Search Results for author:
Found 11 papers, 6 papers with code
RLHF Deciphered: A Critical Analysis of Reinforcement Learning from Human Feedback for LLMs
A promising approach is reinforcement learning from human feedback (RLHF), which leverages human feedback to update the model in accordance with human preferences and mitigate issues like toxicity and hallucinations.
GEO: Generative Engine Optimization
We facilitate systematic evaluation in this new paradigm by introducing GEO-bench, a benchmark of diverse user queries across multiple domains, coupled with sources required to answer these queries.
QualEval: Qualitative Evaluation for Model Improvement
In this work, we address the shortcomings of quantitative metrics by proposing QualEval, which augments quantitative scalar metrics with automated qualitative evaluation as a vehicle for model improvement.
PruMUX: Augmenting Data Multiplexing with Model Compression
As language models increase in size by the day, methods for efficient inference are critical to leveraging their capabilities for various applications.
C-STS: Conditional Semantic Textual Similarity
Semantic textual similarity (STS), a cornerstone task in NLP, measures the degree of similarity between a pair of sentences, and has broad application in fields such as information retrieval and natural language understanding.
Toxicity in ChatGPT: Analyzing Persona-assigned Language Models
Large language models (LLMs) have shown incredible capabilities and transcended the natural language processing (NLP) community, with adoption throughout many services like healthcare, therapy, education, and customer service.
MUX-PLMs: Data Multiplexing for High-throughput Language Models
The widespread adoption of large language models such as ChatGPT and Bard has led to unprecedented demand for these technologies.
Building Scalable Video Understanding Benchmarks through Sports
Our human studies indicate that ASAP can align videos and annotations with high fidelity, precision, and speed.
DataMUX: Data Multiplexing for Neural Networks
In this paper, we introduce data multiplexing (DataMUX), a technique that enables deep neural networks to process multiple inputs simultaneously using a single compact representation.
Large-scale Pretraining for Visual Dialog: A Simple State-of-the-Art Baseline
Next, we find that additional finetuning using "dense" annotations in VisDial leads to even higher NDCG -- more than 10% over our base model -- but hurts MRR -- more than 17% below our base model!
Improving Generative Visual Dialog by Answering Diverse Questions
Prior work on training generative Visual Dialog models with reinforcement learning(Das et al.) has explored a Qbot-Abot image-guessing game and shown that this 'self-talk' approach can lead to improved performance at the downstream dialog-conditioned image-guessing task.
