Enhancing Temporal Sensitivity and Reasoning for Time-Sensitive Question Answering (2024)

Abstract: Time-Sensitive Question Answering (TSQA) demands the effective utilization of specific temporal contexts, encompassing multiple time-evolving facts, to address time-sensitive questions. This necessitates not only the parsing of temporal information within questions but also the identification and understanding of time-evolving facts to generate accurate answers. However, current large language models still have limited sensitivity to temporal information and their inadequate temporal reasoning capabilities. In this paper, we propose a novel framework that enhances temporal awareness and reasoning through Temporal Information-Aware Embedding and Granular Contrastive Reinforcement Learning. Experimental results on four TSQA datasets demonstrate that our framework significantly outperforms existing LLMs in TSQA tasks, marking a step forward in bridging the performance gap between machine and human temporal understanding and reasoning.

## introduction time-sensitive question answering (tsqa) involves parsing and responding to questions that depend on specific time points or periods. for instance, obama's roles in 2006 and 2016 were distinctly different. unlike conventional question answering (qa) tasks @xcite @xcite @xcite , tsqa requires language models to discern and understand temporal information within questions. moreover, it necessitates the capacity to locate relevant facts using temporal information, leveraging time-related knowledge to provide accurate and relevant answers. in the domain of tsqa, models are required to predict answers through generation or extraction methods, given a set of questions and contexts embedded * equal contributions with temporal information. these questions are characterized by explicit or implicit temporal expressions, while the context invariably contains multiple facts evolving over time. recently, several tsqa datasets have been introduced, including notable work @xcite ) that provides 40k time-sensitive questions with corresponding free-text contexts derived unprocessed from wiki-data @xcite . building upon this foundation, @xcite not only furnish free-text contexts from wikidata without any modification but also offer a version of the dataset where the context has been restructured. these contributions have facilitated evaluations of existing large language models (llms) such as fid @xcite , bigbird @xcite , and t5 @xcite on these datasets, underscoring the challenge tsqa poses, as the performance of llms remains significantly inferior to human levels. however, an effective methodology to significantly improve llms performance in tsqa is still lacking. it is challenging to improve model's sensitivity to temporal information and the capacity for temporal reasoning. firstly, llms significantly lack attention to and comprehension of temporal information in both questions and their contexts @xcite . it is essential for models to prioritize temporal cues in questions to accuately locate corresponding times and facts within contexts, especially for lengthy and complex free-text contexts, which encompass multiple irrelevant information and various chronological connections. focusing on temporal information allows a model to circumvent the distraction of irrelevant data and to swiftly locate relevant facts through the chronological connections among different paragraphs. secondly, llms @xcite exhibit notably weak temporal reasoning abilities. this specifically refers to the models' susceptibility to interference from answers that pertain to the same entity or relationship but span different time periods within the context, as well as to disturbances from other events occurring within the same time period. in this paper, we introduce a framework to address these challenges. initially, we implement temporal information-aware embedding to enhance the model's sensitivity towards temporal information by augmenting its focus on temporal data and adjacent specifics. in our time-aware module, we use spacy and identify temporal information within questions and contexts, establishing a question temporal matrix and a context temporal matrix. furthermore, we advance the model's temporal reasoning capabilities through the granular contrastive reinforcement learning. we introduce remote negative answers -answers within contexts corresponding to different time periods for the same entities and relationships, and proximal negative answers -answers related to other events within the same time period. additionally, we propose a more rational reward function to aid the model in reinforcement learning. conclusively, we present comparative results between our framework and existing llms, validating our framework's effectiveness through its application across four tsqa datasets with varying difficulty. the main contributions of this work are summarised as: ## problem definition time sensitive question answering aims to generate answers ã based on a given free-text question q and its corresponding free-text context c. the questions feature explicit or implicit temporal expressions, while the context invariably includes multiple time-evolving facts (s, r, o, [t s , t e ]) where r denotes the relationship that exists between the subject entity s and the object entity o throughout the specified time interval [t s , t e ]. the answer is either an entity or non-existent. the characteristics of tsqa include: 1) the structure (s, r, o i , [t s i , t e i ]) is invariably extractable from the question and the answer. 2) modifying the temporal information within the question will result in a change to the answer. the tsqa primarily evaluates the model's sensitivity to temporal information and its ability of temporal reasoning. temporal information sensitivity refers to the model's capability to pay closer attention to the temporal information presented in both the given question and the corresponding context, recognizing the temporal information as a crucial factor. temporal reasoning ability denotes the model's understanding of basic temporal relationships (for example, the year 2015 falls within the time interval from 2010 to 2020, the year 2015 is five years later than the year 2010, the period of world war ii is within the time interval from 1939 to 1945, etc.) and its capacity to deduce the correct answer based on these temporal relationships. ## method to enhance the sensitivity to temporal information and the capability for temporal reasoning within models, we propose a training framework. our framework addresses the tsqa task by leveraging a pre-trained language model to interpret free-text temporal questions and context. as illustrated in fig. 1 , the framework incorporates two primary methodologies: temporal information-aware embedding and granular contrastive reinforcement learning. temporal information-aware embedding is dedicated to enhancing the model's sensitivity to temporal information by increasing its attention to temporal data and adjacent temporal details. granular contrastive reinforcement learning offers remote and proximal negative answers based on varying temporal distances and employs a more rational reward function to assist the model in improving its temporal reasoning capabilities. ## conclusion in conclusion, tsqa presents unique challenges for large language models (llms), particularly in their attention to and comprehension of temporal information within questions and contexts, as well as in their capacity for temporal reasoning. to bridge these gaps, we have proposed a novel framework that introduces temporal information-aware embedding and enhances temporal reasoning through granular contrastive reinforcement learning. this framework notably improves the model's proficiency in discerning relevant temporal details and temporal reasoning. the comparative evaluation of our framework against existing llms across four diverse tsqa datasets has yielded promising results. it not only underscores the enhanced performance of our framework but also highlights the critical need for specialized strategies in addressing the unique demands of tsqa.