34 citations · 45 across the 13 of their papers we have counts for
6 papers · 1 filter
Consistent but Miscalibrated: Evaluating LLM Limitations for Risk Communication in Natural Language
Diego Cerda-Mardini, Sarath Chandar, Sreenath Madathil
LLMs are increasingly deployed as post-hoc explainers of AI-generated outputs, yet it remains unclear whether they can reliably communicate probabilistic information in natural lan…
Probabilistic Calibration Is a Trainable Capability in Language Models
Davide Baldelli, Sruthi Kuriakose, Maryam Hashemzadeh +2
Language models are increasingly used in settings where outputs should follow an intended distribution, yet their generation probabilities are often poorly calibrated to it. In man…
Why Don't Prompt-Based Fairness Metrics Correlate?
Abdelrahman Zayed, Goncalo Mordido, Ioana Baldini +1
The widespread use of large language models has brought up essential questions about the potential biases these models might learn. This led to the development of several metrics a…
A Deep Dive into the Trade-Offs of Parameter-Efficient Preference Alignment Techniques
Megh Thakkar, Quentin Fournier, Matthew D Riemer +4
Large language models are first pre-trained on trillions of tokens and then instruction-tuned or aligned to specific preferences. While pre-training remains out of reach for most r…
Fairness-Aware Structured Pruning in Transformers
Abdelrahman Zayed, Goncalo Mordido, Samira Shabanian +2
The increasing size of large language models (LLMs) has introduced challenges in their training and inference. Removing model components is perceived as a solution to tackle the la…
Reasoning about Linguistic Regularities in Word Embeddings using Matrix Manifolds
Sridhar Mahadevan, Sarath Chandar
Recent work has explored methods for learning continuous vector space word representations reflecting the underlying semantics of words. Simple vector space arithmetic using cosine…