE), and explore the application of ASE to QbE with arbitrary-length queries in multiple unseen languages.
Multilingual Jointly Trained Acoustic and Written Word Embeddings
The pre-trained models can then be used for unseen zero-resource languages, or fine-tuned on data from low-resource languages.
