TTQA-RS- A break-down prompting approach for Multi-hop Table-Text Question Answering with Reasoning and Summarization

Question answering (QA) over tables and text has gained much popularity over the years. Multi-hop table-text QA requires multiple hops between the table and text, making it a challenging QA task. Although several works have attempted to solve the table-text QA task, most involve training the models and requiring labeled data. In this paper, we have proposed a model - TTQA-RS: A break-down prompting approach for Multi-hop Table-Text Question Answering with Reasoning and Summarization. Our model uses augmented knowledge including table-text summary with decomposed sub-question with answer for a reasoning-based table-text QA. Using open-source language models our model outperformed all existing prompting methods for table-text QA tasks on existing table-text QA datasets like HybridQA and OTT-QA's development set. Our results are comparable with the training-based state-of-the-art models, demonstrating the potential of prompt-based approaches using open-source LLMs. Additionally, by using GPT-4 with LLaMA3-70B, our model achieved state-of-the-art performance for prompting-based methods on multi-hop table-text QA.

Modeling Bilingual Sentence Processing: Evaluating RNN and Transformer Architectures for Cross-Language Structural Priming

This study evaluates the performance of Recurrent Neural Network (RNN) and Transformer in replicating cross-language structural priming: a key indicator of abstract grammatical representations in human language processing. Focusing on Chinese-English priming, which involves two typologically distinct languages, we examine how these models handle the robust phenomenon of structural priming, where exposure to a particular sentence structure increases the likelihood of selecting a similar structure subsequently. Additionally, we utilize large language models (LLM) to measure the cross-lingual structural priming effect. Our findings indicate that Transformer outperform RNN in generating primed sentence structures, challenging the conventional belief that human sentence processing primarily involves recurrent and immediate processing and suggesting a role for cue-based retrieval mechanisms. Overall, this work contributes to our understanding of how computational models may reflect human cognitive processes in multilingual contexts.