Retrieval-augmented generation (RAG) has recently become a very popular task for Large Language Models (LLMs). Evaluating them on multi-turn RAG conversations, where the system is asked to generate a response to a question in the context of a preceding conversation is an important and often overlooked task with several additional challenges. We present MTRAG: an end-to-end human-generated multi-turn RAG benchmark that reflects several real-world properties across diverse dimensions for evaluating the full RAG pipeline. MTRAG contains 110 conversations averaging 7.7 turns each across four domains for a total of 842 tasks. We also explore automation paths via synthetic data and LLM-as-a-Judge evaluation. Our human and automatic evaluations show that even state-of-the-art LLM RAG systems struggle on MTRAG. We demonstrate the need for strong retrieval and generation systems that can handle later turns, unanswerable questions, non-standalone questions, and multiple domains. MTRAG is available at https://github.com/ibm/mt-rag-benchmark.

The performance greatly benefits from the self-attention mechanism in Transformers, which could capture long-range dependencies Non-linear functions are prevalent in Transformers and their lightweight well, but with a substantial overhead in computation variants, incurring substantial and frequently underestimated and memory. Extensive research has been conducted to facilitate the hardware costs. Previous state-of-the-art works optimize deployment of Transformers on edge devices. Techniques like lightweight these operations by piece-wise linear approximation and store the structure integrating convolution and linear attention [4, 5] parameters in look-up tables (LUT), but most of them require unfriendly emerge, while quantization [6-8] and run-time pruning [9] has become high-precision arithmetics such as FP/INT 32 and lack consideration favored approaches to further reduced the hardware burden. of integer-only INT quantization. This paper proposed a However, the optimization of non-linear operations is frequently genetic LUT-Approximation algorithm namely GQA-LUT that can neglected in Transformer-based models which can be costly due to automatically determine the parameters with quantization awareness.