Papers by Phillip Schneider
CarMem: Enhancing Long-Term Memory in LLM Voice Assistants through Category-Bounding (2025.coling-industry)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are stateless and present all relevant memories during each interaction, resulting in repetitive user requests and disengagement. |
| Approach: | They propose a long-term memory system for voice assistants structured around predefined categories that leverages Large Language Models to extract, store, and retrieve preferences within these categories. |
| Outcome: | The proposed system achieves an F1-score of .78 to .95 in preference extraction, depending on category granularity, and is suitable for industrial applications. |
A Decade of Knowledge Graphs in Natural Language Processing: A Survey (2022.aacl-main)
Copied to clipboard
| Challenge: | Knowledge graphs (KGs) are a representation of semantic relations between entities . despite their popularity, there is still no general understanding of what exactly a KG is or for what tasks it is applicable. |
| Approach: | They analyze 507 papers on knowledge graphs in natural language processing (NLP) they provide a taxonomy of tasks and review the maturity of individual research streams . |
| Outcome: | The findings summarize the literature and highlight directions for future work. |
MedREQAL: Examining Medical Knowledge Recall of Large Language Models via Question Answering (2024.findings-acl)
Copied to clipboard
| Challenge: | Large language models can encode knowledge during pre-training on large text corpora, enabling downstream tasks like question answering (QA). |
| Approach: | They construct a dataset derived from systematic reviews to examine their ability to encode medical knowledge and their recall. |
| Outcome: | The proposed model performs well on the biomedical QA dataset. |
A Comparative Analysis of Conversational Large Language Models in Knowledge-Based Text Generation (2024.eacl-short)
Copied to clipboard
| Challenge: | Generating natural language text from graph-structured data is essential for conversational information seeking. |
| Approach: | They conduct an empirical analysis of conversational large language models in generating natural language text from semantic triples using a WebNLG dataset. |
| Outcome: | The proposed models improve their ability to generate natural language text from semantic triples using few-shot prompting, post-processing, and efficient fine-tuning techniques. |
HealthFC: Verifying Health Claims with Evidence-Based Medical Fact-Checking (2024.lrec-main)
Copied to clipboard
| Challenge: | determining the trustworthiness of online medical content is challenging in the digital age . fact-checking is an approach to assess the veracity of factual claims . a new dataset is presented to help advance automated fact- checking . |
| Approach: | They propose a dataset that assesses the veracity of factual claims using evidence from credible sources. |
| Outcome: | The proposed dataset can be used for automated fact-checking tasks. |