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Found 7 papers, 3 papers with code
Mitigating Unsafe Feedback with Learning Constraints
While there has been progress towards aligning Large Language Models (LLMs) with human values and ensuring safe behaviour at inference time, safety-guards can easily be removed when fine-tuned on unsafe and harmful datasets. While this setting has been treated extensively, another popular training paradigm, learning from unsafe feedback with reinforcement learning, has previously been unexplored.
Semantic Consistency for Assuring Reliability of Large Language Models
Large Language Models (LLMs) exhibit remarkable fluency and competence across various natural language tasks.
Measuring Reliability of Large Language Models through Semantic Consistency
While large pretrained language models (PLMs) demonstrate incredible fluency and performance on many natural language tasks, recent work has shown that well-performing PLMs are very sensitive to what prompts are feed into them.
AskYourDB: An end-to-end system for querying and visualizing relational databases using natural language
Querying databases for the right information is a time consuming and error-prone task and often requires experienced professionals for the job.
GANDALF: Gated Adaptive Network for Deep Automated Learning of Features
We propose a novel high-performance, interpretable, and parameter \& computationally efficient deep learning architecture for tabular data, Gated Adaptive Network for Deep Automated Learning of Features (GANDALF).
Multi-Image Visual Question Answering
While a lot of work has been done on developing models to tackle the problem of Visual Question Answering, the ability of these models to relate the question to the image features still remain less explored.
Exploration of Visual Features and their weighted-additive fusion for Video Captioning
Video captioning is a popular task that challenges models to describe events in videos using natural language.
