9 citations · 12 across the 16 of their papers we have counts for
17 papers
Open Technical Problems in Open-Weight AI Model Risk Management
Stephen Casper, Kyle O'Brien, Shayne Longpre +19
Frontier AI models with openly available weights are steadily becoming more powerful and widely adopted. However, compared to proprietary models, open-weight models pose different…
Voluntary Collusion with Secret Tools in Competing LLM Agents
Xijie Zeng, Frank Rudzicz
Even when a tool is explicitly described as unfair and harmful to others, ostensibly safety-aligned LLM agents still voluntarily engage in secret collusion whenever doing so confer…
Seeking SOTA: Time-Series Forecasting Must Adopt Taxonomy-Specific Evaluation to Dispel Illusory Gains
Raeid Saqur, Christoph Bergmeir, Blanka Horvath +3
We argue that the current practice of evaluating AI/ML time-series forecasting models, predominantly on benchmarks characterized by strong, persistent periodicities and seasonaliti…
LLM Library Learning Fails: A LEGO-Prover Case Study
Ian Berlot-Attwell, Frank Rudzicz, Xujie Si
Recent advancements in the coding, reasoning, and tool-using abilities of LLMs have spurred interest in library learning (i.e., online learning through the creation, storage, and r…
Contrastive Similarity Learning for Market Forecasting: The ContraSim Framework
Nicholas Vinden, Raeid Saqur, Zining Zhu +1
We introduce the Contrastive Similarity Space Embedding Algorithm (ContraSim), a novel framework for uncovering the global semantic relationships between daily financial headlines…
Show, Don't Tell: Uncovering Implicit Character Portrayal using LLMs
Brandon Jaipersaud, Zining Zhu, Frank Rudzicz +1
Tools for analyzing character portrayal in fiction are valuable for writers and literary scholars in developing and interpreting compelling stories. Existing tools, such as visuali…
Library Learning Doesn't: The Curious Case of the Single-Use "Library"
Ian Berlot-Attwell, Frank Rudzicz, Xujie Si
Advances in Large Language Models (LLMs) have spurred a wave of LLM library learning systems for mathematical reasoning. These systems aim to learn a reusable library of tools, suc…
Self-Supervised Embeddings for Detecting Individual Symptoms of Depression
Sri Harsha Dumpala, Katerina Dikaios, Abraham Nunes +3
Depression, a prevalent mental health disorder impacting millions globally, demands reliable assessment systems. Unlike previous studies that focus solely on either detecting depre…
Scenarios and Approaches for Situated Natural Language Explanations
Pengshuo Qiu, Frank Rudzicz, Zining Zhu
Large language models (LLMs) can be used to generate natural language explanations (NLE) that are adapted to different users' situations. However, there is yet to be a quantitative…
How Well Can Knowledge Edit Methods Edit Perplexing Knowledge?
Huaizhi Ge, Frank Rudzicz, Zining Zhu
Large language models (LLMs) have demonstrated remarkable capabilities, but updating their knowledge post-training remains a critical challenge. While recent model editing techniqu…
Understanding Language Model Circuits through Knowledge Editing
Huaizhi Ge, Frank Rudzicz, Zining Zhu
Recent advances in language model interpretability have identified circuits, critical subnetworks that replicate model behaviors, yet how knowledge is structured within these cruci…
Filtered not Mixed: Stochastic Filtering-Based Online Gating for Mixture of Large Language Models
Raeid Saqur, Anastasis Kratsios, Florian Krach +5
We propose MoE-F - a formalized mechanism for combining pre-trained Large Language Models (LLMs) for online time-series prediction by adaptively forecasting the best weighting…
ACCORD: Closing the Commonsense Measurability Gap
François Roewer-Després, Jinyue Feng, Zining Zhu +1
We present ACCORD, a framework and benchmark suite for disentangling the commonsense grounding and reasoning abilities of large language models (LLMs) through controlled, multi-hop…
NIFTY Financial News Headlines Dataset
Raeid Saqur, Ken Kato, Nicholas Vinden +1
We introduce and make publicly available the NIFTY Financial News Headlines dataset, designed to facilitate and advance research in financial market forecasting using large languag…
LLM-Generated Black-box Explanations Can Be Adversarially Helpful
Rohan Ajwani, Shashidhar Reddy Javaji, Frank Rudzicz +1
Large Language Models (LLMs) are becoming vital tools that help us solve and understand complex problems by acting as digital assistants. LLMs can generate convincing explanations,…
Plug and Play with Prompts: A Prompt Tuning Approach for Controlling Text Generation
Rohan Deepak Ajwani, Zining Zhu, Jonathan Rose +1
Transformer-based Large Language Models (LLMs) have shown exceptional language generation capabilities in response to text-based prompts. However, controlling the direction of gene…
A State-Vector Framework for Dataset Effects
Esmat Sahak, Zining Zhu, Frank Rudzicz
The impressive success of recent deep neural network (DNN)-based systems is significantly influenced by the high-quality datasets used in training. However, the effects of the data…