Marked Temporal Point Process (MTPP) -- the de-facto sequence model for continuous-time event sequences -- historically employed for modeling human-generated action sequences, lack awareness of external stimuli. In this study, we propose a novel framework developed over Transformer Hawkes Process (THP) to incorporate external stimuli in a domain-agnostic manner. Furthermore, we integrate personalization into our framework by employing language model-based representations of user and event descriptions, which is essential for modeling human-generated action sequences. Towards evaluating the efficacy, we put together a comprehensive benchmark comprising 5 datasets (2 novel additions, and 3 repurposed from existing open datasets) harvested from several domains, spanning education, e-commerce, online payment, and discussion forum. On average, we achieve 9.35% gain in type-prediction accuracy and 7.38% reduction in time-prediction RMSE across all datasets over SOTA MTPP baselines. We demonstrate the superior performance of our proposed model through extensive ablations and showcasing its ability to capture complex combinations of external stimuli in a synthetic set up.

Despite continuous advancements in the capabilities of large language models (LLMs), numerical reasoning remains a challenging area.Techniques like chain-of-thought prompting, tree-of-thought prompting, and program-ofthought prompting guide LLMs through intermediate reasoning steps.Although in-context learning with few-shot prompting has improved performance, LLMs still lag behind state-ofthe-art models on financial numerical reasoning datasets such as FinQA and ConvFinQA.In this work, we introduce FINDER, a novel twostep framework, to enhance LLM's capabilities in financial numerical reasoning.The first step utilizes a generative retriever to extract relevant facts from unstructured data, including both text and tables.This is followed by contextaware Program of Thought prompting with dynamic selection of in-context examples.Our model FINDER achieves a new state-of-theart performance on both the FinQA and Con-vFinQA datasets, surpassing previous benchmarks with execution accuracy improvements of 5.98% and 4.05%, respectively.

The debate around vaccines has been going on for decades, but the COVID-19 pandemic showed how crucial it is to understand and mitigate anti-vaccine sentiments. While the pandemic may be over, it is still important to understand how the pandemic affected the anti-vaccine discourse, and whether the arguments against non-COVID vaccines have also changed due to the pandemic. This study attempts to answer these questions through a large-scale study of anti-vaccine posts on Twitter. Almost all prior works that utilized social media to understand anti-vaccine opinions considered only the three broad stances of Anti-Vax, Pro-Vax, and Neutral. There has not been any effort to identify the specific reasons/concerns behind the anti-vax sentiments (e.g., side-effects, conspiracy theories, political reasons) on social media at scale. In this work, we propose two novel methods for classifing tweets into 11 different anti-vax concerns -- a discriminative approach (entailment-based) and a generative approach (based on instruction tuning of LLMs) -- which outperform several strong baselines. We then apply this classifier on anti-vaccine tweets posted over a 5-year period (Jan 2018 - Jan 2023) to understand how the COVID-19 pandemic has impacted the anti-vaccine concerns among the masses. We find that the pandemic has made the anti-vaccine discourse far more complex than in the pre-COVID times, and increased the variety of concerns being voiced. Alarmingly, we find that concerns about COVID vaccines are now being projected onto the non-COVID vaccines, thus making more people hesitant in taking them in post-COVID times.

Subhendu Khatuya, Rajdeep Mukherjee, Akash Ghosh, Manjunath Hegde, Koustuv Dasgupta, Niloy Ganguly, Saptarshi Ghosh, Pawan Goyal. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

While automatic summarization techniques have made significant advancements, their primary focus has been on summarizing short news articles or documents that have clear structural patterns like scientific articles or government reports. There has not been much exploration into developing efficient methods for summarizing financial documents, which often contain complex facts and figures. Here, we study the problem of bullet point summarization of long Earning Call Transcripts (ECTs) using the recently released ECTSum dataset. We leverage an unsupervised question-based extractive module followed by a parameter efficient instruction-tuned abstractive module to solve this task. Our proposed model FLANFinBPS achieves new state-of-the-art performances outperforming the strongest baseline with 14.88% average ROUGE score gain, and is capable of generating factually consistent bullet point summaries that capture the important facts discussed in the ECTs. We make the codebase publicly available at https://github.com/subhendukhatuya/FLAN-FinBPS.

A large population of users gets affected by sudden slowdown or shutdown of an enterprise application. System administrators and analysts spend considerable amount of time dealing with functional and performance bugs. These problems are particularly hard to detect and diagnose in most computer systems, since there is a huge amount of system generated supportability data (counters, logs etc.) that need to be analyzed. Most often, there isn't a very clear or obvious root cause. Timely identification of significant change in application behavior is very important to prevent negative impact on the service. In this paper, we present ADELE, an empirical, data-driven methodology for early detection of anomalies in data storage systems. The key feature of our solution is diligent selection of features from system logs and development of effective machine learning techniques for anomaly prediction. ADELE learns from system's own history to establish the baseline of normal behavior and gives accurate indications of the time period when something is amiss for a system. Validation on more than 4800 actual support cases shows~83% true positive rate and~12% false positive rate in identifying periods when the machine is not performing normally. We also establish the existence of problem “signatures” which help map customer problems to already seen issues in the field. ADELE's capability to predict early paves way for online failure prediction for customer systems.