Fetching the paper…

Reading the bibliography…

2023

On the steerability of large language models toward data-driven personas

Li, Junyi, Mehrabi, Ninareh, Peris, Charith et al.

Original
PDF
Post on X

Understand

Large language models (LLMs) are known to generate biased responses where the opinions of certain groups and populations are underrepresented.

Here, we present a novel approach to achieve controllable generation of specific viewpoints using LLMs, that can be leveraged to produce multiple perspectives and to reflect the diverse opinions.
Moving beyond the traditional reliance on demographics like age, gender, or party affiliation, we introduce a data-driven notion of persona grounded in collaborative filtering, which is defined as either a single individual or a cohort of individuals manifesting similar views across specific inquiries.
As individuals in the same demographic group may have different personas, our data-driven persona definition allows for a more nuanced understanding of different (latent) social groups present in the population.

On the steerability of large language models toward data-driven personas · Around

Built on

Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 1901
Earlier work this paper cites.
Fast transformer decoding: One write-head is all you need
Original
Noam Shazeer. 2019 · 1911
Earlier work this paper cites.
Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning
Haokun Liu, Derek Tam, Mohammed Muqeeth, Jay Mohta, Tenghao Huang, Mohit Bansal, and Colin A Raffel. 2022 · 1965
Earlier work this paper cites.
Pattern Recognition and Machine Learning
Christopher M. Bishop. 2006 · 2006
Earlier work this paper cites.
Decoupled weight decay regularization
Original
Ilya Loshchilov and Frank Hutter. 2017 · 2017
Earlier work this paper cites.
Lora: Low-rank adaptation of large language models
Original
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021 · 2021
Earlier work this paper cites.
The power of scale for parameter-efficient prompt tuning
Original
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
Earlier work this paper cites.
Prefix-tuning: Optimizing continuous prompts for generation
Original
Xiang Lisa Li and Percy Liang. 2021 · 2021
Earlier work this paper cites.
Gpt understands, too
Original
Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, and Jie Tang. 2021 · 2021
Earlier work this paper cites.
Communitylm: Probing partisan worldviews from language models
Original
Hang Jiang, Doug Beeferman, Brandon Roy, and Deb Roy. 2022 · 2022
Earlier work this paper cites.

Similar

Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022 · 2022
Cited alongside, same era.
Moral mimicry: Large language models produce moral rationalizations tailored to political identity
Original
Gabriel Simmons. 2022 · 2022
Cited alongside, same era.
Out of one, many: Using language models to simulate human samples
Lisa P Argyle, Ethan C Busby, Nancy Fulda, Joshua R Gubler, Christopher Rytting, and David Wingate. 2023 · 2023
Cited alongside, same era.
Toxicity in chatgpt: Analyzing persona-assigned language models
Original
Ameet Deshpande, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, and Karthik Narasimhan. 2023 · 2023
Cited alongside, same era.
Towards measuring the representation of subjective global opinions in language models
Original

Then

In-context impersonation reveals large language models’ strengths and biases
Original
Leonard Salewski, Stephan Alaniz, Isabel Rio-Torto, Eric Schulz, and Zeynep Akata. 2023 · 2023
Closest in time.
Whose opinions do language models reflect?
Original
Shibani Santurkar, Esin Durmus, Faisal Ladhak, Cinoo Lee, Percy Liang, and Tatsunori Hashimoto. 2023 · 2023
Closest in time.
Nlpositionality: Characterizing design biases of datasets and models
Original
Sebastin Santy, Jenny T Liang, Ronan Le Bras, Katharina Reinecke, and Maarten Sap. 2023 · 2023
Closest in time.
Evaluating the moral beliefs encoded in llms
Original
Nino Scherrer, Claudia Shi, Amir Feder, and David M Blei. 2023 · 2023
Closest in time.
Large language models encode clinical knowledge

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Esin Durmus, Karina Nyugen, Thomas I Liao, Nicholas Schiefer, Amanda Askell, Anton Bakhtin, Carol Chen, Zac Hatfield-Dodds, Danny Hernandez, Nicholas Joseph, et al. 2023 · 2023

Cited alongside, same era.

From pretraining data to language models to downstream tasks: Tracking the trails of political biases leading to unfair nlp models

Original

Shangbin Feng, Chan Young Park, Yuhan Liu, and Yulia Tsvetkov. 2023 · 2023

Cited alongside, same era.

Aligning language models to user opinions

EunJeong Hwang, Bodhisattwa Prasad Majumder, and Niket Tandon. 2023 · 2023

Cited alongside, same era.

New and improved embedding model

OpenAI. 2023 · 2023

Cited alongside, same era.

Gpt-4 technical report

R OpenAI. 2023 · 2023

Cited alongside, same era.

Controlling the extraction of memorized data from large language models via prompt-tuning

Mustafa Safa Ozdayi, Charith S. Peris, Jack G. M. FitzGerald, Christophe Dupuy, Jimit Majmudar, Haidar Khan, Rahil Parikh, and Rahul Gupta. 2023 · 2023

Cited alongside, same era.

Karan Singhal, Shekoofeh Azizi, Tao Tu, S Sara Mahdavi, Jason Wei, Hyung Won Chung, Nathan Scales, Ajay Tanwani, Heather Cole-Lewis, Stephen Pfohl, et al. 2023 · 2023

Closest in time.

Towards applying powerful large ai models in classroom teaching: Opportunities, challenges and prospects

Kehui Tan, Tianqi Pang, and Chenyou Fan. 2023 · 2023

Closest in time.

Llama 2: Open foundation and fine-tuned chat models

Original

Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. 2023 · 2023

Closest in time.

Bloomberggpt: A large language model for finance

Shijie Wu, Ozan Irsoy, Steven Lu, Vadim Dabravolski, Mark Dredze, Sebastian Gehrmann, Prabhanjan Kambadur, David Rosenberg, and Gideon Mann. 2023 · 2023

Closest in time.

Llama-adapter: Efficient fine-tuning of language models with zero-init attention

Renrui Zhang, Jiaming Han, Aojun Zhou, Xiangfei Hu, Shilin Yan, Pan Lu, Hongsheng Li, Peng Gao, and Yu Qiao. 2023 · 2023

Closest in time.

A survey of large language models

Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al. 2023 · 2023

Closest in time.