C o TAR : Chain-of-Thought Attribution Reasoning with Multi-level Granularity (2024)

Abstract: State-of-the-art performance in QA tasks is currently achieved by systems employing Large Language Models (LLMs), however these models tend to hallucinate information in their responses. One approach focuses on enhancing the generation process by incorporating attribution from the given input to the output. However, the challenge of identifying appropriate attributions and verifying their accuracy against a source is a complex task that requires significant improvements in assessing such systems. We introduce an attribution-oriented Chain-of-Thought reasoning method to enhance the accuracy of attributions. This approach focuses the reasoning process on generating an attribution-centric output. Evaluations on two context enhanced question-answering datasets using GPT-4 demonstrate improved accuracy and correctness of attributions. In addition, the combination of our method with finetuning enhances the response and attribution accuracy of two smaller LLMs, showing their potential to outperform GPT-4 in some cases.

## introduction text generation from sources, such as information obtained through retrieval, is a key aspect of grounded question answering. one way to ensure the credibility of these models is through attributed text generation. this method pairs the generated text with supporting evidence, enhancing the model's trustworthiness and allowing for easier detection of errors. the pairing process can be done in several levels. firstly, we can pair each sentence in our response with a set of passage identifiers @xcite . on a model detailed level, individual spans in the answer can correspond to a specific passage, hence attributing specific portions to various passages cot @xcite panic and the bible of dreams ] ... they also have [ 3 international hit singles ] such as [ 3 "mothers talk", "shout", ... ]. ## evaluation metrics in order to evaluate the answers, we specify metrics of two types. answer quality, measuring similarity between the predicted answer and the gold answer. citation quality, assessing the similarity between the cited text, the cited passage, and the citations present in the gold answer. for answer quality , we use the n-grams based method rouge-l (rl), and the semantic method bertscore (bert) @xcite . for hallucination evaluation, we use the hem 2 model, which was finetuned on nli datasets, and predict whether two texts are factually consistent. for citation quality, we measure the quality of the cited content; we propose specific metrics for each of the citation levels. we note that span citations must match the source passages precisely, while sentence and passage attributions are not required to do so. we use the sem-f1 metric proposed by @xcite for n-gram token level similarity between the cited content. in addition, we include the citation precision/recall introduced in @xcite , and combine them into alce f1, by using the harmonic mean. we also measure the f1 score over passage indices between the cited passages and the passages cited by the expected answer, denoted as doc f1, and the correct span citation attribution (csca) which indicates whether a predicted span is a direct span from the attributed passage. preprocessing flows, train our models, conduct inference, and evaluate on the various metrics in a customizable and seamless manner. our codebase provides a detailed description of how to reproduce the results using the custom steps we have incorporated with the framework. ## experimental results to examine how cot affects the model, we inspect the gpt-4 case, and provide a detailed overview of the metrics in in particular, the csca metric in the span level case showcases a significant advantage, mainly since the model is guided to produce the spans relevant for the answer in advance, thus allowing the model to accurately reference them. in these results also support our claims, as shown in the doc f1 metric, showcasing a significant advantage for our method across the various citation levels. since doc f1 is lower for missing and excessive citations, our superior performance in it demonstrates how our approach improves upon those aspects. at the passage level, the sem-f1 and alce-f1 scores showcase an advantage for the standard run. we hypothesize that the addition of cot for such a broad citation task is less helpful than in the more granular sentence and span level citations. we observe that the gpt-4 model performed best in the span level when using the span cot method, and the same applies for the sentence level with sentence cot. in the passage level, while the cot methods improve the answer quality, they do not aid in the citation quality. we showcase similar results for all the models, across the various citation levels and cot methods in tables 8 and 9 . for each setting, we display all metrics and category averages for answer and citation quality, respectively. we present an overview of the various combinations of citation levels and cot methods in for encoder-decoder models, the cot methods produce better results than the standard run, both in the answer and citation metrics. however, aside from the ms citation quality results, the advantage of using each citation level task with its corresponding cot method is less prominent. the sentence cot method is highly dominant in most of the metrics in the qsum dataset, and passage cot method being prominent in the ms dataset. idx. expected answer no-cot span-cot 75 (2) [ 2 general paralysis of the insane (gpi) ] gpi stands for [ 2 general paralysis of the insane ], [ 3 georgia budget and policy institute ], and [ 5 global residence program index ]. lets analyze the relevant spans of information from the input passages. from passage [2], the relevant spans are the following: * general paralysis of the insane thus, the final answer is: gpi stands for [ 2 general paralysis of the insane ]. 941 some sources state that [ 1 "born on the fourth of july" ] lasts [ 1 for two hours and twenty-five minutes. ] [ 2 "born on the fourth of july" was written in santa monica, california, during the fall of 1974 in exactly one month, three weeks and two days. ] ... [ 1 born on the fourth of july ] is the title of [ 1 stone's movie ], which runs [ 1 two hours and twenty-five minutes ]. [ 2 "born on the fourth of july" was written in santa monica, california, during the fall of 1974 in exactly one month, three weeks and two days. ] finally, we observe in ## related work several recent studies have suggested different techniques for text generation including citation attribution, with varying degrees of detail. attributing relevant information to web pages was done in @xcite ; @xcite ; @xcite . @xcite attributes the response to specific snippets from the source passages. in particular, @xcite assigns attribution for each produced sentence to one or several input passages. our sentence level citation approach and the evaluation metrics are based on this work. substantial work has been done to reduce hallucination in the generated text produced by the model @xcite , as citation has the ability to increase the reliability of the generated text. some work has been done on correlating answer to text sections in the retrieved passages @xcite . however, these techniques require additional processing. the usage of cot has shown significant improvements in a wide variety of tasks @xcite . additionally, in-context learning can be added to the input, by providing examples of how to solve the task at hand @xcite . we explore the effects of using cot with the various citation level tasks, in addition to the usage of in-context learning examples in every scenario. @xcite instruct and finetune models to produce claims, based on retrieved passages, differentiating between informative and distracting input segments. we specify the text granularity level we expect the model should cite and phrase its answers. @xcite focus on the sentence citation task, splitting up the generation task into multiple reasoning steps, including cot as well. our approach focuses on using a single call, producing the answer at-once, with a cot approach. ## conclusions through extensive measurements applied across various models and citation levels, we have been able to ascertain the quality of both answers and citations. our findings indicate that the use of our co-tar reasoning significantly enhances the capacity of a model to generate superior quality answers and more accurate, faithful citations from the source. this improvement is evident in both decoder-only and encoder-decoder models. furthermore, our research shows that by using finetuning, smaller models can compete with or even surpass the performance of gpt-4 across a wide range of answer and citation metrics. this is particularly evident when considering the diversity of citation levels and cot methods.