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Found 18 papers, 8 papers with code
Artificial Intelligence Bias on English Language Learners in Automatic Scoring
This study investigated potential scoring biases and disparities toward English Language Learners (ELLs) when using automatic scoring systems for middle school students' written responses to science assessments.
Beyond Input Activations: Identifying Influential Latents by Gradient Sparse Autoencoders
Sparse Autoencoders (SAEs) have recently emerged as powerful tools for interpreting and steering the internal representations of large language models (LLMs).
A Survey on Sparse Autoencoders: Interpreting the Internal Mechanisms of Large Language Models
Despite the challenges that remain around SAE implementation and scaling, they continue to provide valuable tools for understanding the internal mechanisms of large language models.
Interpreting and Steering LLMs with Mutual Information-based Explanations on Sparse Autoencoders
Large language models (LLMs) excel at handling human queries, but they can occasionally generate flawed or unexpected responses.
Self-Regularization with Latent Space Explanations for Controllable LLM-based Classification
In training the classification model, we propose a simple and effective regularizer, by minimizing the similarity between the classifier weights and the identified unintended feature, to remove the impacts of these unintended features toward classification.
LMOD: A Large Multimodal Ophthalmology Dataset and Benchmark for Large Vision-Language Models
The prevalence of vision-threatening eye diseases is a significant global burden, with many cases remaining undiagnosed or diagnosed too late for effective treatment.
Unveiling Scoring Processes: Dissecting the Differences between LLMs and Human Graders in Automatic Scoring
Specifically, we prompt LLMs to generate analytic rubrics that they use to assign scores and study the alignment gap with human grading rubrics.
Retrieval-enhanced Knowledge Editing in Language Models for Multi-Hop Question Answering
To tackle the problem, we propose the Retrieval-Augmented model Editing (RAE) framework for multi-hop question answering.
Usable XAI: 10 Strategies Towards Exploiting Explainability in the LLM Era
Therefore, in this paper, we introduce Usable XAI in the context of LLMs by analyzing (1) how XAI can benefit LLMs and AI systems, and (2) how LLMs can contribute to the advancement of XAI.
InFoBench: Evaluating Instruction Following Ability in Large Language Models
This paper introduces the Decomposed Requirements Following Ratio (DRFR), a new metric for evaluating Large Language Models' (LLMs) ability to follow instructions.
Applying Large Language Models and Chain-of-Thought for Automatic Scoring
We found a more balanced accuracy across different proficiency categories when CoT was used with a scoring rubric, highlighting the importance of domain-specific reasoning in enhancing the effectiveness of LLMs in scoring tasks.
From Language Modeling to Instruction Following: Understanding the Behavior Shift in LLMs after Instruction Tuning
In this work, we investigate how the instruction tuning adjusts pre-trained models with a focus on intrinsic changes.
Could Small Language Models Serve as Recommenders? Towards Data-centric Cold-start Recommendations
To evaluate our approach, we introduce a cold-start recommendation benchmark, and the results demonstrate that the enhanced small language models can achieve comparable cold-start recommendation performance to that of large models with only $17\%$ of the inference time.
AGI: Artificial General Intelligence for Education
AGI, driven by the recent large pre-trained models, represents a significant leap in the capability of machines to perform tasks that require human-level intelligence, such as reasoning, problem-solving, decision-making, and even understanding human emotions and social interactions.
Black-box Backdoor Defense via Zero-shot Image Purification
Defending against such attacks is challenging, especially for real-world black-box models where only query access is permitted.
A Survey of Graph Prompting Methods: Techniques, Applications, and Challenges
In particular, we introduce the basic concepts of graph prompt learning, organize the existing work of designing graph prompting functions, and describe their applications and future challenges.
NoPPA: Non-Parametric Pairwise Attention Random Walk Model for Sentence Representation
We propose a novel non-parametric/un-trainable language model, named Non-Parametric Pairwise Attention Random Walk Model (NoPPA), to generate sentence embedding only with pre-trained word embedding and pre-counted word frequency.
Matching Exemplar as Next Sentence Prediction (MeNSP): Zero-shot Prompt Learning for Automatic Scoring in Science Education
Developing models to automatically score students' written responses to science problems is critical for science education.
