Narrowing the Knowledge Evaluation Gap: Open-Domain Question Answering with Multi-Granularity Answers (2024)

Abstract: Factual questions typically can be answered correctly at different levels of granularity. For example, both “August 4, 1961” and “1961” are correct answers to the question “When was Barack Obama born?”. Standard question answering (QA) evaluation protocols, however, do not explicitly take this into account and compare a predicted answer against answers of a single granularity level. In this work, we propose GRANOLA QA, a novel evaluation setting where a predicted answer is evaluated in terms of accuracy and informativeness against a set of multi-granularity answers. We present a simple methodology for enriching existing datasets with multi-granularity answers, and create GRANOLA-EQ, a multi-granularity version of the EntityQuestions dataset. We evaluate a range of decoding methods on GRANOLA-EQ, including a new algorithm, called Decoding with Response Aggregation (DRAG), that is geared towards aligning the response granularity with the model’s uncertainty. Our experiments show that large language models with standard decoding tend to generate specific answers, which are often incorrect. In contrast, when evaluated on multi-granularity answers, DRAG yields a nearly 20 point increase in accuracy on average, which further increases for rare entities. Overall, this reveals that standard evaluation and decoding schemes may significantly underestimate the knowledge encapsulated in LMs.

## introduction large language models (llms) often generate factual errors, especially when the task requires less widely-known knowledge @xcite . such factual errors are commonly attributed to the lm lacking relevant knowledge @xcite or over-committing to earlier mistakes @xcite . we conjecture that factual mistakes can stem from a different failure source, when the model prioritizes different textual attributes (e.g., fluency or specific formats that appeared in the training corpora) over factuality. such failures can result in generated text that mixes both correct and incorrect statements, even when the incorrect parts are not strictly required by the question. consider for example the question "when was mark bils born?". when prompting chatgpt foot_0 for answering this question, sampled responses include " @xcite @xcite . this may suggest that the model is confident that bils was born in 1958 -which is a correct answer in this case, albeit not the most informative one -yet it displays a preference for outputting a more detailed but incorrect response in a specific full-date format. this example also highlights how factual questions can be answered correctly at different levels of granularity. namely, while the answers " @xcite @xcite in terms of informativeness, they are all factually correct. however, answer granularity levels are not considered in standard question answering (qa) settings, which typically evaluate a predicted answer based on its similarity to a set of reference answers of the same (usually the most-specific) granularity. even when different levels of granularity are present, there is no notion in which matching to a more specific answer is "better". as a result, standard qa evaluation may significantly underestimate the knowledge encapsulated in lms, a phenomenon which we refer to as the knowledge evaluation gap. indeed, recent human evaluation suggests that such granularity disparities account for approximately 10-15% of the disagreements between lexical matching and human evaluation @xcite . in this work, we tackle this problem by proposing a novel multi-granularity qa evaluation setting, called granola qa (short for granularity of labels). unlike existing evaluation, in granola qa questions are labeled with groundtruth answers with multiple levels of granularity and predicted answers are evaluated in terms of both their accuracy and informativeness ( §2). the evaluation is done using two new metrics: granola accuracy, which checks if there was a match against any of the answers, and granola informativeness, which is a weighted score prioritizing fine-grained correct answers over their coarse-grained counterparts. next, we present a simple and general methodology for augmenting an existing single-granularity qa dataset to the setting of granola qa, which does not involve any human labor ( §3). this process is based on obtaining additional information about entities present in the original questions and answer(s) from an external knowledge graph (kg), and then using an llm to form multi-granularity answers conditioned on this information. we apply our methodology on the entityquestions (eq) dataset @xcite , using wiki-data (vrandečić and krötzsch, 2014) as the kg. the resulting dataset, granola-eq, consists of 12k qa examples with an average of 2.9 multigranularity answers per question. a manual analy-sis of a random subset of the data shows that our automatic procedure yields highly-accurate answers. we evaluate various baselines on granola-eq, including greedy decoding and methods that abstain from answering in cases of uncertainty @xcite @xcite . in addition, we introduce a novel decoding strategy, called decoding with response aggregation (drag), that is geared towards aligning the granularity level of a model's response with its uncertainty level ( §4). drag uses temperature sampling to obtain a set of candidate responses, and then answers the original question based on an aggregation of these responses, which we implement using few-shot prompting. figure 1 depicts an example of drag's aggregation of several incorrect responses into a correct coarser answer that matches against the multi-granularity labels. our experiments ( §5) show that: (1) with standard decoding the gap between granola accuracy and standard accuracy is small, which corroborates that lms tend to output detailed responses, even when these are incorrect, (2) with drag this gap is high, showing that unlike standard decoding, drag outputs coarse answers, (3) granola accuracy remains high with drag even for rare entities, suggesting that llms know less detailed information about them rather than lacking any knowledge @xcite , (4) compared to standard decoding and methods that allow the model to abstain from answering ("idk"), drag yields a better trade-off between factuality and response informativeness, and (5) this evaluation gap is not observed when using semantic similarity scores against single-granularity reference answers. to summarize, we introduce granola, a new qa evaluation setting that considers both the accuracy and informativeness of predicted answers. we propose a simple automatic procedure for generating accurate multi-granular answers for given qa pairs, and apply it to the entityquestions dataset to create granola-eq. we introduce a new decoding scheme, called drag, tailored to modify the response to a level of granularity that fits the model's uncertainty levels. we show that drag improves both informativeness and accuracy (relative to standard decoding), and that standard evaluation may significantly under-estimate the knowledge of lms, especially about rare entities. ## granola question answering we formalize the setting of granola qa and define new metrics for quantifying accuracy and informativeness of qa predictions. ## decoding with response aggregation humans naturally tailor the granularity level of their responses to their uncertainty levels. consider asking a person a, when another person b was born. the format of the response will depend on the relationship between a and b, and specifically on how much a knows about b. for example, if a is extremely familiar with b (e.g., b is a's son), then we expect the answer to include the full date of birth. if a is only partially familiar with b (e.g., b is a celebrity that a knows), then we expect the answer to be more generic (e.g. only the year or decade). if a is not familiar with b, then we expect a to say that they do not know the answer. in this section, we propose a novel decoding strategy, called decoding with response aggregation (drag), that is intended to encourage lms to do the same. we focus on a fixed (i.e., frozen) lm, and our objective is to improve factuality at inference time by attempting to provide a coarser answer in the place of a fine-grained but incorrect answer. in §5, we will evaluate our proposed decoding strategy against various existing baselines on the granola qa dataset we constructed. drag consists of two stages: • sampling: we sample n responses from the model with temperature @xmath0. revisiting the example question "when was mark bils born?" ( §1), aggregating the sampled responses "march 22, 1958", "may 19, 1958" and "august 15, 1958", should yield "1958". pseudocode for drag is provided in figure 4 . ## experiments we assess how accounting for answer granularity, both in evaluation and during decoding, influences the evaluation of llm performance on factual questions. after describing our experimental setting ( §5.1), we compare between evaluation with standard accuracy and granola accuracy ( §5.2), which reveals that current qa settings underestimate llms' knowledge. then, we show that the gains in accuracy from using granola cannot be matched by existing semantic similarity scores ( §5.3), which highlights the utility of this setting in capturing differences between multi-granularity answers. last, we use the granola metrics to evaluate drag with respect to baselines in terms of accuracy and informativeness ( §5.3), showing its superiority in decoding answers that are tuned towards the llm's knowledge. ## conclusion we highlight a prominent source of factuality errors in modern lms: generating more detailed responses than their knowledge can support. using a new qa benchmark, granola-eq, with multi-granularity answers, and a novel decoding algorithm, drag, we show that taking the answer granularity level into account leads to a dramatic increase in model accuracy. in appendix a we discuss various directions for future work.