Awesome Papers for Understanding LLM Mechanism This list focuses on understanding the internal mechanism of large language models (LLM). Works in this list are accepted by top conferences (e.g. ICML, NeurIPS, I
The Internal State of an LLM Knows When It's Lying [EMNLP 2023] [2023.4] [hallucination] Are Emergent Abilities of Large Language Models a Mirage? [NeurIPS 2023] [2023.4] [grokking] Towards automated circuit discovery for mechanistic interpretability ...
which calculate an individual's disease susceptibility based on quantitative trait loci (QTLs) and other regulatory genetic variants [24,58]. These scores sum up an individual's risk alleles, each weighted by its effect size derived from GWAS. Techniques such as penalized regression—LASSO...
Large Language Models (LLMs) shows powerful capability in natural language understanding by capturing hidden semantics in vector space. This process enriches the value of the text embeddings for various downstream tasks, thereby fostering the Embedding-as-a-Service (EaaS) business model. However, the...
(ρ = 0.182,p = 0.087), the regression analysis revealed a statistically significant relationship (B = 0.55,p = 0.038) after controlling for other variables. Thus, extended and structured interactions with ChatGPT under the CILP framework significantly enhanced conceptual learning ...
is also prevalent in Graph LLMs. This may be attributed to the baseline’s inability to recall the entire graph structure from a single graph embedding, leading to the generation of incorrect nodes or edges during the QA task. In contrast, by employing RAG for direct information retrieval from...
Mixed sample data augmentation是一种在机器学习中常用的技术,它通过结合不同样本的特征来创建新的训练样本,从而提高模型的泛化能力和鲁棒性。以下是关于mixed sample...
In recent years, the rapid development of natural language processing (NLP) techniques has enabled the effective vectorial representation of textual data through semantic embedding models, particularly with the emergence of large language models (LLMs). Given the inherent similarities between time series...
The reason is that the naive textual input cannot fully describe 3D shapes and harms the pre-trained language–image fusion in the embedding space [13]. To overcome the above challenge, we propose LLM-assisted textual feature learning to generate 3D-specific prompts that are rich in 3D ...
inputs. It’s worth noting that in the LLaMA-Adapter method, the prefix is learned and maintained within an embedding table rather than being provided externally. Each transformer block in the model has its own distinct learned prefix, allowing for more tailored adaptation across different model ...