FaithBench: A Diverse Hallucination Benchmark for Summarization by Modern LLMs

Summarization is one of the most common tasks performed by large language models (LLMs), especially in applications like Retrieval-Augmented Generation (RAG). However, existing evaluations of hallucinations in LLM-generated summaries, and evaluations of hallucination detection models both suffer from a lack of diversity and recency in the LLM and LLM families considered. This paper introduces FaithBench, a summarization hallucination benchmark comprising challenging hallucinations made by 10 modern LLMs from 8 different families, with ground truth annotations by human experts. ``Challenging'' here means summaries on which popular, state-of-the-art hallucination detection models, including GPT-4o-as-a-judge, disagreed on. Our results show GPT-4o and GPT-3.5-Turbo produce the least hallucinations. However, even the best hallucination detection models have near 50\% accuracies on FaithBench, indicating lots of room for future improvement. The repo is https://github.com/vectara/FaithBench

Can't Hide Behind the API: Stealing Black-Box Commercial Embedding Models

Embedding models that generate representation vectors from natural language text are widely used, reflect substantial investments, and carry significant commercial value. Companies such as OpenAI and Cohere have developed competing embedding models accessed through APIs that require users to pay for usage. In this architecture, the models are "hidden" behind APIs, but this does not mean that they are "well guarded". We present, to our knowledge, the first effort to "steal" these models for retrieval by training local models on text-embedding pairs obtained from the commercial APIs. Our experiments show using standard benchmarks that it is possible to efficiently replicate the retrieval effectiveness of the commercial embedding models using an attack that costs only around $200 to train (presumably) smaller models with fewer dimensions. Our findings raise important considerations for deploying commercial embedding models and suggest measures to mitigate the risk of model theft.

Scaling Down, LiTting Up: Efficient Zero-Shot Listwise Reranking with Seq2seq Encoder-Decoder Models

Recent work in zero-shot listwise reranking using LLMs has achieved state-of-the-art results. However, these methods are not without drawbacks. The proposed methods rely on large LLMs with billions of parameters and limited context sizes. This paper introduces LiT5-Distill and LiT5-Score, two methods for efficient zero-shot listwise reranking, leveraging T5 sequence-to-sequence encoder-decoder models. Our approaches demonstrate competitive reranking effectiveness compared to recent state-of-the-art LLM rerankers with substantially smaller models. Through LiT5-Score, we also explore the use of cross-attention to calculate relevance scores to perform reranking, eliminating the reliance on external passage relevance labels for training. We present a range of models from 220M parameters to 3B parameters, all with strong reranking results, challenging the necessity of large-scale models for effective zero-shot reranking and opening avenues for more efficient listwise reranking solutions. We provide code and scripts to reproduce our results at https://github.com/castorini/LiT5.