12 accepted papers
Large Language Models (LLMs) demonstrate strong capabilities in code generation but often lack adaptability in planning and refinement. We propose Self-PR, a framework that integrates adaptive plan selection and iterative repair to improve correctness and generalization. Self-PR constructs a reusabl
In the post-Turing era, evaluating large language models (LLMs) involves assessing generated text based on readers’ decisions rather than merely its indistinguishability from human-produced content. This paper explores how LLM-generated text impacts readers’ decisions, focusing on both amateur and e…
This paper explores the use of Large Language Models (LLMs) in the generation and evaluation of analytical reports derived from Earnings Calls (ECs). Addressing a current gap in research, we explore the generation of analytical reports with LLMs in a multi-agent framework, designing specialized agen…
The advancement of text generation models has granted us the capability to produce coherent and convincing text on demand. Yet, in real-life circumstances, individuals do not continuously generate text or voice their opinions. For instance, consumers pen product reviews after weighing the merits and…
This paper introduces a novel approach to analyzing the forward-looking statements in equity research reports by integrating argument mining with sentiment analysis. Recognizing the limitations of traditional models in capturing the nuances of future-oriented analysis, we propose a refined categoriz…
This paper introduces the Database Querying and Reasoning Dataset for Question Answering (DBQR-QA), aimed at addressing the gap in current question-answering (QA) research by emphasizing the essential processes of database querying and reasoning to answer questions. Specifically designed to accommod…
Argument mining has typically been researched for specific corpora belonging to concrete languages and domains independently in each research work. Human argumentation, however, has domain- and language-dependent linguistic features that determine the content and structure of arguments. Also, when d…
Headline generation, a key task in abstractive summarization, strives to condense a full-length article into a succinct, single line of text. Notably, while contemporary encoder-decoder models excel based on the ROUGE metric, they often falter when it comes to the precise generation of numerals in h…
Argument synthesis aims to generate rational claims, representing a fundamental objective in this field. While existing models excel in summarizing arguments and engaging in debates, we observe a critical gap in their ability to generate accurate arguments that incorporate forward-looking perspectiv…
This paper critically examines the arithmetic capabilities of Large Language Models (LLMs), uncovering significant limitations in their performance. Our research reveals a notable decline in accuracy for complex calculations involving large numbers, with addition and subtraction tasks showing varyin…
In this paper, we address the hallucination problem commonly found in natural language generation tasks. Language models often generate fluent and convincing content but can lack consistency with the provided source, resulting in potential inaccuracies. We propose a new decoding method called Fideli…
Both the issues of data deficiencies and semantic consistency are important for data augmentation. Most of previous methods address the first issue, but ignore the second one. In the cases of aspect-based sentiment analysis, violation of the above issues may change the aspect and sentiment polarity.…