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Found 12 papers, 7 papers with code
When Parts Are Greater Than Sums: Individual LLM Components Can Outperform Full Models
This paper studies in-context learning by decomposing the output of large language models into the individual contributions of attention heads and MLPs (components).
Do Localization Methods Actually Localize Memorized Data in LLMs? A Tale of Two Benchmarks
On the other hand, even successful methods identify neurons that are not specific to a single memorized sequence.
Data Curation Alone Can Stabilize In-context Learning
Across five tasks and two LLMs, sampling from stable subsets selected by CondAcc and Datamodels improves average accuracy over sampling from the entire training set by 7. 7% and 6. 3%, respectively.
CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks
Existing CL benchmarks have facilitated research on task adaptation and mitigating "catastrophic forgetting", but are limited to vision-only and language-only tasks.
Rethinking Why Intermediate-Task Fine-Tuning Works
Supplementary Training on Intermediate Labeled-data Tasks (STILTs) is a widely applied technique, which first fine-tunes the pretrained language models on an intermediate task before on the target task of interest.
Incorporating Commonsense Knowledge Graph in Pretrained Models for Social Commonsense Tasks
Pretrained language models have excelled at many NLP tasks recently; however, their social intelligence is still unsatisfactory.
Go Beyond Plain Fine-tuning: Improving Pretrained Models for Social Commonsense
Towards improving language models' social intelligence, we focus on the Social IQA dataset, a task requiring social and emotional commonsense reasoning.
TinyGAN: Distilling BigGAN for Conditional Image Generation
Generative Adversarial Networks (GANs) have become a powerful approach for generative image modeling.
Leveraging Hierarchical Category Knowledge for Data-Imbalanced Multi-Label Diagnostic Text Understanding
Clinical notes are essential medical documents to record each patient{'}s symptoms.
What Does This Word Mean? Explaining Contextualized Embeddings with Natural Language Definition
Contextualized word embeddings have boosted many NLP tasks compared with traditional static word embeddings.
xSense: Learning Sense-Separated Sparse Representations and Textual Definitions for Explainable Word Sense Networks
This paper focuses on interpreting the embeddings for various aspects, including sense separation in the vector dimensions and definition generation.
A Meaning-based English Math Word Problem Solver with Understanding, Reasoning and Explanation
This paper presents a meaning-based statistical math word problem (MWP) solver with understanding, reasoning and explanation.
