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Found 16 papers, 2 papers with code
TN-Eval: Rubric and Evaluation Protocols for Measuring the Quality of Behavioral Therapy Notes
To address this gap, we collaborated with licensed therapists to design a comprehensive rubric for evaluating therapy notes across key dimensions: completeness, conciseness, and faithfulness.
BabyLM Turns 3: Call for papers for the 2025 BabyLM workshop
We also call for papers outside the competition in any relevant areas.
The potential -- and the pitfalls -- of using pre-trained language models as cognitive science theories
Many studies have evaluated the cognitive alignment of Pre-trained Language Models (PLMs), i. e., their correspondence to adult performance across a range of cognitive domains.
Understanding Graphical Perception in Data Visualization through Zero-shot Prompting of Vision-Language Models
Vision Language Models (VLMs) have been successful at many chart comprehension tasks that require attending to both the images of charts and their accompanying textual descriptions.
Development of Cognitive Intelligence in Pre-trained Language Models
The increasing cognitive alignment of these models has made them candidates for cognitive science theories.
LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing
This study focuses on the topic of LLMs assist NLP Researchers, particularly examining the effectiveness of LLM in assisting paper (meta-)reviewing and its recognizability.
From Intentions to Techniques: A Comprehensive Taxonomy and Challenges in Text Watermarking for Large Language Models
With the rapid growth of Large Language Models (LLMs), safeguarding textual content against unauthorized use is crucial.
How Well Do Deep Learning Models Capture Human Concepts? The Case of the Typicality Effect
How well do representations learned by ML models align with those of humans?
Incremental Comprehension of Garden-Path Sentences by Large Language Models: Semantic Interpretation, Syntactic Re-Analysis, and Attention
The overall goal is to evaluate whether humans and LLMs are aligned in their processing of garden-path sentences and in the lingering misinterpretations past the point of disambiguation, especially when extra-syntactic information (e. g., a comma delimiting a clause boundary) is present to guide processing.
Multi-Level Feedback Generation with Large Language Models for Empowering Novice Peer Counselors
We further design a self-improvement method on top of large language models to enhance the automatic generation of feedback.
Natural Mitigation of Catastrophic Interference: Continual Learning in Power-Law Learning Environments
The benefits of this environment include simplicity, rehearsal that is agnostic to both tasks and models, and the lack of a need for extra neural circuitry.
Pre-training LLMs using human-like development data corpus
Pre-trained Large Language Models (LLMs) have shown success in a diverse set of language inference and understanding tasks.
Human Behavioral Benchmarking: Numeric Magnitude Comparison Effects in Large Language Models
This research shows the utility of understanding LLMs using behavioral benchmarks and points the way to future work on the number representations of LLMs and their cognitive plausibility.
Helping the Helper: Supporting Peer Counselors via AI-Empowered Practice and Feedback
Millions of users come to online peer counseling platforms to seek support.
Modeling Motivational Interviewing Strategies On An Online Peer-to-Peer Counseling Platform
This work provides a deeper understanding of the use of motivational interviewing techniques on peer-to-peer counselor platforms and sheds light on how to build better training programs for volunteer counselors on online platforms.
WHEN FLUE MEETS FLANG: Benchmarks and Large Pre-trained Language Model for Financial Domain
To this end, we contribute the Financial Language Understanding Evaluation (FLUE), an open-source comprehensive suite of benchmarks for the financial domain.
