Desiderata For The Context Use Of Question Answering Systems (2024)

Abstract: Prior work has uncovered a set of common problems in state-of-the-art context-based question answering (QA) systems: a lack of attention to the context when the latter conflicts with a model’s parametric knowledge, little robustness to noise, and a lack of consistency with their answers. However, most prior work focus on one or two of those problems in isolation, which makes it difficult to see trends across them. We aim to close this gap, by first outlining a set of – previously discussed as well as novel – desiderata for QA models. We then survey relevant analysis and methods papers to provide an overview of the state of the field. The second part of our work presents experiments where we evaluate 15 QA systems on 5 datasets according to all desiderata at once. We find many novel trends, including (1) systems that are less susceptible to noise are not necessarily more consistent with their answers when given irrelevant context; (2) most systems that are more susceptible to noise are more likely to correctly answer according to a context that conflicts with their parametric knowledge; and (3) the combination of conflicting knowledge and noise can reduce system performance by up to 96%. As such, our desiderata help increase our understanding of how these models work and reveal potential avenues for improvements.

## introduction question answering (qa) systems which are based on large language models (llms) play a larger role than ever before in our society, due to their ability to offer quick access to information @xcite @xcite @xcite . many qa systems can make use of context information when available, which often contains relevant information to help systems answer questions, cf. figure 1 . we many aspects of such systems have been evaluated by previous work, such as the amount of their parametric knowledge @xcite and their robustness to noise @xcite , conflicting knowledge @xcite , or irrelevant contexts @xcite . however, looking at such aspects in isolation makes it difficult to see trends across problems, e.g., to explore whether there is a connection between a model's attention to context and its ability to handle noise. here, we 1) outline a set of -previously discussed as well as novel -desiderata for contextbased qa models and 2) provide an extensive survey of related works, which we group and discuss according to our desiderata. such desiderata unify some of the existing aspects from the literature, e.g., robustness to conflicting knowledge, and outline how a qa system should behave from the perspective of the context. we will publicly release a toolkit to prepare datasets -both free-form and multiple choice (mc) type -to evaluate models context distractor known knowledge unknown knowledge 1 original t t 2 original t t 3 alternative a a 4 alternative a a 5 none t b 6 irrelevant t b using our toolkit, we 3) evaluate 15 llm-based qa systems and first confirm prior works' results: while some systems appear nearly perfect, scoring 99% accuracy on standard datasets, their performance is significantly worse according to many of our desiderata. for instance, their accuracy drops by up to 93% with noise, such as random strings as distractors. second, considering all desiderata at once, we find that (1) systems that are less susceptible to noise are more consistent with their answers when provided with irrelevant context; (2) most systems that are more susceptible to noise are more likely to correctly answer according to a context that conflicts with their parametric knowledge; and (3) the combination of conflicting knowledge and noise can reduce system performance by up to 96%. finding these novel trends using our desiderata opens new avenues to improve qa models. ## desiderata we now develop a set of desiderata regarding the context use of a model, before presenting our survey on what prior work has found with regards to our desiderata and performing our own experiments in the next two sections. to come up with our desiderata, which are presented in the ideal behavior depends on whether the knowledge in the context is known or unknown to the model. for example, looking at we follow the work by @xcite ; @xcite ; @xcite and define known knowledge as questions that a model can answer correctly without context, and unknown knowledge as those it cannot. proposed desiderata an ideal model should: ∎ for both known and unknown knowledge: a. answer correctly with the original context: this is the standard qa systems evaluation approach. b. answer correctly with a noisy irrelevant variation of the original context: qa systems should be robust to distractors, as different users and information retrieval (ir) systems introduce varying amounts of irrelevant information. c. change its answer with conflicting context to the conflicting knowledge: as our world is constantly changing, qa systems should be dynamic in their knowledge. that is, similarly to @xcite ; @xcite , we believe that the context should always take priority over a model's parametric knowledge, when relevant. ∎ for known knowledge: d. answer correctly with no context: in our setting this happens by default for known knowledge, as by definition known knowledge is questions that can be answered without context. however, we expect the ideal system to have the largest possible amount of knowledge, i.e., to be able to answer most questions without context. e. answer correctly with an irrelevant context: since the model answers questions correctly without context for known knowledge, it should also answer correctly with irrelevant context. ∎ for unknown knowledge: f. answer incorrectly/"unanswerable" with no context: in our setting this happens by default for unknown knowledge. while the ideal model should predict "unanswerable" for questions it cannot answer, most existing datasets do not include questions that, according to our definition, are truly "unanswerable," as they can be answered with parametric knowledge (cf. sec. 4.1.2). hence, we add here that models may also predict an incorrect answer, as expected from models that are forced to predict any answer other than "unanswerable" for unknown knowledge. g. answer the same with irrelevant context as with no context: the ideal model should be consistent in its answer, even when wrong. hence, the model's answer with irrelevant context and no context -(f) above -should be the same. ## experiments in our experiments, each context c i and question q i are input into the model within the following string: "question: q i . context: c i ." in the free-form setting and for mcqa, we use exact match (em) and, respectively, accuracy to measure model performance. that being said, our approach is by design extremely easily adaptable to different choices of metrics, such as llm-based ones @xcite , which could have a higher correlation with humans for qa tasks than em. ## conclusion we outline a set of -previously discussed as well as novel -desiderata for context-based qa systems. we survey relevant papers to provide an overview of the state of the field, and evaluate 15 qa systems according to all desiderata at once. while previous work examine desiderata aspects in isolation, by looking at all aspects together, we are able to find novel trends which increase our understanding of how these models work and reveal potential avenues to improve such models.