Search Results for author:
Found 9 papers, 4 papers with code
Does Instruction Tuning Make LLMs More Consistent?
The purpose of instruction tuning is enabling zero-shot performance, but instruction tuning has also been shown to improve chain-of-thought reasoning and value alignment (Si et al., 2023).
Word Order's Impacts: Insights from Reordering and Generation Analysis
Existing works have studied the impacts of the order of words within natural text.
Exploring Visual Culture Awareness in GPT-4V: A Comprehensive Probing
Pretrained large Vision-Language models have drawn considerable interest in recent years due to their remarkable performance.
Random Entity Quantization for Parameter-Efficient Compositional Knowledge Graph Representation
We analyze this phenomenon and reveal that entity codes, the quantization outcomes for expressing entities, have higher entropy at the code level and Jaccard distance at the codeword level under random entity quantization.
Copyright Violations and Large Language Models
Language models may memorize more than just facts, including entire chunks of texts seen during training.
PokemonChat: Auditing ChatGPT for Pokémon Universe Knowledge
We then evaluate its ability to acquire new knowledge and include it in its reasoning process.
Structural Similarities Between Language Models and Neural Response Measurements
Human language processing is also opaque, but neural response measurements can provide (noisy) recordings of activation during listening or reading, from which we can extract similar representations of words and phrases.
Inductive Relation Prediction from Relational Paths and Context with Hierarchical Transformers
Relation prediction on knowledge graphs (KGs) is a key research topic.
Implications of the Convergence of Language and Vision Model Geometries
Large-scale pretrained language models (LMs) are said to ``lack the ability to connect [their] utterances to the world'' (Bender and Koller, 2020).
