Are NLP Models Good at Tracing Thoughts: An Overview of Narrative Understanding

• URL: http://arxiv.org/abs/2310.18783v1
• Date: Sat, 28 Oct 2023 18:47:57 GMT
• Title: Are NLP Models Good at Tracing Thoughts: An Overview of Narrative Understanding
• Authors: Lixing Zhu, Runcong Zhao, Lin Gui, Yulan He
• Abstract summary: Narrative understanding involves capturing the author's cognitive processes, providing insights into their knowledge, intentions, beliefs, and desires. Although large language models (LLMs) excel in generating grammatically coherent text, their ability to comprehend the author's thoughts remains uncertain. This hinders the practical applications of narrative understanding.
• Score: 21.900015612952146
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Narrative understanding involves capturing the author's cognitive processes, providing insights into their knowledge, intentions, beliefs, and desires. Although large language models (LLMs) excel in generating grammatically coherent text, their ability to comprehend the author's thoughts remains uncertain. This limitation hinders the practical applications of narrative understanding. In this paper, we conduct a comprehensive survey of narrative understanding tasks, thoroughly examining their key features, definitions, taxonomy, associated datasets, training objectives, evaluation metrics, and limitations. Furthermore, we explore the potential of expanding the capabilities of modularized LLMs to address novel narrative understanding tasks. By framing narrative understanding as the retrieval of the author's imaginative cues that outline the narrative structure, our study introduces a fresh perspective on enhancing narrative comprehension.

Related papers

• Are Large Language Models Capable of Generating Human-Level Narratives? [114.34140090869175]
  This paper investigates the capability of LLMs in storytelling, focusing on narrative development and plot progression. We introduce a novel computational framework to analyze narratives through three discourse-level aspects. We show that explicit integration of discourse features can enhance storytelling, as is demonstrated by over 40% improvement in neural storytelling.
  arXiv  Detail & Related papers  (2024-07-18T08:02:49Z)
• Reasoning in Conversation: Solving Subjective Tasks through Dialogue Simulation for Large Language Models [56.93074140619464]
  We propose RiC (Reasoning in Conversation), a method that focuses on solving subjective tasks through dialogue simulation. The motivation of RiC is to mine useful contextual information by simulating dialogues instead of supplying chain-of-thought style rationales. We evaluate both API-based and open-source LLMs including GPT-4, ChatGPT, and OpenChat across twelve tasks.
  arXiv  Detail & Related papers  (2024-02-27T05:37:10Z)
• Identifying Semantic Induction Heads to Understand In-Context Learning [103.00463655766066]
  We investigate whether attention heads encode two types of relationships between tokens present in natural languages. We find that certain attention heads exhibit a pattern where, when attending to head tokens, they recall tail tokens and increase the output logits of those tail tokens.
  arXiv  Detail & Related papers  (2024-02-20T14:43:39Z)
• Think from Words(TFW): Initiating Human-Like Cognition in Large Language Models Through Think from Words for Japanese Text-level Classification [0.0]
  "Think from Words" (TFW) initiates the comprehension process at the word level and then extends it to encompass the entire text. "TFW with Extra word-level information" (TFW Extra) augmenting comprehension with additional word-level data. Our findings shed light on the impact of various word-level information types on LLMs' text comprehension.
  arXiv  Detail & Related papers  (2023-12-06T12:34:46Z)
• Interpreting Pretrained Language Models via Concept Bottlenecks [55.47515772358389]
  Pretrained language models (PLMs) have made significant strides in various natural language processing tasks. The lack of interpretability due to their black-box'' nature poses challenges for responsible implementation. We propose a novel approach to interpreting PLMs by employing high-level, meaningful concepts that are easily understandable for humans.
  arXiv  Detail & Related papers  (2023-11-08T20:41:18Z)
• MAGNIFICo: Evaluating the In-Context Learning Ability of Large Language Models to Generalize to Novel Interpretations [37.13707912132472]
  Humans possess a remarkable ability to assign novel interpretations to linguistic expressions. Large Language Models (LLMs) have a knowledge cutoff and are costly to finetune repeatedly. We systematically analyse the ability of LLMs to acquire novel interpretations using in-context learning.
  arXiv  Detail & Related papers  (2023-10-18T00:02:38Z)
• Context-faithful Prompting for Large Language Models [51.194410884263135]
  Large language models (LLMs) encode parametric knowledge about world facts. Their reliance on parametric knowledge may cause them to overlook contextual cues, leading to incorrect predictions in context-sensitive NLP tasks. We assess and enhance LLMs' contextual faithfulness in two aspects: knowledge conflict and prediction with abstention.
  arXiv  Detail & Related papers  (2023-03-20T17:54:58Z)
• M-SENSE: Modeling Narrative Structure in Short Personal Narratives Using Protagonist's Mental Representations [14.64546899992196]
  We propose the task of automatically detecting prominent elements of the narrative structure by analyzing the role of characters' inferred mental state. We introduce a STORIES dataset of short personal narratives containing manual annotations of key elements of narrative structure, specifically climax and resolution. Our model is able to achieve significant improvements in the task of identifying climax and resolution.
  arXiv  Detail & Related papers  (2023-02-18T20:48:02Z)
• Imagination-Augmented Natural Language Understanding [71.51687221130925]
  We introduce an Imagination-Augmented Cross-modal (iACE) to solve natural language understanding tasks. iACE enables visual imagination with external knowledge transferred from the powerful generative and pre-trained vision-and-language models. Experiments on GLUE and SWAG show that iACE achieves consistent improvement over visually-supervised pre-trained models.
  arXiv  Detail & Related papers  (2022-04-18T19:39:36Z)
• Accessible Visualization via Natural Language Descriptions: A Four-Level Model of Semantic Content [6.434361163743876]
  We introduce a conceptual model for the semantic content conveyed by natural language descriptions of visualizations. We conduct a mixed-methods evaluation with 30 blind and 90 sighted readers, and find that these reader groups differ significantly on which semantic content they rank as most useful.
  arXiv  Detail & Related papers  (2021-10-08T23:37:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.