Summary of Enhancing Annotated Bibliography Generation with Llm Ensembles, by Sergio Bermejo
Enhancing Annotated Bibliography Generation with LLM Ensembles
by Sergio Bermejo
First submitted to arxiv on: 30 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This work proposes an innovative approach to improving annotated bibliography generation using Large Language Model (LLM) ensembles. Multiple LLMs play different roles – controllable text generation, evaluation, and summarization – to enhance model performance in scholarly tasks. The ensemble is validated through a systematic methodology, producing output diversity by varying LLM parameters. An LLM serves as a judge to assess relevance, accuracy, and coherence, selecting responses that are then merged and refined using summarization and redundancy removal techniques. Experimental results show that the combined outputs from the LLM ensemble improve coherence and relevance compared to individual responses, leading to a 38% improvement in annotation quality and a 51% reduction in content redundancy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how to make computers better at writing summaries of academic papers. They use special computer models called Large Language Models (LLMs) that can generate text. The researchers combine multiple LLMs to create an “ensemble” that works together to write a summary. They test this approach and find that it produces better summaries than using just one model. This could be useful for people who need help summarizing long papers or articles. |
Keywords
» Artificial intelligence » Large language model » Summarization » Text generation
