2.3k citations · 3.6k across the 40 of their papers we have counts for
11 papers · 1 filter
Evaluating and Mitigating Discrimination in Language Model Decisions
Alex Tamkin, Amanda Askell, Liane Lovitt +6
As language models (LMs) advance, interest is growing in applying them to high-stakes societal decisions, such as determining financing or housing eligibility. However, their poten…
Specific versus General Principles for Constitutional AI
Sandipan Kundu, Yuntao Bai, Saurav Kadavath +33
Human feedback can prevent overtly harmful utterances in conversational models, but may not automatically mitigate subtle problematic behaviors such as a stated desire for self-pre…
Towards Understanding Sycophancy in Language Models
Mrinank Sharma, Meg Tong, Tomasz Korbak +16
Human feedback is commonly utilized to finetune AI assistants. But human feedback may also encourage model responses that match user beliefs over truthful ones, a behaviour known a…
Studying Large Language Model Generalization with Influence Functions
Roger Grosse, Juhan Bae, Cem Anil +14
When trying to gain better visibility into a machine learning model in order to understand and mitigate the associated risks, a potentially valuable source of evidence is: which tr…
Question Decomposition Improves the Faithfulness of Model-Generated Reasoning
Ansh Radhakrishnan, Karina Nguyen, Anna Chen +21
As large language models (LLMs) perform more difficult tasks, it becomes harder to verify the correctness and safety of their behavior. One approach to help with this issue is to p…
Measuring Faithfulness in Chain-of-Thought Reasoning
Tamera Lanham, Anna Chen, Ansh Radhakrishnan +27
Large language models (LLMs) perform better when they produce step-by-step, "Chain-of-Thought" (CoT) reasoning before answering a question, but it is unclear if the stated reasonin…
Opportunities and Risks of LLMs for Scalable Deliberation with Polis
Christopher T. Small, Ivan Vendrov, Esin Durmus +6
Polis is a platform that leverages machine intelligence to scale up deliberative processes. In this paper, we explore the opportunities and risks associated with applying Large Lan…
Towards Measuring the Representation of Subjective Global Opinions in Language Models
Esin Durmus, Karina Nguyen, Thomas I. Liao +15
Large language models (LLMs) may not equitably represent diverse global perspectives on societal issues. In this paper, we develop a quantitative framework to evaluate whose opinio…
Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models
Myra Cheng, Esin Durmus, Dan Jurafsky
To recognize and mitigate harms from large language models (LLMs), we need to understand the prevalence and nuances of stereotypes in LLM outputs. Toward this end, we present Marke…
Whose Opinions Do Language Models Reflect?
Shibani Santurkar, Esin Durmus, Faisal Ladhak +3
Language models (LMs) are increasingly being used in open-ended contexts, where the opinions reflected by LMs in response to subjective queries can have a profound impact, both on…
Benchmarking Large Language Models for News Summarization
Tianyi Zhang, Faisal Ladhak, Esin Durmus +3
Large language models (LLMs) have shown promise for automatic summarization but the reasons behind their successes are poorly understood. By conducting a human evaluation on ten LL…