9 citations · 12 across the 16 of their papers we have counts for
11 papers · 1 filter
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…