2.3k citations · 2.9k across the 20 of their papers we have counts for
5 papers · 1 filter
Meta-Learning Online Adaptation of Language Models
Nathan Hu, Eric Mitchell, Christopher D. Manning +1
Large language models encode impressively broad world knowledge in their parameters. However, the knowledge in static language models falls out of date, limiting the model's effect…
Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback
Katherine Tian, Eric Mitchell, Allan Zhou +5
A trustworthy real-world prediction system should produce well-calibrated confidence scores; that is, its confidence in an answer should be indicative of the likelihood that the an…
RECKONING: Reasoning through Dynamic Knowledge Encoding
Zeming Chen, Gail Weiss, Eric Mitchell +2
Recent studies on transformer-based language models show that they can answer questions by reasoning over knowledge provided as part of the context (i.e., in-context reasoning). Ho…
DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature
Eric Mitchell, Yoonho Lee, Alexander Khazatsky +2
The increasing fluency and widespread usage of large language models (LLMs) highlight the desirability of corresponding tools aiding detection of LLM-generated text. In this paper,…
Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference
Eric Mitchell, Joseph J. Noh, Siyan Li +5
While large pre-trained language models are powerful, their predictions often lack logical consistency across test inputs. For example, a state-of-the-art Macaw question-answering…