Large Language Models Fall Short: Understanding Complex Relationships in Detective Narratives (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing datasets for narrative understanding fail to represent complexity and uncertainty of relationships in real-life social scenarios. |
| Approach: | They propose a benchmark for extracting and analysing intricate character relation graphs from detective narratives using large-scale large-language models. |
| Outcome: | The proposed dataset extracts and analyses character relation graphs from detective narratives using advanced Large Language Models like GPT-3.5, GPT-4, and Llama2 . |
Similar Papers
Large Language Models Meet Harry Potter: A Dataset for Aligning Dialogue Agents with Characters (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing models that can create open-domain dialogue agents lack character representation and annotations. |
| Approach: | They propose a dataset to study character alignment and character representation . it includes all dialogue sessions from the Harry Potter series and includes annotations . |
| Outcome: | The proposed dataset can be used as a universal benchmark for character-driven LLMs. |
Are LLMs Good Annotators for Discourse-level Event Relation Extraction? (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have demonstrated proficiency in a wide array of natural language processing tasks, but their effectiveness over discourse-level event relation extraction tasks remains unexplored. |
| Approach: | They evaluate LLMs' ability to address discourse-level event relation extraction tasks using an open-source model and a commercial model. |
| Outcome: | The proposed model performs poorly on discourse-level event relation extraction tasks. |
Can Large Language Models Infer Causal Relationships from Real-World Text? (2026.acl-long)
Copied to clipboard
| Challenge: | Existing work evaluating large language models relies on synthetic or simplified texts with explicit causal relationships. |
| Approach: | They develop a benchmark to evaluate LLMs' ability to infer causal relationships from texts . they use a dataset of texts with different levels of explicitness and complexity . |
| Outcome: | The proposed benchmark is the first-ever real-world dataset for this task. |
My side, your side and the evidence: Discovering aligned actor groups and the narratives they weave (2023.acl-long)
Copied to clipboard
| Challenge: | Identify distinct sets of aligned story actors responsible for sustaining issue-specific narratives . authors propose a novel two-step graph-based framework that identifies alignments between actors . |
| Approach: | They propose a proxy task to identify the distinct sets of aligned story actors . they propose identifying alignments between actors and extracting alignes using TAMPA . |
| Outcome: | The proposed framework is based on a corpus of text segments associated with six issues . it identifies aligned actors and extracts alignable actor groups from the network structure . |
How do Language Models Reshape Entity Alignment? A Survey of LM-Driven EA Methods: Advances, Benchmarks, and Future (2025.emnlp-main)
Copied to clipboard
| Challenge: | Entity alignment (EA) is critical for knowledge graph (KG) integration. |
| Approach: | They propose a taxonomy that categorizes methods in three stages: data preparation, feature embedding, and alignment. |
| Outcome: | The proposed taxonomy categorizes methods in three key stages: data preparation, feature embedding, and alignment. |
CLAUSE-ATLAS: A Corpus of Narrative Information to Scale up Computational Literary Analysis (2024.lrec-main)
Copied to clipboard
| Challenge: | XIX and XX century English novels annotated automatically contain 41,715 labeled clauses . a new approach to analyze novels based on clauses captures structural patterns within books, as well as qualitative differences between them. |
| Approach: | They propose to use a corpus of XIX and XX century English novels annotated automatically to study stories as sequences of eventive, subjective and contextual information. |
| Outcome: | The proposed method captures structural patterns within books, as well as qualitative differences between them. |
Deciphering Digital Detectives: Understanding LLM Behaviors and Capabilities in Multi-Agent Mystery Games (2024.findings-acl)
Copied to clipboard
| Challenge: | In this study, we explore the application of Large Language Models (LLMs) in Jubensha, a Chinese detective role-playing game and a novel area in Artificial Intelligence (AI) driven gaming. |
| Approach: | They propose to use large language models to foster AI agent development in Jubensha, a Chinese detective role-playing game. |
| Outcome: | The proposed framework enables AI agents to engage in Jubensha games autonomously. |
How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances (2023.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) are impressive in solving tasks, but they can quickly be outdated after deployment. |
| Approach: | They provide a review of recent advances in aligning deployed large language models with the ever-changing world knowledge. |
| Outcome: | The proposed models can be used to perform various tasks directly through in-context learning or for further fine-tuning for domain-specific uses. |
LLMs Underperform Graph-Based Parsers on Supervised Relation Extraction for Complex Graphs (2026.acl-short)
Copied to clipboard
| Challenge: | Relation extraction is a core NLP task which involves extracting [head, relation, dependent] RDF triples from text. |
| Approach: | They evaluate four large language models against a graph-based parser on six relation extraction datasets with sentence graphs of varying sizes and complexities. |
| Outcome: | The graph-based parser outperforms the LLMs on six relation extraction datasets with sentence graphs of varying sizes and complexities. |
Are Large Language Models Capable of Generating Human-Level Narratives? (2024.emnlp-main)
Copied to clipboard
Yufei Tian, Tenghao Huang, Miri Liu, Derek Jiang, Alexander Spangher, Muhao Chen, Jonathan May, Nanyun Peng
| Challenge: | a recent HCI study has pointed to gaps in machine storytelling ability at the global level . authors show that LLMs have less suspense and less tension than human stories . |
| Approach: | They propose a computational framework to analyze narratives through three discourse-level aspects. |
| Outcome: | The proposed framework analyzes narratives through three discourse-level aspects . it shows that LLMs fall short of human abilities in discourse understanding . |