Abstract Evaluation practices in natural language generation (NLG) have many known flaws,
but improved evaluation approaches are rarely widely adopted. This issue has become …

Evaluating the factuality of long-form text generated by large language models (LMs) is non-
trivial because (1) generations often contain a mixture of supported and unsupported pieces …

Large language models (LLMs) have emerged as a widely-used tool for information
seeking, but their generated outputs are prone to hallucination. In this work, our aim is to …

While human evaluation remains best practice for accurately judging the faithfulness of
automatically-generated summaries, few solutions exist to address the increased difficulty …

We study continued training and supervised fine-tuning (SFT) of a language model (LM) to
make effective use of long-context information. We first establish a reliable evaluation …

While large language models (LLMs) already achieve strong performance on standard
generic summarization benchmarks, their performance on more complex summarization …

The problems of unfaithful summaries have been widely discussed under the context of
abstractive summarization. Though extractive summarization is less prone to the common …

There is significant interest in developing evaluation metrics which accurately estimate the
quality of generated text without the aid of a human-written reference text, which can be time …

There have been many benchmarks for evaluating long-context language models (LCLMs),
but developers often rely on synthetic tasks like needle-in-a-haystack (NIAH) or arbitrary …

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 …