As large language models (LLMs) advance, it becomes more challenging to reliably
evaluate their output due to the high costs of human evaluation. To make progress towards …

LLMs and RAG systems are now capable of handling millions of input tokens or more.
However, evaluating the output quality of such systems on long-context tasks remains …

Existing metrics for evaluating the factuality of long-form text, such as FACTSCORE (Min et
al., 2023) and SAFE (Wei et al., 2024), decompose an input text into" atomic claims" and …

Conversational interfaces powered by Large Language Models (LLMs) have recently
become a popular way to obtain feedback during document editing. However, standard chat …

Recent work has explored the capability of large language models (LLMs) to identify and
correct errors in LLM-generated responses. These refinement approaches frequently …

Automatic factuality verification of large language model (LLM) generations is becoming
more and more widely used to combat hallucinations. A major point of tension in the …

Factuality evaluation aims to detect factual errors produced by language models (LMs) and
hence guide the development of more factual models. Towards this goal, we train a factuality …

Human evaluation has been the gold standard for checking faithfulness in abstractive
summarization. However, with a challenging source domain like narrative, multiple …

Large language models (LLMs) frequently generate non-factual content, known as
hallucinations. Existing retrieval-augmented-based hallucination detection approaches …

The large and ever-increasing amount of data available on the Internet coupled with the
laborious task of manual claim and fact verification has sparked the interest in the …