Stanford UG Y1, Aut

• Decision Making Index
• Computer Systems Index
• CS Probability Index
• Speech Processing Index

Stanford UG Y1, Win

• ODEs Index
• OS Index
• POMDPs Index
• Language Information Index

Stanford UG Y1, Spr

• Optimization Index
• Rust Systems Index
• Signal Processing Index
• NLP Index
• Democracy Index

Stanford UG Y2, Aut

• PL Index
• Theory of Computing Index

Stanford UG Y2, Win

• Complexity Theory Index
• ML Math Index
• Validation Index

Stanford UG Y2, Spr

• Complexity Theory Index
• Compilers Index
• Moral Philosophy Index

Stanford UG Y3, Aut

• Machine Learning Index
• Algorithms Index

Stanford UG Y3, Win

• Medical Ethics Index
• China Economy Index
• Convex Optimization Index
• Gender and Sexuality in East Asia

Stanford UG Y3, Spr

• Geology Index

Stanford UG Talks

Contacts

Talk Contacts

Several authors we have read questioned the possibility or appropriateness of democracy in countries where certain social structures and cultural ideologies are dominant—e.g., cases of Singapore and China. Do you think that particular cultures hinder the practice of democracy? Evaluate the debate on whether democracy has a universal appeal or is only appropriate to some cultures. What evidence exists to support each side of the debate and is it compelling? Take a position in this debate and make an argument for that position.

General Information

> Please indicate which prompt you have selected (Q1, Q2, or Q3) at the beginning of your essay.

In recent years, political polarization has increased in many democratic societies. As Diamond has observed, “among the liberal democracies, partisan and ideological polarization is often worrisomely high, while political tolerance and trust have eroded.” This trend also manifests itself in the growing ideological distance between political parties, increasing partisanship among the electorate, and the erosion of civility in public discourse.

Key Information

• Title: Fine-Grained Language Model Detoxification with Dense, Token-Level Rewards
• Team Member (in 224n): Houjun Liu <[email protected]>
• External Collaborators: Amelia Hardy <[email protected]>, Bernard Lange <[email protected]>
• Custom Project
• Mentor: we have no particular mentor within 224n
• Sharing Project: this project is shared with AA222, and is a part of a research project PI’d by Mykel Kochenderfer <[email protected]>, of which Houjun is taking a leading role

Research Paper Summary

Background

Reinforcement Learning with Human Feedback (RLHF) has demonstrated superb effect for improving performance of a language model (LM) via human preference judgments of LM output desirability–reducing incidences of toxic or false generation trajectories ((Ziegler et al. 2020)). Naive application of RLHF directly has shown success in reducing the toxicity in language model outputs, yet its effects could sometimes be inconsistent without further in-context guidance of the resulting model ((Ouyang et al. 2022)).

Background

Current deep-learning first approaches have shown promising results for the speech text diarization task. For ASR-independent diarization, specifically, two main methods appear as yielding fruitful conclusions:

1. Auditory feature extraction using deep learning to create a trained, fixed-size latent representation via Mel-frequency cepstral coefficients slices that came from any existing voice-activity detection (VAD) scheme ((Snyder et al. 2018)), where the features extracted with the neural network are later used with traditional clustering and Variational Bayes refinement ((Sell et al. 2018; Landini et al. 2022)) approaches to produce groups of diarized speakers