Search Results for author:
Found 6 papers, 4 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.
AutoMix: Automatically Mixing Language Models
Large language models (LLMs) are now available from cloud API providers in various sizes and configurations.
Let's Sample Step by Step: Adaptive-Consistency for Efficient Reasoning and Coding with LLMs
A popular approach for improving the correctness of output from large language models (LLMs) is Self-Consistency - poll the LLM multiple times and output the most frequent solution.
SemSup-XC: Semantic Supervision for Zero and Few-shot Extreme Classification
In this paper, we develop SemSup-XC, a model that achieves state-of-the-art zero-shot and few-shot performance on three XC datasets derived from legal, e-commerce, and Wikipedia data.
Hope Speech Detection on Social Media Platforms
Since personal computers became widely available in the consumer market, the amount of harmful content on the internet has significantly expanded.
