Learning Fine-Grained Grounded Citations for Attributed Large Language Models (2024)

Abstract: Despite the impressive performance on information-seeking tasks, large language models (LLMs) still struggle with hallucinations. Attributed LLMs, which augment generated text with in-line citations, demonstrate potential in mitigating hallucinations and improving verifiability. However, current approaches suffer from suboptimal citation quality due to their reliance on in-context learning. Furthermore, the practice of merely citing document identifiers complicates the process for users to pinpoint specific supporting evidence. In this work, we introduce FRONT, a training framework that teaches LLMs to generate Fine-grained grounded citations. By initially grounding fine-grained supporting quotes, which then guide the generation process, these quotes not only provide supervision signals to improve citation quality but also serve as fine-grained attributions. Experiments on the ALCE benchmark demonstrate the efficacy of FRONT in generating superior grounded responses and highly supportive citations. With LLaMA-2-7B, the framework significantly outperforms all the baselines, achieving an average of 14.21% improvement in citation quality across all datasets, even surpassing ChatGPT.

## introduction the recent advent of large language models (llms) @xcite has taken the world by storm, fueling a paradigm shift in information acquisition @xcite . despite their compelling performance, llms still struggle with hallucinations @xcite , a tendency to fabricate non-existent facts or generate unfaithful content. this issue further poses a risk of misinformation dissemination(chen and shu, 2023), directly impacting the reliability and trustworthiness of llms. why is it bad to eat cookie dough for risk of salmonella but things like cookie dough bites are ok? salmonella is most commonly caused by eating undercooked or raw foods like eggs or meat. (...) you know how your mom always warned you not to eat raw cookie dough? this is why. food safety recommendations encourage people to cook eggs until the white and yolk are firm in order to kill any bacteria. (...) however, anyone making cookies can do things to reduce this risk by using pasteurized egg products. ## 2 raw cookie dough can carry the risk of salmonella contamination, which can lead to foodborne illness if the dough is eaten raw . however, products like cookie dough bites are generally safe because they are made with pasteurized eggs . ## task formulation and methodology following @xcite , the task is formalized as follows: given a user query q and a corpus of retrieved documents d as input, the llm is required to produce a response s, which consists of statements with embedded in-line citations. we assume the response s comprising with n statements @xmath0, s 2 , . . . , s n } and each statement s @xmath1, cites a list of passage c @xmath2, c i2 , . . .}, where c @xmath3. specifically, citations are presented in the form of @xcite . next, we present a comprehensive overview of our method, which consists of two primary compo- ## 3 2 attributed? informative? bagel holes ensures even baking (...) a area . another suggests (...) and transport . ## 3 another suggests that they were used for easier handling and transport . nents: an automatic data generation pipeline ( §3.1) and a two-stage training framework ( §3.2). ## human evaluation given the significant impact of the quality of grounded quotes on fine-grained verification for users, we conducted a human evaluation to assess the quality of grounded quotes at different stages of our framework: (1) quotes extracted by chatgpt from 50 sampled data points during the g 3 stage. we engaged four annotators, each with relevant expertise and holding at least a bachelor's degree. the quality of quotes was evaluated on two dimensions: authenticity and helpfulness. authenticity (a binary scale of 0/1) refers to whether the quotes genuinely originate from the corresponding documents (quotes that are hallucinated or mismatched with the corresponding document id are considered inauthentic). helpfulness (5-point likert scale) refers to the degree to which the quotes are beneficial in addressing the query. the results in furthermore, to evaluate the consistency of quote quality annotations, we computed the interannotator agreement using fleiss' kappa coefficient. the obtained kappa coefficient of 0.82 indicates a high level of agreement among annotators. the results of the human evaluation indicate that both quotes extracted by chatgpt and those generated by front are of high quality, further substantiating the effectiveness of our method. ## conclusion in this work, we present front, a two-stage training framework designed to equip llms with finegrained attribution capabilities. front enables llms to initially select supporting quotes, which then guide the generation process. by further enhancing the consistency between the grounding and generation process via preference optimization, these supporting quotes can serve as fine-grained citations. through comprehensive experiments, front has demonstrated its ability to generate superior grounded responses and highly supportive citations. further analysis shows that front significantly reduces hallucinations and benefits user verification.