23 citations · 65 across the 30 of their papers we have counts for
32 papers
Token Rankings are Unforgeable Language Model Signatures
Matthew Finlayson, Andreas Grivas, Xiang Ren +1
Language model parameters are known to impose unique (to each model) geometric constraints on their logit outputs, which serves as a signature that identifies the model, but also l…
Side-by-side Comparison Amplifies Dialect Bias in Language Models
Kritee Kondapally, Claire J. Smerdon, Pooja C. Patel +5
Language models (LMs) can exhibit biases based on variations in their dialects, even in the absence of a dialect label, a behavior known as covert dialect bias. In this work, we qu…
BenchBrowser: Retrieving Evidence for Evaluating Benchmark Validity
Harshita Diddee, Gregory Yauney, Swabha Swayamdipta +1
Do language model benchmarks actually measure what practitioners intend them to ? High-level metadata is too coarse to convey the granular reality of benchmarks: a "poetry" benchma…
Why Fine-Tuning Encourages Hallucinations and How to Fix It
Guy Kaplan, Zorik Gekhman, Zhen Zhu +5
Large language models are prone to hallucinating factually incorrect statements. A key source of these errors is exposure to new factual information through supervised fine-tuning…
Beyond Explainable AI (XAI): An Overdue Paradigm Shift and Post-XAI Research Directions
Saleh Afroogh, Syed Ishtiaque Ahmed, Petra Ahrweiler +46
This study provides a cross-disciplinary examination of Explainable Artificial Intelligence (XAI) approaches-focusing on deep neural networks (DNNs) and large language models (LLMs…
Disentangling Geometry, Performance, and Training in Language Models
Atharva Kulkarni, Jacob Mitchell Springer, Arjun Subramonian +1
Geometric properties of Transformer weights, particularly the unembedding matrix, have been widely useful in language model interpretability research. Yet, their utility for estima…
ChEmREF: Evaluating Language Model Readiness for Chemical Emergency Response
Risha Surana, Qinyuan Ye, Swabha Swayamdipta
Emergency responders managing hazardous material HAZMAT incidents face critical, time-sensitive decisions, manually navigating extensive chemical guidelines. We investigate whether…
Every Language Model Has a Forgery-Resistant Signature
Matthew Finlayson, Xiang Ren, Swabha Swayamdipta
The ubiquity of closed-weight language models with public-facing APIs has generated interest in forensic methods, both for extracting hidden model details (e.g., parameters) and fo…
Believing without Seeing: Quality Scores for Contextualizing Vision-Language Model Explanations
Keyu He, Tejas Srinivasan, Brihi Joshi +3
When people query Vision-Language Models (VLMs) but cannot see the accompanying visual context (e.g. for blind and low-vision users), augmenting VLM predictions with natural langua…
ELI-Why: Evaluating the Pedagogical Utility of Language Model Explanations
Brihi Joshi, Keyu He, Sahana Ramnath +5
Language models today are widely used in education, yet their ability to tailor responses for learners with varied informational needs and knowledge backgrounds remains under-explo…
Better Language Model Inversion by Compactly Representing Next-Token Distributions
Murtaza Nazir, Matthew Finlayson, John X. Morris +2
Language model inversion seeks to recover hidden prompts using only language model outputs. This capability has implications for security and accountability in language model deplo…
Improving Language Model Personas via Rationalization with Psychological Scaffolds
Brihi Joshi, Xiang Ren, Swabha Swayamdipta +2
Language models prompted with a user description or persona are being used to predict the user's preferences and opinions. However, existing approaches to building personas mostly…
Teaching Models to Understand (but not Generate) High-risk Data
Ryan Wang, Matthew Finlayson, Luca Soldaini +2
Language model developers typically filter out high-risk content -- such as toxic or copyrighted text -- from their pre-training data to prevent models from generating similar outp…
Evaluating Evaluation Metrics -- The Mirage of Hallucination Detection
Atharva Kulkarni, Yuan Zhang, Joel Ruben Antony Moniz +5
Hallucinations pose a significant obstacle to the reliability and widespread adoption of language models, yet their accurate measurement remains a persistent challenge. While many…
On the Trustworthiness of Generative Foundation Models: Guideline, Assessment, and Perspective
Yue Huang, Chujie Gao, Siyuan Wu +63
Generative Foundation Models (GenFMs) have emerged as transformative tools. However, their widespread adoption raises critical concerns regarding trustworthiness across dimensions.…
Political-LLM: Large Language Models in Political Science
Lincan Li, Jiaqi Li, Catherine Chen +44
In recent years, large language models (LLMs) have been widely adopted in political science tasks such as election prediction, sentiment analysis, policy impact assessment, and mis…
Crowd-Calibrator: Can Annotator Disagreement Inform Calibration in Subjective Tasks?
Urja Khurana, Eric Nalisnick, Antske Fokkens +1
Subjective tasks in NLP have been mostly relegated to objective standards, where the gold label is decided by taking the majority vote. This obfuscates annotator disagreement and t…
Logits of API-Protected LLMs Leak Proprietary Information
Matthew Finlayson, Xiang Ren, Swabha Swayamdipta
Large language model (LLM) providers often hide the architectural details and parameters of their proprietary models by restricting public access to a limited API. In this work we…
Closing the Curious Case of Neural Text Degeneration
Matthew Finlayson, John Hewitt, Alexander Koller +2
Despite their ubiquity in language generation, it remains unknown why truncation sampling heuristics like nucleus sampling are so effective. We provide a theoretical explanation fo…
COBRA Frames: Contextual Reasoning about Effects and Harms of Offensive Statements
Xuhui Zhou, Hao Zhu, Akhila Yerukola +4
Warning: This paper contains content that may be offensive or upsetting. Understanding the harms and offensiveness of statements requires reasoning about the social and situational…
NeuroComparatives: Neuro-Symbolic Distillation of Comparative Knowledge
Phillip Howard, Junlin Wang, Vasudev Lal +3
Comparative knowledge (e.g., steel is stronger and heavier than styrofoam) is an essential component of our world knowledge, yet understudied in prior literature. In this paper, we…
We're Afraid Language Models Aren't Modeling Ambiguity
Alisa Liu, Zhaofeng Wu, Julian Michael +6
Ambiguity is an intrinsic feature of natural language. Managing ambiguity is a key part of human language understanding, allowing us to anticipate misunderstanding as communicators…
MAUVE Scores for Generative Models: Theory and Practice
Krishna Pillutla, Lang Liu, John Thickstun +6
Generative artificial intelligence has made significant strides, producing text indistinguishable from human prose and remarkably photorealistic images. Automatically measuring how…
I2D2: Inductive Knowledge Distillation with NeuroLogic and Self-Imitation
Chandra Bhagavatula, Jena D. Hwang, Doug Downey +7
Commonsense capabilities of pre-trained language models dramatically improve with scale, leading many to believe that scale is the only winning recipe. But is it? Here, we investig…