“Knowing When You Don’t Know”: A Multilingual Relevance Assessment Dataset for Robust Retrieval-Augmented Generation (2024)

Abstract: Retrieval-Augmented Generation (RAG) grounds Large Language Model (LLM) output by leveraging external knowledge sources to reduce factual hallucinations. However, prior work lacks a comprehensive evaluation of different language families, making it challenging to evaluate LLM robustness against errors in external retrieved knowledge. To overcome this, we establish **NoMIRACL**, a human-annotated dataset for evaluating LLM robustness in RAG across 18 typologically diverse languages. NoMIRACL includes both a non-relevant and a relevant subset. Queries in the non-relevant subset contain passages judged as non-relevant, whereas queries in the relevant subset include at least a single judged relevant passage. We measure relevance assessment using: (i) *hallucination rate*, measuring model tendency to hallucinate when the answer is not present in passages in the non-relevant subset, and (ii) *error rate*, measuring model inaccuracy to recognize relevant passages in the relevant subset. In our work, we observe that most models struggle to balance the two capacities. Models such as LLAMA-2 and Orca-2 achieve over 88% hallucination rate on the non-relevant subset. Mistral and LLAMA-3 hallucinate less but can achieve up to a 74.9% error rate on the relevant subset. Overall, GPT-4 is observed to provide the best tradeoff on both subsets, highlighting future work necessary to improve LLM robustness. NoMIRACL dataset and evaluation code are available at: https://github.com/project-miracl/nomiracl.

## introduction retrieval-augmented generation (rag) @xcite @xcite is a promising way to incorporate external knowledge via a first-stage retrieval system. rag instills information from reliable knowledge corpora (provided as external pas-[2] calculator: electronic calculators contain a keyboard with buttons for digits and arithmetical operations; some even contain \"00\" and \"000\" buttons to make larger or smaller numbers easier ... @xcite food pyramid (nutrition): a food pyramid represents the optimal number of servings to be eaten each day from each of the food groups. the first pyramid was published in sweden in 1974. when was the food pyramid first introduced? ## background and problem identification a challenging issue in rag is to provide robust and reliable llm-generated output against a first-stage information retrieval system. overreliance of the llm on the content of the retrieved passages (i.e., tendency to extract information from passages) can be limiting when passages are noisy or non-relevant @xcite @xcite . ## nomiracl dataset as the goal of nomiracl is to understand to which extent llms tend to hallucinate across different languages, our dataset contains 18 diverse languages with a myriad of both correct or answerable queries (relevant) subset and hallucinated or unanswerable queries (non-relevant). we describe our dataset construction procedure in ( §3.1), fold creation in ( §3.2) and languages covered and dataset usage in ( §3. 3 ). an overview of our data construction procedure is shown in figure 3 . ## experimental results we discuss our llm robustness evaluation results using the hallucination rate on the non-relevant subset in ( §5.1) and using the error rate on the relevant subset in ( §5.2), and compare overall both the relevant and non-relevant capacities in ( §5.3). ## empirical analysis in this section, we conduct an empirical analysis of llm outputs, with both the non-relevant subset (containing hallucinations) and the relevant subset (containing errors). we conduct our ablation study on the english (en) subset in nomiracl. we categorize each llm output pattern as either positive or accurate (highlighted in green), unable to understand instruction (highlighted in orange), or either a hallucination or error (highlighted in red). non-relevant subset. as shown in ## further studies prompt optimization. prompting is crucial in handling the robustness evaluation of multilingualfocused llms. techniques such as chain-of-thought (cot) @xcite or algorithmically optimizing prompts using dspy @xcite highlight the necessity of prompt optimization. although optimizing for the prompt is certainly challenging and expensive to evaluate all llms across 18 languages relevant and nonrelevant subsets, we experiment with three listwise variations techniques inspired by @xcite . the prompt template changes are listed in figure 10 : (i) role, we highlight the role of llm as an evaluator within the prompt at the beginning, (ii) repeat, we repeat the task instructions at the end of the prompt to remind the llm, and (iii) explanation, we ask the llm model to provide a step-by-step explanation and then answer and require 400 output tokens to fit both the llm reasoning and the answer. we evaluate mistral-7b with three prompt variations independently on nomiracl. the complete results are listed in fine-tuning on nomiracl. in this section, following prior works such as chain-of-verification (cove; @xcite or chain-of-noting (con; @xcite , we investigate the following research question: does fine-tuning on the nomir-acl development set help increase robustness? we experiment with two open-sourced llms: mistral-7b and llama-3 (8b). we supervised fine-tune (sft) lora adapters @xcite on the development set of nomiracl for all 18 languages (randomly sampled 90% train, 10% development) using 4-a6000 gpus each containing 48gb ram with peft. 5 our hyperparameter settings are listed in as shown in ## related work to hallucinations by generating factually incorrect output @xcite . by grounding on external knowledge, a retrieval-augmented llm can generate better and more trustworthy output @xcite @xcite . retrieval-augmented generation has achieved remarkable results in various tasks such as open-domain question answering (odqa) @xcite @xcite , argument extraction (du and ji, 2022) and code generation @xcite . real-world products such as bing search and langchain have incorporated rag applications. ## conclusion we introduce nomiracl, a multilingual humanlabeled dataset for relevance assessment of llm robustness as a binary relevance identification task in 18 languages. our multilingual dataset is humanannotated and constructed with 31 native speakers. we provide two subsets in nomiracl, the nonrelevant subset, where queries contain all judged non-relevant passages, and the relevant subset, where queries contain at least one relevant judged passage to measure the hallucination on the nonrelevant and error on the relevant subset. our experimental results indicate that existing llms are not robust, as we observe challenges in llm robustness in either hallucination or error. gpt-4 achieves the best model and performance tradeoff across both subsets. nomiracl can facilitate research in understanding to which extent llms tend to hallucinate, ultimately paving the way for building more effective and robust multilingual-focused llms in the future.