A survey of large language models
Language is essentially a complex, intricate system of human expressions governed by
grammatical rules. It poses a significant challenge to develop capable AI algorithms for …
grammatical rules. It poses a significant challenge to develop capable AI algorithms for …
The flan collection: Designing data and methods for effective instruction tuning
We study the design decision of publicly available instruction tuning methods, by
reproducing and breaking down the development of Flan 2022 (Chung et al., 2022) …
reproducing and breaking down the development of Flan 2022 (Chung et al., 2022) …
Siren's song in the AI ocean: a survey on hallucination in large language models
While large language models (LLMs) have demonstrated remarkable capabilities across a
range of downstream tasks, a significant concern revolves around their propensity to exhibit …
range of downstream tasks, a significant concern revolves around their propensity to exhibit …
In-context retrieval-augmented language models
Abstract Retrieval-Augmented Language Modeling (RALM) methods, which condition a
language model (LM) on relevant documents from a grounding corpus during generation …
language model (LM) on relevant documents from a grounding corpus during generation …
Benchmarking large language models for news summarization
Large language models (LLMs) have shown promise for automatic summarization but the
reasons behind their successes are poorly understood. By conducting a human evaluation …
reasons behind their successes are poorly understood. By conducting a human evaluation …
Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models
Generative Large Language Models (LLMs) such as GPT-3 are capable of generating highly
fluent responses to a wide variety of user prompts. However, LLMs are known to hallucinate …
fluent responses to a wide variety of user prompts. However, LLMs are known to hallucinate …